Welcome to EMOD malaria modeling

The Institute for Disease Modeling (IDM) develops disease modeling software that is thoroughly tested and shared with the research community to advance the understanding of disease dynamics. This software helps determine the combination of health policies and intervention strategies that can lead to disease eradication. If you encounter any issues while using the software, please contact support@idmod.org.

Epidemiological MODeling software (EMOD), is an agent-based model (ABM) that simulates the simultaneous interactions of agents in an effort to recreate complex phenomena. Each agent (such as a human or vector) can be assigned a variety of “properties” (for example, age, gender, etc.), and their behavior and interactions with one another are determined by using decision rules. These models have strong predictive power and are able to leverage spatial and temporal dynamics.

EMOD is also stochastic, meaning that there is randomness built into the model. Infection and recovery processes are represented as probabilistic Bernoulli random draws. In other words, when a susceptible person comes into contact with a pathogen, they are not guaranteed to become infected. Instead, you can imagine flipping a coin that has a λ chance of coming up tails S(t) times, and for every person who gets a “head” you say they are infected. This randomness better approximates what happens in reality. It also means that you must run many simulations to determine the probability of particular outcomes.

Documentation structure

Using the model contains information that will help you get started with EMOD, including installation instructions, a basic overview of the software, and some example simulations you can run on your computer.

Understanding the model contains more detailed information about the disease biology, workings of the EMOD model, complete parameter reference, and a glossary.

Advancing the model contains information for researchers and developers who want to modify the EMOD source code to add more functionality to the model.

The EMOD documentation is broken up into disease-specific sets that provide guidance for researchers modeling particular diseases. The documentation contains only the parameters, output reports, and other components of the model that are available to use with this disease model.

For example, this documentation set includes general installation and usage instructions that are common in all simulation types in addition to content specific to modeling malaria and other vector-borne diseases.

What’s new

This topic describes new functionality and breaking changes for recently released versions of Epidemiological MODeling software (EMOD).

EMOD v2.20

The EMOD v2.20 release includes support for typhoid disease modeling, including new campaign classes: EnvironmentalDiagnostic, TyphoidCarrierDiagnostic, TyphoidVaccine, and TyphoidWASH.

ImmunityBloodTest was added for identifying whether an individual’s immunity meets a specified threshold and then broadcasts an event based on the results. This new campaign class can be used with all supported disease modeling sim types.

InterventionForCurrentPartners can be used with STI and HIV sim types and provides a mechanism for the partners of individuals in the care system to also seek care.

OutbreakIndividualTBorHIV extends OutbreakIndividual and allows for specifying HIV or a specific strain of infection for TB.

In addition, configuration and campaign parameters that set the type of distribution (uniform, Gaussian, etc.) of infectiousness, incubation period, and delivery of interventions have been refactored. The number of distributions available and naming conventions used are now consistent across the configuration and campaign files. This change does not affect the distributions used in the demographics files.

A beta release of new campaign classes (not yet fully tested) are included to support surveillance of events, where events are listened to, detected, and broadcast when a threshold has been met. These classes include: BroadcastCoordinatorEvent, BroadcastNodeEvent, DelayEventCoordinator, SurveillanceEventCoordinator, and TriggeredEventCoordinator.

New configuration parameters

For the generic simulation type, the following new configuration parameters are available:

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Enable_Infectivity_Reservoir

boolean

0

1

0

Controls whether or not an exogeneous reservoir of infectivity will be included in the simulation and allows for the infectivity in a node to be increased additively. When set to 1 (true), the demographics parameter InfectivityReservoirSize is expected in NodeAtttributes for each node.

 
{
    "Enable_Infectivity_Reservoir": 1
}

Enable_Abort_Zero_Infectivity

boolean

0

Controls whether or not the simulation should be ended when total infectivity falls to zero. Supported only in single-node simulations.

 
{
    "Enable_Abort_Zero_Infectivity": 1,
    "Minimum_End_Time": 3650
}

Enable_Random_Generator_From_Serialized_Population

boolean

0

Determines if the random number generator(s) should be extracted from a serialized population file. Enabling this will start a simulation from this file with the exact same random number stream and location in that stream as when the file was serialized.

 
{
    "Run_Number": 12,
    "Random_Number_Generator_Type": "USE_AES_COUNTER",
    "Random_Number_Generator_Policy": "ONE_PER_NODE",
    "Enable_Random_Generator_From_Serialized_Population": 1
}

Minimum_End_Time

float

0

1000000

0

The minimum time step the simulation must reach before checking for early termination conditions. Enable_Abort_Zero_Infectivity must be set to 1 (true).

 
{
    "Enable_Abort_Zero_Infectivity": 1,
    "Minimum_End_Time": 3650
}

Random_Number_Generator_Policy

enum

ONE_PER_CORE

The policy that determines if random numbers are generated for objects in a simulation on a per-core or per-node basis. The following values are available:

ONE_PER_CORE

A random number generator (RNG) is created for each computing core running a simulation. When running a simulation on a single core, there will be only one RNG. If running the single simulation on multiple cores, each core will have its own RNG. The RNGs on the different cores start out such that they will not generate the same stream of random numbers. Prior to EMOD 2.19, all simulations used this policy.

ONE_PER_NODE

An RNG is created for each geographic node in the simulation. The advantages of this policy are that 1) an event that causes a random number to be drawn in one node does not cause things to change in another node and 2) changing a simulation from single core to multi-core will not change the results. The RNGs on the different nodesstart out such that they will not generate the same stream of random numbers.

 
{
    "Run_Number": 15,
    "Random_Number_Generator_Type": "USE_AES_COUNTER",
    "Random_Number_Generator_Policy": "ONE_PER_NODE"
}

Random_Number_Generator_Type

enum

USE_PSEUDO_DES

The type of random number generator to use for objects in a simulation. Must set the RNG seed in Run_Number. The following values are available:

USE_PSEUDO_DES

Based on Numerical Recipes in C. The Art of Scientific Computing, 2nd ed. Press, William H. et. al, 1992. Prior to EMOD 2.19, this was the only generator available.

USE_LINEAR_CONGRUENTIAL

Based on The Structure of Linear Congruential Sequences, Marsaglia, George, 1972.

USE_AES_COUNTER

Based on AES in CTR Mode encryption as implemented in Intel (R) Advanced Encryption Standard (AES) New Instruction Set.

 
{
    "Run_Number": 15,
    "Random_Number_Generator_Type": "USE_LINEAR_CONGRUENTIAL",
    "Random_Number_Generator_Policy": "ONE_PER_NODE",
    "Enable_Random_Generator_From_Serialized_Population": 1
}
New configuration parameters (Distribution)

Note: These configuration parameters are part of the refactoring of distribution parameters.

For the generic simulation type, the following new configuration parameters are available:

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Incubation_Period_Constant

float

0

3.40E+38

6

The incubation period to use for all individuals.

 
{
    "Incubation_Period_Distribution": "CONSTANT_DISTRIBUTION",
    "Incubation_Period_Constant": 8
}

Incubation_Period_Distribution

enum

NA

NA

NOT_INITIALIZED

The distribution type to use for assigning the incubation period to each individual in the population. Each individual’s value is a random draw from the distribution.

Possible values are:

NOT_INITIALIZED

No distribution set.

CONSTANT_DISTRIBUTION

Use the same value for each individual. Set Incubation_Period_Constant.

UNIFORM_DISTRIBUTION

Use a uniform distribution with a given minimum and maximum. Set Incubation_Period_Max and Incubation_Period_Min.

GAUSSIAN_DISTRIBUTION

The distribution is Gaussian (or normal). Set Incubation_Period_Gaussian_Mean and Incubation_Period_Gaussian_Std_Dev.

EXPONENTIAL_DISTRIBUTION

The distribution is exponential with a given mean. Set Incubation_Period_Exponential.

WEIBULL_DISTRIBUTION

Use a Weibull distribution with a given shape and scale. Set Incubation_Period_Kappa and Incubation_Period_Lambda.

LOG_NORMAL_DISTRIBUTION

Use a log-normal distribution with a given mean and standard deviation of the natural log. Set Incubation_Period_Log_Normal_Mu and Incubation_Period_Log_Normal_Sigma.

POISSON_DISTRIBUTION

Use a Poisson distribution with a given mean. Set Incubation_Period_Poisson_Mean.

DUAL_CONSTANT_DISTRIBUTION

Use a distribution where some individuals are set to a value of zero and the rest to a given value. Set Incubation_Period_Proportion_0 and Peak_2_Value. This distribution does not use the parameters set for CONSTANT_DISTRIBUTION.

DUAL_EXPONENTIAL_DISTRIBUTION

Use two exponential distributions with given means. Set Incubation_Period_Mean_1, Incubation_Period_Mean_2, and Incubation_Period_Proportion_1. This distribution does not use the parameters set for DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Incubation_Period_Gaussian_Mean": 8,
    "Incubation_Period_Gaussian_Std_Dev": 1.5
}

Incubation_Period_Exponential

float

0

3.40E+38

6

The mean incubation period when Incubation_Period_Distribution is set to EXPONENTIAL_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "EXPONENTIAL_DISTRIBUTION",
    "Incubation_Period_Exponential": 4.25
}

Incubation_Period_Gaussian_Mean

float

0

3.40E+38

6

The mean of the incubation period when Incubation_Period_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Incubation_Period_Gaussian_Mean": 8,
    "Incubation_Period_Gaussian_Std_Dev": 1.5
}

Incubation_Period_Gaussian_Std_Dev

float

1.18E-38

3.40E+38

1

The standard deviation of the incubation period when Incubation_Period_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Incubation_Period_Gaussian_Mean": 8,
    "Incubation_Period_Gaussian_Std_Dev": 1.5
}

Incubation_Period_Kappa

float

1.18E-38

3.40E+38

1

The shape value for the incubation period when Incubation_Period_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "WEIBULL_DISTRIBUTION",
    "Incubation_Period_Kappa": 0.9,
    "Incubation_Period_Lambda": 1.5
}

Incubation_Period_Lambda

float

1.18E-38

3.40E+38

1

The scale value for the incubation period when Incubation_Period_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "WEIBULL_DISTRIBUTION",
    "Incubation_Period_Kappa": 0.9,
    "Incubation_Period_Lambda": 1.5
}

Incubation_Period_Log_Normal_Mu

float

1.18E-38

3.40E+38

6

The mean of the natural log of the incubation period when Incubation_Period_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Incubation_Period_Log_Normal_Mu": 4,
    "Incubation_Period_Log_Normal_Sigma": 1
}

Incubation_Period_Log_Normal_Sigma

float

1.18E-38

3.40E+38

1

The standard deviation of the natural log of the incubation period when Incubation_Period_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Incubation_Period_Log_Normal_Mu": 4,
    "Incubation_Period_Log_Normal_Sigma": 1
}

Incubation_Period_Max

float

0

3.40E+38

1

The maximum incubation period when Incubation_Period_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "UNIFORM_DISTRIBUTION",
    "Incubation_Period_Min": 2,
    "Incubation_Period_Max": 7
}

Incubation_Period_Mean_1

float

1.18E-38

3.40E+38

1

The mean of the first exponential distribution when Incubation_Period_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Incubation_Period_Mean_1": 4,
    "Incubation_Period_Mean_2": 12,
    "Incubation_Period_Proportion_1": 0.2
}

Incubation_Period_Mean_2

float

1.18E-38

3.40E+38

1

The mean of the second exponential distribution when Incubation_Period_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Incubation_Period_Mean_1": 4,
    "Incubation_Period_Mean_2": 12,
    "Incubation_Period_Proportion_1": 0.2
}

Incubation_Period_Min

float

0

3.40E+38

0

The minimum incubation period when Incubation_Period_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "UNIFORM_DISTRIBUTION",
    "Incubation_Period_Min": 2,
    "Incubation_Period_Max": 7
}

Incubation_Period_Peak_2_Value

float

0

3.40E+38

1

The incubation period value to assign to the remaining individuals when Incubation_Period_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Incubation_Period_Proportion_0": 0.25,
    "Incubation_Period_Peak_2_Value": 5
}

Incubation_Period_Poisson_Mean

float

0

3.40E+38

6

The mean of the incubation period when Incubation_Period_Distribution is set to POISSON_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "POISSON_DISTRIBUTION",
    "Incubation_Period_Poisson_Mean": 5
}

Incubation_Period_Proportion_0

float

0

1

1

The proportion of individuals to assign a value of zero days incubation when Incubation_Period_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Incubation_Period_Proportion_0": 0.25,
    "Incubation_Period_Peak_2_Value": 5
}

Incubation_Period_Proportion_1

float

0

1

1

The proportion of individuals in the first exponential distribution when Incubation_Period_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Incubation_Period_Mean_1": 4,
    "Incubation_Period_Mean_2": 12,
    "Incubation_Period_Proportion_1": 0.2
}

Infectious_Period_Constant

float

0

3.40E+38

6

The infectious period to use for all individuals.

 
{
    "Infectious_Period_Distribution": "CONSTANT_DISTRIBUTION",
    "Infectious_Period_Constant": 8
}

Infectious_Period_Distribution

enum

NA

NA

NOT_INITIALIZED

The distribution type to use for assigning the infectious period to each individual in the population. Each individual’s value is a random draw from the distribution.

Possible values are:

NOT_INITIALIZED

No distribution set.

CONSTANT_DISTRIBUTION

Use the same value for each individual. Set Infectious_Period_Constant.

UNIFORM_DISTRIBUTION

Use a uniform distribution with a given minimum and maximum. Set Infectious_Period_Max and Infectious_Period_Min.

GAUSSIAN_DISTRIBUTION

The distribution is Gaussian (or normal). Set Infectious_Period_Gaussian_Mean and Infectious_Period_Gaussian_Std_Dev.

EXPONENTIAL_DISTRIBUTION

The distribution is exponential with a given mean. Set Infectious_Period_Exponential.

WEIBULL_DISTRIBUTION

Use a Weibull distribution with a given shape and scale. Set Infectious_Period_Kappa and Infectious_Period_Lambda.

LOG_NORMAL_DISTRIBUTION

Use a log-normal distribution with a given mean and standard deviation of the natural log. Set Infectious_Period_Log_Normal_Mu and Infectious_Period_Log_Normal_Sigma.

POISSON_DISTRIBUTION

Use a Poisson distribution with a given mean. Set Infectious_Period_Poisson_Mean.

DUAL_CONSTANT_DISTRIBUTION

Use a distribution where some individuals are set to a value of zero and the rest to a given value. Set Infectious_Period_Proportion_0 and Peak_2_Value. This distribution does not use the parameters set for CONSTANT_DISTRIBUTION.

DUAL_EXPONENTIAL_DISTRIBUTION

Use two exponential distributions with given means. Set Infectious_Period_Mean_1, Infectious_Period_Mean_2, and Infectious_Period_Proportion_1. This distribution does not use the parameters set for EXPONENTIAL_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Infectious_Period_Gaussian_Mean": 4,
    "Infectious_Period_Gaussian_Std_Dev": 1
}

Infectious_Period_Exponential

float

0

3.40E+38

6

The mean infectious period when Infectious_Period_Distribution is set to EXPONENTIAL_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "EXPONENTIAL_DISTRIBUTION",
    "Infectious_Period_Exponential": 4.25
}

Infectious_Period_Gaussian_Mean

float

0

3.40E+38

6

The mean of the infectious period when Infectious_Period_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Infectious_Period_Gaussian_Mean": 4,
    "Infectious_Period_Gaussian_Std_Dev": 1
}

Infectious_Period_Gaussian_Std_Dev

float

1.18E-38

3.40E+38

1

The standard deviation of the infectious period when Infectious_Period_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Infectious_Period_Gaussian_Mean": 4,
    "Infectious_Period_Gaussian_Std_Dev": 1
}

Infectious_Period_Kappa

float

1.18E-38

3.40E+38

1

The shape value for the infectious period when Infectious_Period_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "WEIBULL_DISTRIBUTION",
    "Infectious_Period_Kappa": 0.9,
    "Infectious_Period_Lambda": 1.5
}

Infectious_Period_Lambda

float

1.18E-38

3.40E+38

1

The scale value for the infectious period when Infectious_Period_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "WEIBULL_DISTRIBUTION",
    "Infectious_Period_Kappa": 0.9,
    "Infectious_Period_Lambda": 1.5
}

Infectious_Period_Log_Normal_Mu

float

1.18E-38

3.40E+38

6

The mean of the natural log of the infectious period when Infectious_Period_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Infectious_Period_Log_Normal_Mu": 9,
    "Infectious_Period_Log_Normal_Sigma": 2
}

Infectious_Period_Log_Normal_Sigma

float

1.18E-38

3.40E+38

1

The standard deviation of the natural log of the infectious period when Infectious_Period_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Infectious_Period_Log_Normal_Mu": 9,
    "Infectious_Period_Log_Normal_Sigma": 2
}

Infectious_Period_Max

float

0

3.40E+38

1

The maximum infectious period when Infectious_Period_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "UNIFORM_DISTRIBUTION",
    "Infectious_Period_Min": 2,
    "Infectious_Period_Max": 7
}

Infectious_Period_Mean_1

float

1.18E-38

3.40E+38

1

The mean of the first exponential distribution when Infectious_Period_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Infectious_Period_Mean_1": 4,
    "Infectious_Period_Mean_2": 12,
    "Infectious_Period_Proportion_1": 0.2
}

Infectious_Period_Mean_2

float

1.18E-38

3.40E+38

1

The mean of the second exponential distribution when Infectious_Period_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Infectious_Period_Mean_1": 4,
    "Infectious_Period_Mean_2": 12,
    "Infectious_Period_Proportion_1": 0.2
}

Infectious_Period_Min

float

0

3.40E+38

0

The minimum infectious period when Infectious_Period_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "UNIFORM_DISTRIBUTION",
    "Infectious_Period_Min": 2,
    "Infectious_Period_Max": 7
}

Infectious_Period_Peak_2_Value

float

0

3.40E+38

1

The infectious period value to assign to the remaining individuals when Infectious_Period_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Infectious_Period_Proportion_0": 0.25,
    "Infectious_Period_Peak_2_Value": 5
}

Infectious_Period_Poisson_Mean

float

0

3.40E+38

6

The mean of the infectious period with Infectious_Period_Distribution is set to POISSON_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "POISSON_DISTRIBUTION",
    "Infectious_Period_Poisson_Mean": 5
}

Infectious_Period_Proportion_0

float

0

1

1

The proportion of individuals to assign a value of zero days infectiousness when Infectious_Period_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Infectious_Period_Proportion_0": 0.25,
    "Infectious_Period_Peak_2_Value": 5
}

Infectious_Period_Proportion_1

float

0

1

1

The proportion of individuals in the first exponential distribution when Infectious_Period_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Infectious_Period_Mean_1": 4,
    "Infectious_Period_Mean_2": 12,
    "Infectious_Period_Proportion_1": 0.2
}

Symptomatic_Infectious_Offset

float

-3.40E+38

3.40E+38

3.40E+38

Amount of time, in days, after the infectious period starts that symptoms appear. Negative values imply an individual is symptomatic before infectious. If this offset is greater than the infectious duration, the infection will not be symptomatic. For example, if Incubation_Period_Constant is set to 10 and Symptomatic_Infectious_Offset is set to 4, then an infected person would become symptomatic 14 days after transmission.

 
{
    "Infectious_Period_Distribution": "CONSTANT_DISTRIBUTION",
    "Symptomatic_Infectious_Offset": 4,
    "Incubation_Period_Constant": 10
}

Symptomatic_Infectious_Offset

float

-3.40E+38

3.40E+38

3.40E+38

Amount of time, in days, after the infectious period starts that symptoms appear. Negative values imply an individual is symptomatic before infectious. If this offset is greater than the infectious duration, the infection will not be symptomatic. For example, if Incubation_Period_Constant is set to 10 and Symptomatic_Infectious_Offset is set to 4, then an infected person would become symptomatic 14 days after transmission.

 
{
    "Infectious_Period_Distribution": "CONSTANT_DISTRIBUTION",
    "Symptomatic_Infectious_Offset": 4,
    "Incubation_Period_Constant": 10
}
New configuration parameters (Beta)

Note: These configuration parameters are currently in beta release and have not yet been fully tested.

For the generic simulation type, the following new configuration parameters are available:

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Report_Coordinator_Event_Recorder

boolean

0

Enables or disables the ReportCoordinatorEventRecorder.csv output report for coordinator events. When enabled (set to 1) reports will be generated for the broadcasted valid coordinator events, as specified in Report_Coordinator_Event_Recorder_Events.

Note: This configuration parameter is currently in beta release and has not yet been fully tested.

 
{
    "Custom_Coordinator_Events": [
        "Coordinator_Event_1",
        "Coordinator_Event_2",
        "Coordinator_Event_3"
    ],
    "Report_Coordinator_Event_Recorder": 1,
    "Report_Coordinator_Event_Recorder_Events": [
        "Coordinator_Event_1",
        "Coordinator_Event_2",
        "Coordinator_Event_3"
    ]
}

Report_Coordinator_Event_Recorder_Events

array of strings

NA

The list of events to include or exclude in the ReportCoordinatorEventRecorder.csv output report, based on how Report_Coordinator_Event_Recorder_Ignore_Events_In_List is set. This list must not be empty and is dependent upon Report_Coordinator_Event_Recorder being enabled. In addition, the events must be defined in Customer_Coordinator_Events.

Note: This configuration parameter is currently in beta release and has not yet been fully tested.

 
{
    "Custom_Coordinator_Events": [
        "Coordinator_Event_1",
        "Coordinator_Event_2",
        "Coordinator_Event_3"
    ],
    "Report_Coordinator_Event_Recorder": 1,
    "Report_Coordinator_Event_Recorder_Events": [
        "Coordinator_Event_1",
        "Coordinator_Event_2",
        "Coordinator_Event_3"
    ]
}

Report_Coordinator_Event_Recorder_Ignore_Events_In_List

boolean

0

If set to false (0), only the events listed in the Report_Coordinator_Event_Recorder_Events array will be included in the ReportCoordinatorEventRecorder.csv output report. If set to true (1), only the events listed in the array will be excluded, and all other events will be included. If you want to return all events from the simulation, leave the events array empty.

Note: This configuration parameter is currently in beta release and has not yet been fully tested.

 
{
    "Custom_Coordinator_Events": [
        "Coordinator_Event_1",
        "Coordinator_Event_2",
        "Coordinator_Event_3"
    ],
    "Report_Coordinator_Event_Recorder": 1,
    "Report_Coordinator_Event_Recorder_Events": [
        "Coordinator_Event_1",
        "Coordinator_Event_2",
        "Coordinator_Event_3"
    ],
    "Report_Coordinator_Event_Recorder_Ignore_Events_In_List": 0
}

Report_Node_Event_Recorder

boolean

0

Enables or disables the ReportNodeEventRecorder.csv output report. When enabled (set to 1) reports will be generated for the broadcasted valid node events, as specified in Report_Node_Event_Recorder_Events.

Note: This configuration parameter is currently in beta release and has not yet been fully tested.

 
{
    "Custom_Node_Events": [
        "Node_Event_1",
        "Node_Event_2"
    ],
    "Report_Node_Event_Recorder": 1,
    "Report_Node_Event_Recorder_Events": [
        "Node_Event_1",
        "Node_Event_2"
    ]
}

Report_Node_Event_Recorder_Events

array of strings

NA

The list of node events to include or exclude in the ReportNodeEventRecorder.csv output report, based on how Report_Node_Event_Recorder_Ignore_Events_In_List is set.

Note: This configuration parameter is currently in beta release and has not yet been fully tested.

 
{
    "Custom_Node_Events": [
        "Node_Event_1",
        "Node_Event_2"
    ],
    "Report_Node_Event_Recorder": 1,
    "Report_Node_Event_Recorder_Events": [
        "Node_Event_1",
        "Node_Event_2"
    ],
    "Report_Node_Event_Recorder_Ignore_Events_In_List": 0
}

Report_Node_Event_Recorder_Ignore_Events_In_List

boolean

0

If set to false (0), only the node events listed in the Report_Node_Event_Recorder_Events array will be included in the ReportNodeEventRecorder.csv output report. If set to true (1), only the node events listed in the array will be excluded, and all other node events will be included. If you want to return all node events from the simulation, leave the node events array empty.

Note: This configuration parameter is currently in beta release and has not yet been fully tested.

Value

Events array

Output file

0

No events

No events

0

One or more events

Only the listed events.

1

No events

All events occurring in the simulation.

1

One or more events

All simulation events occurring in the simulation, except for those listed.

 
{
    "Custom_Node_Events": [
        "Node_Event_1",
        "Node_Event_2"
    ],
    "Report_Node_Event_Recorder": 1,
    "Report_Node_Event_Recorder_Events": [
        "Node_Event_1",
        "Node_Event_2"
    ],
    "Report_Node_Event_Recorder_Ignore_Events_In_List": 0
}

Report_Node_Event_Recorder_Node_Properties

array of strings

[]

Specifies an array of (optional) node property keys, as defined in NodeProperties in the demographics file, to be added as additional columns to the ReportNodeEventRecorder.csv output report.

 
{
    "Custom_Node_Events": [
        "Node_Event_1",
        "Node_Event_2"
    ],
    "Report_Node_Event_Recorder": 1,
    "Report_Node_Event_Recorder_Events": [
        "Node_Event_1",
        "Node_Event_2"
    ],
    "Report_Node_Event_Recorder_Ignore_Events_In_List": 0,
    "Report_Node_Event_Recorder_Node_Properties": [
        "Geographic"
    ]
}

Report_Node_Event_Recorder_Stats_By_IPs

array of strings

[]

Specifies an array of (optional) individual property keys, as defined in IndividualProperties in the demographics file, to be added to the ReportNodeEventRecorder.csv output report. For each key:value pair there will be two additional columns (Key:Value:NumIndividuals, Key:Value:NumInfected) added to the report. For example, with a Risk property key assigned the values of LOW and HIGH there would then be four additional columns (Risk:LOW:NumIndividuals, Risk:LOW:NumInfected, Risk:HIGH:NumIndividuals, Risk:HIGH:NumInfected). An empty array equals no additional columns added.

 
{
    "Custom_Node_Events": [
        "Node_Event_1",
        "Node_Event_2"
    ],
    "Report_Node_Event_Recorder": 1,
    "Report_Node_Event_Recorder_Events": [
        "Node_Event_1",
        "Node_Event_2"
    ],
    "Report_Node_Event_Recorder_Ignore_Events_In_List": 0,
    "Report_Node_Event_Recorder_Node_Properties": [
        "Geographic"
    ],
    "Report_Node_Event_Recorder_Stats_By_IPs": [
        "Risk"
    ]
}

Report_Surveillance_Event_Recorder

boolean

0

Enables or disables the ReportSurveillanceEventRecorder.csv output report. When enabled (set to 1) reports will be generated for the broadcasted valid coordinator events, as specified in Report_Surveillance_Event_Recorder_Events.

Note: This configuration parameter is currently in beta release and has not yet been fully tested.

 
{
    "Custom_Coordinator_Events": [
        "Start_ACF",
        "Start_SIA_2",
        "Start_SIA_4",
        "Ind_Start_SIA_2",
        "Ind_Start_SIA_4",
        "Respond_To_Surveillance"
    ],
    "Report_Surveillance_Event_Recorder": 1,
    "Report_Surveillance_Event_Recorder_Events": [
        "Respond_To_Surveillance"
    ]
}

Report_Surveillance_Event_Recorder_Events

array of strings

NA

The list of events to include or exclude in the ReportSurveillanceEventRecorder.csv output report, based on how Report_Surveillance_Event_Recorder_Ignore_Events_In_List is set. This list must not be empty and is dependent upon Report_Surveillance_Event_Recorder being enabled.

Note: This configuration parameter is currently in beta release and has not yet been fully tested.

 
{
    "Custom_Coordinator_Events": [
        "Start_ACF",
        "Start_SIA_2",
        "Start_SIA_4",
        "Ind_Start_SIA_2",
        "Ind_Start_SIA_4",
        "Respond_To_Surveillance"
    ],
    "Report_Surveillance_Event_Recorder": 1,
    "Report_Surveillance_Event_Recorder_Events": [
        "Respond_To_Surveillance"
    ]
}

Report_Surveillance_Event_Recorder_Ignore_Events_In_List

boolean

0

If set to false (0), only the events listed in the Report_Surveillance_Event_Recorder_Events array will be included in the ReportSurveillanceEventRecorder.csv output report. If set to true (1), only the events listed in the array will be excluded, and all other events will be included. If you want to return all events from the simulation, leave the events array empty.

Note: This configuration parameter is currently in beta release and has not yet been fully tested.

Value

Events array

Output file

0

No events

No events

0

One or more events

Only the listed events.

1

No events

All events occurring in the simulation.

1

One or more events

All simulation events occurring in the simulation, except for those listed.

 
{
    "Custom_Coordinator_Events": [
        "Start_ACF",
        "Start_SIA_2",
        "Start_SIA_4",
        "Ind_Start_SIA_2",
        "Ind_Start_SIA_4",
        "Respond_To_Surveillance"
    ],
    "Report_Surveillance_Event_Recorder": 1,
    "Report_Surveillance_Event_Recorder_Events": [
        "Respond_To_Surveillance"
    ],
    "Report_Surveillance_Event_Recorder_Ignore_Events_In_List": 0
}

Report_Surveillance_Event_Recorder_Stats_By_IPs

array of strings

[]

Specifies an array of (optional) individual property keys, as defined in IndividualProperties in the demographics file, to be added to the ReportSurveillanceEventRecorder.csv output report. For each key:value pair there will be two additional columns (Key:Value:NumIndividuals, Key:Value:NumInfected) added to the report. For example, with a Risk property key assigned the values of LOW and HIGH there would then be four additional columns (Risk:LOW:NumIndividuals, Risk:LOW:NumInfected, Risk:HIGH:NumIndividuals, Risk:HIGH:NumInfected). An empty array equals no additional columns added.

 
{
    "Custom_Coordinator_Events": [
        "Start_ACF",
        "Start_SIA_2",
        "Start_SIA_4",
        "Ind_Start_SIA_2",
        "Ind_Start_SIA_4",
        "Respond_To_Surveillance"
    ],
    "Report_Surveillance_Event_Recorder": 1,
    "Report_Surveillance_Event_Recorder_Events": [
        "Respond_To_Surveillance"
    ],
    "Report_Surveillance_Event_Recorder_Stats_By_IPs": []
}
New demographics parameters

In all simulation types, you can now specify the quantity-per-timestep added to the total infectivity present in a node; it is equivalent to the expected number of additional infections in a node, per timestep. For example, if timestep is equal to a day, then setting InfectivityReservoirSize to a value of 0.1 would introduce an infection every 10 days from the exogenous reservoir.

For more information, see the table below.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

InfectivityReservoirEndTime

float

InfectivityReservoirStartTime

3.40E+38

3.40E+38

The ending of the exogeneous reservoir of infectivity. This parameter is conditional upon the configuration parameter, Enable_Infectivity_Reservoir, being enabled (set to 1).

 
{
    "NodeAttributes": {
        "InfectivityReservoirSize": 0.1,
        "InfectivityReservoirStartTime": 90,
        "InfectivityReservoirEndTime": 365
    }
}

InfectivityReservoirSize

float

0

3.40E+38

0

The quantity-per-timestep added to the total infectivity present in a node; it is equivalent to the expected number of additional infections in a node, per timestep. For example, if timestep is equal to a day, then setting InfectivityReservoirSize to a value of 0.1 would introduce an infection every 10 days from the exogenous reservoir. This parameter is conditional upon the configuration parameter, Enable_Infectivity_Reservoir, being enabled (set to 1).

 
{
    "NodeAttributes": {
        "InfectivityReservoirSize": 0.1,
        "InfectivityReservoirStartTime": 90,
        "InfectivityReservoirEndTime": 365
    }
}

InfectivityReservoirStartTime

float

0

3.40E+38

0

The beginning of the exogeneous reservoir of infectivity. This parameter is conditional upon the configuration parameter, Enable_Infectivity_Reservoir, being enabled (set to 1).

 
{
    "NodeAttributes": {
        "InfectivityReservoirSize": 0.1,
        "InfectivityReservoirStartTime": 90,
        "InfectivityReservoirEndTime": 365
    }
}
New campaign parameters

The following campaign classes are new and can be used in the (specified) models:

ImmunityBloodTest (generic)

The ImmunityBloodTest intervention class identifies whether an individual’s immunity meets a specified threshold (as set with the Positive_Threshold_AcquisitionImmunity campaign parameter) and then broadcasts an event based on the results; positive has immunity while negative does not.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Base_Sensitivity

float

0

1

1

The sensitivity of the diagnostic. This sets the proportion of the time that individuals with the condition being tested receive a positive diagnostic test. When set to zero, then individuals who have the condition always receive a false-negative diagnostic test.

 
{
    "Event_Coordinator_Config": {
        "Demographic_Coverage": 1,
        "Intervention_Config": {
            "Base_Sensitivity": 1,
            "Base_Specificity": 1,
            "Cost_To_Consumer": 0,
            "Days_To_Diagnosis": 0,
            "Positive_Diagnosis_Event": "TestedPositive_IamImmune",
            "Negative_Diagnosis_Event": "TestedNegative_IamSusceptible",
            "Treatment_Fraction": 1,
            "Positive_Threshold_AcquisitionImmunity": 0.99,
            "class": "ImmunityBloodTest"
        }
    }
}

Base_Specificity

float

0

1

1

The specificity of the diagnostic. This sets the proportion of the time that individuals without the condition being tested receive a negative diagnostic test. When set to 1, the diagnostic always accurately reflects the lack of having the condition. When set to zero, then individuals who do not have the condition always receive a false-positive diagnostic test.

 
{
    "Event_Coordinator_Config": {
        "Demographic_Coverage": 1,
        "Intervention_Config": {
            "Base_Sensitivity": 1,
            "Base_Specificity": 1,
            "Cost_To_Consumer": 0,
            "Days_To_Diagnosis": 0,
            "Negative_Diagnosis_Event": "TestedNegative_IamSusceptible",
            "Positive_Diagnosis_Event": "TestedPositive_IamImmune",
            "Positive_Threshold_AcquisitionImmunity": 0.99,
            "Treatment_Fraction": 1,
            "class": "ImmunityBloodTest"
        }
    }
}

Cost_To_Consumer

float

0

3.40282e+38

1

The unit ‘cost’ assigned to the diagnostic. Setting Cost_To_Consumer to zero for all other interventions, and to a non-zero amount for one intervention, provides a convenient way to track the number of times the intervention has been applied in a simulation.

 
{
    "Event_Coordinator_Config": {
        "Demographic_Coverage": 1,
        "Intervention_Config": {
            "Base_Sensitivity": 1,
            "Base_Specificity": 1,
            "Cost_To_Consumer": 0,
            "Days_To_Diagnosis": 0,
            "Negative_Diagnosis_Event": "TestedNegative_IamSusceptible",
            "Positive_Diagnosis_Event": "TestedPositive_IamImmune",
            "Positive_Threshold_AcquisitionImmunity": 0.99,
            "Treatment_Fraction": 1,
            "class": "ImmunityBloodTest"
        }
    }
}

Days_To_Diagnosis

float

0

3.40282e+38

0

The number of days from test until diagnosis.

 
{
    "Event_Coordinator_Config": {
        "Demographic_Coverage": 1,
        "Intervention_Config": {
            "Base_Sensitivity": 1,
            "Base_Specificity": 1,
            "Cost_To_Consumer": 0,
            "Days_To_Diagnosis": 0,
            "Negative_Diagnosis_Event": "TestedNegative_IamSusceptible",
            "Positive_Diagnosis_Event": "TestedPositive_IamImmune",
            "Positive_Threshold_AcquisitionImmunity": 0.99,
            "Treatment_Fraction": 1,
            "class": "ImmunityBloodTest"
        }
    }
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Event_Coordinator_Config": {
        "Demographic_Coverage": 1,
        "Intervention_Config": {
            "Base_Sensitivity": 1,
            "Base_Specificity": 1,
            "Cost_To_Consumer": 0,
            "Days_To_Diagnosis": 0,
            "Disqualifying_Properties": [
                "InterventionStatus:LostForever"
            ],
            "Negative_Diagnosis_Event": "TestedNegative_IamSusceptible",
            "Positive_Diagnosis_Event": "TestedPositive_IamImmune",
            "Positive_Threshold_AcquisitionImmunity": 0.99,
            "Treatment_Fraction": 1,
            "class": "ImmunityBloodTest"
        }
    }
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Event_Coordinator_Config": {
        "Demographic_Coverage": 1,
        "Intervention_Config": {
            "Base_Sensitivity": 1,
            "Base_Specificity": 1,
            "Cost_To_Consumer": 0,
            "Days_To_Diagnosis": 0,
            "Dont_Allow_Duplicates": 0,
            "Negative_Diagnosis_Event": "TestedNegative_IamSusceptible",
            "Positive_Diagnosis_Event": "TestedPositive_IamImmune",
            "Positive_Threshold_AcquisitionImmunity": 0.99,
            "Treatment_Fraction": 1,
            "class": "ImmunityBloodTest"
        }
    }
}

Enable_IsSymptomatic

boolean

NA

NA

0

If true (1), requires an infection to be symptomatic to return a positive test.

 
{
    "Enable_Is_Symptomatic": 1,
    "Base_Specificity": 0.85,
    "Base_Sensitivity": 0.92
}

Intervention_Name

string

NA

NA

ImmunityBloodTest

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Event_Coordinator_Config": {
        "Demographic_Coverage": 1,
        "Intervention_Config": {
            "Base_Sensitivity": 1,
            "Base_Specificity": 1,
            "Cost_To_Consumer": 0,
            "Days_To_Diagnosis": 0,
            "Intervention_Name": "Immunity Blood Test - Series 1",
            "Negative_Diagnosis_Event": "TestedNegative_IamSusceptible",
            "Positive_Diagnosis_Event": "TestedPositive_IamImmune",
            "Positive_Threshold_AcquisitionImmunity": 0.99,
            "Treatment_Fraction": 1,
            "class": "ImmunityBloodTest"
        }
    }
}

Negative_Diagnosis_Event

enum

NA

NA

“”

If an individual tests negative (does not have immunity), then an individual type event is broadcast; custom individual events can be defined in the config parameter Custom_Individual_Events. This may trigger another intervention when the event occurs.

 
{
    "Event_Coordinator_Config": {
        "Demographic_Coverage": 1,
        "Intervention_Config": {
            "Base_Sensitivity": 1,
            "Base_Specificity": 1,
            "Cost_To_Consumer": 0,
            "Days_To_Diagnosis": 0,
            "Negative_Diagnosis_Event": "TestedNegative_IamSusceptible",
            "Positive_Diagnosis_Event": "TestedPositive_IamImmune",
            "Positive_Threshold_AcquisitionImmunity": 0.99,
            "Treatment_Fraction": 1,
            "class": "ImmunityBloodTest"
        }
    }
}

New_Property_Value

string

NA

NA

NA

An optional IndividualProperty key:value pair that will be assigned when the intervention is first updated. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Positive_Diagnosis_Event

enum

NA

NA

“”

If the test is positive (has immunity), then an individual type event is broadcast; custom individual events can be defined in the config parameter Custom_Individual_Events. This may trigger another intervention when the event occurs.

 
{
    "Event_Coordinator_Config": {
        "Demographic_Coverage": 1,
        "Intervention_Config": {
            "Base_Sensitivity": 1,
            "Base_Specificity": 1,
            "Cost_To_Consumer": 0,
            "Days_To_Diagnosis": 0,
            "Negative_Diagnosis_Event": "TestedNegative_IamSusceptible",
            "Positive_Diagnosis_Event": "TestedPositive_IamImmune",
            "Positive_Threshold_AcquisitionImmunity": 0.99,
            "Treatment_Fraction": 1,
            "class": "ImmunityBloodTest"
        }
    }
}

Positive_Threshold_AcquisitionImmunity

float

0

1

1

Specifies the threshold for acquired immunity, where 1 equals 100% immunity and 0 equals 100% susceptible.

 
{
    "Event_Coordinator_Config": {
        "Demographic_Coverage": 1,
        "Intervention_Config": {
            "Base_Sensitivity": 1,
            "Base_Specificity": 1,
            "Cost_To_Consumer": 0,
            "Days_To_Diagnosis": 0,
            "Negative_Diagnosis_Event": "TestedNegative_IamSusceptible",
            "Positive_Diagnosis_Event": "TestedPositive_IamImmune",
            "Positive_Threshold_AcquisitionImmunity": 0.99,
            "Treatment_Fraction": 1,
            "class": "ImmunityBloodTest"
        }
    }
}

Treatment_Fraction

float

0

1

1

The fraction of positive diagnoses that are treated.

 
{
    "Event_Coordinator_Config": {
        "Demographic_Coverage": 1,
        "Intervention_Config": {
            "Base_Sensitivity": 1,
            "Base_Specificity": 1,
            "Cost_To_Consumer": 0,
            "Days_To_Diagnosis": 0,
            "Negative_Diagnosis_Event": "TestedNegative_IamSusceptible",
            "Positive_Diagnosis_Event": "TestedPositive_IamImmune",
            "Positive_Threshold_AcquisitionImmunity": 0.99,
            "Treatment_Fraction": 1,
            "class": "ImmunityBloodTest"
        }
    }
}
InterventionForCurrentPartners (HIV, STI)

The InterventionForCurrentPartners intervention class provides a mechanism for the partners of individuals in the care system to also seek care. Partners do not need to seek testing at the same time; a delay may occur between the initial test and the partner’s test. If a relationship has been paused, such as when a partner migrates to a different node, the partner will not be contacted.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Broadcast_Event

string

NA

NA

“”

The event that is immediately broadcast to the partner. Required if Event_or_Config is set to Event. See Event list for possible built-in values, or create custom values using Custom_Individual_Events.

 
{
    "Broadcast_Event": "HIVNewlyDiagnosed"
}

Disqualifying_Properties

array of strings

NA

NA

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

0

1

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Event_Or_Config

enum

NA

NA

Config

Specifies whether the current intervention (or a positive diagnosis, depending on the intervention class) distributes a nested intervention (the Config option) or an event will be broadcast which may trigger other interventions in the campaign file (the Event option). Possible values are:

  • Event

  • Config

 
{
    "Event_Or_Config": "Event"
}

Intervention_Config

JSON object

The intervention definition that is immediately distributed to the partner. Required if Event_Or_Config is set to Config.

 
{
    "class": "StandardInterventionDistributionEventCoordinator",
    "Intervention_Config": {
        "class": "NodeLevelHealthTriggeredIV",
        "Trigger_Condition_List": [
            "Some_Other_Event"
        ],
        "Actual_IndividualIntervention_Config": {
            "class": "InterventionForCurrentPartners",
            "Disqualifying_Properties": [],
            "New_Property_Value": "CascadeState:TestingCurrentPartner",
            "Relationship_Types": [
                "MARITAL",
                "INFORMAL",
                "TRANSITORY",
                "COMMERCIAL"
            ],
            "Minimum_Duration_Years": 0.5,
            "Prioritize_Partners_By": "LONGER_TIME_IN_RELATIONSHIP",
            "Maximum_Partners": 2,
            "Event_Or_Config": "Event",
            "Broadcast_Event": "HIVNewlyDiagnosed"
        }
    }
}

Intervention_Name

string

NA

NA

InterventionForCurrentPartners

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "Intervention_Name": "Intervention_For_Spouse",
        "class": "InterventionForCurrentPartnersr"
    }
}

Maximum_Partners

integer

0

1000

100

The maximum number of partners that will receive the intervention. Required when Prioritize_Partners_By is not set to NO_PRIORITIZATION.

 
{
    "Prioritize_Partners_By": "NO_PRIORITIZATION",
    "Maximum_Partners": 2
}

Minimum_Duration_Years

float

0

200

0

The minimum amount of time, in years, between relationship formation and the current time for the partner to qualify for the intervention.

 
{
    "Minimum_Duration_Years": 0.5
}

New_Property_Value

string

NA

NA

An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Prioritize_Partners_By

enum

NA

NA

NO_PRIORITIZATION

How to prioritize partners for the intervention, as long as they have been in a relationship longer than Minimum_Duration_Years. Possible values are:

NO_PRIORTIZATION

All partners are contacted.

CHOSEN_AT_RANDOM

Partners are randomly selected until Maximum_Partners have received the intervention.

LONGER_TIME_IN_RELATIONSHIP

Partners are sorted in descending order of the duration of the relationship. Partners are contacted from the beginning of this list until Maximum_Partners have received the intervention.

SHORTER_TIME_IN RELATIONSHIP

Partners are sorted in ascending order of the duration of the relationship. Partners are contacted from the beginning of the list until Maximum_Partners have received the intervention.

OLDER_AGE

Partners are sorted in descending order of their age. Partners are contacted from the beginning of this list until Maximum_Partners have received the intervention.

YOUNGER_AGE

Partners are sorted in ascending order of the duration of the relationship. Partners are contacted from the beginning of this list until Maximum_Partners have received the intervention.

RELATIONSHIP_TYPE

Partners are sorted based on the order of relationship types defined in the Relationship_Types array. For example, “Relationship_Types” : [“MARITAL”, “INFORMAL”, “TRANSITORY”, “COMMERCIAL”], will prioritize marital first, then informal, then transitory, then commercial, with random selection between mulitple partners of the same type.

 
{
    "Prioritize_Partners_By": "LONGER_TIME_IN_RELATIONSHIP"
}

Relationship_Types

array of strings

NA

NA

NA

An array listing all possible relationship types for which partners can qualify for the intervention. Supported partnerships include TRANSITORY, INFORMAL, MARITAL, and COMMERCIAL. If Prioritize_Partners_By is set to RELATIONSHIP_TYPE, then the order of these types is used. The array may not contain duplicates, and cannot be empty.

 
{
    "Relationship_Types": [
        "MARITAL",
        "INFORMAL",
        "TRANSITORY",
        "COMMERCIAL"
    ]
}
OutbreakIndividualTBorHIV (tuberculosis)

The OutbreakIndividualTBorHIV class extends OutbreakIndividual class and allows for specifying HIV or a specific strain of infection for TB.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Clade

integer

0

9

0

The clade ID (Clade) of the outbreak infection. Together with the genome ID (Genome) they are a unitary object representing a strain of infection, which allows for differentiation among infections. Values for Clade may range from 0 to Number_of_Clades-1.

Intervention distribution example:

{
    "Enable_Strain_Tracking": 1,
    "Events": [
        {
            "Event_Coordinator_Config": {
                "Demographic_Coverage": 0.001,
                "Intervention_Config": {
                    "Clade": 1,
                    "Genome": 3,
                    "IgnoreImmunity": 1,
                    "class": "Outbreak"
                },
                "Target_Demographic": "Everyone",
                "class": "StandardInterventionDistributionEventCoordinator"
            },
            "Event_Name": "Outbreak",
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 30,
            "class": "CampaignEvent"
        }
    ]
}

Genome

integer

0

16777215

0

The genome ID (Genome) of the outbreak infection. Together with the clade ID (Clade) they represent an infection as a unitary object. Values for Genome may range from -1 to 2Log2_Number_of_Genomes_per_Clade-1

Intervention distribution example:

{
    "Enable_Strain_Tracking": 1,
    "Events": [
        {
            "Event_Coordinator_Config": {
                "Demographic_Coverage": 0.001,
                "Intervention_Config": {
                    "Clade": 1,
                    "Genome": 3,
                    "IgnoreImmunity": 1,
                    "class": "OutbreakIndividualTBorHIV"
                },
                "Target_Demographic": "Everyone",
                "class": "StandardInterventionDistributionEventCoordinator"
            },
            "Event_Name": "OutbreakIndividualTBorHIV",
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 30,
            "class": "CampaignEvent"
        }
    ]
}

Ignore_Immunity

boolean

0

1

1

Individuals will be force-infected (with a specific strain) regardless of actual immunity level when set to true (1).

 
{
    "Intervention_Config": {
        "Antigen": 0,
        "Genome": 0,
        "Infection_Type": "TB",
        "Outbreak_Source": "PrevalenceIncrease",
        "Ignore_Immunity": 0,
        "class": "OutbreakIndividualTBorHIV"
    }
}

Incubation_Period_Override

integer

-1

2147480000

-1

The incubation period, in days, that infected individuals will go through before becoming infectious. This value overrides the incubation period set in the configuration file. Set to -1 to honor the configuration parameter settings.

 
{
    "Intervention_Config": {
        "Antigen": 0,
        "Genome": 0,
        "Ignore_Immunity": 0,
        "Incubation_Period_Override": -1,
        "Infection_Type": "TB",
        "Outbreak_Source": "PrevalenceIncrease",
        "class": "OutbreakIndividualTBorHIV"
    }
}

Infection_Type

enum

NA

NA

HIV

Infection type. Possible values are:

HIV

HIV infection.

TB

TB infection.

 
{
    "Intervention_Config": {
        "Antigen": 0,
        "Genome": 0,
        "Ignore_Immunity": 0,
        "Incubation_Period_Override": -1,
        "Infection_Type": "TB",
        "Outbreak_Source": "PrevalenceIncrease",
        "class": "OutbreakIndividualTBorHIV"
    }
}
EnvironmentalDiagnostic (typhoid)

The EnvironmentalDiagnostic intervention class identifies contaminated locations by sampling the environment, comparing the value to a threshold, and broadcasting either a positive or negative node event.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Base_Sensitivity

float

0

1

1

The likelihood that a positive measurement was made. If the contagion measurement is greater than the Sample_Threshold, a random number is drawn to determine if the detection was actually made.

 
{
    "Base_Sensitivity": 0.7,
    "Sample_Threshold": 0.85
}

Base_Specificity

float

0

1

1

The proportion of time that the environment being tested and without the condition receives a negative diagnostic test. When set to 1, the diagnostic always accurately reflects the lack of having the condition. When set to zero, then environments that do not have the condition always receive a false-positive diagnostic test.

 
{
    "Base_Specificity": 0.9
}

Disqualifying_Properties

array of strings

NA

NA

NA

A list of NodeProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to nodes with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": []
}

Environment_IP_Key_Value

string

NA

NA

NA

An IndividualProperty key:value pair that indicates a specific transmission pool, typically used to identify a location. If none is provided, the sample will be taken on the entire node.

 
{
    "Environment_IP_Key_Value": "Location:UP_RIVER"
}

Intervention_Name

string

NA

NA

EnvironmentalDiagnostic

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "Intervention_Name": "Downriver sample testing",
        "class": "EnvironmentalDiagnostic"
    }
}

Negative_Diagnostic_Event

string

NA

NA

“”

The event that will be broadcast to the node when the sample value is less than the threshold (e.g. the test is negative). If this is an empty string or set to NoTrigger, no event will be broadcast. See Event list for possible values, or create custom events using Custom_Node_Events.

 
{
    "Event_Or_Config": "Event",
    "Negative_Diagnostic_Event": "Up_River_Clean"
}

New_Property_Value

string

NA

NA

NA

An optional NodeProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Positive_Diagnostic_Event

string

NA

NA

“”

The event that will be broadcast to the node when the sample value is greater than the threshold (e.g. the test is positive). This cannot be an empty string or set to NoTrigger. See Event list for possible values, or create custom events using Custom_Node_Events.

 
{
    "Positive_Diagnostic_Event": "Up_River_Contagion_Found"
}

Sample_Threshold

float

0

3.40282e+38

0

The threshold that delineates a positive versus negative sampling result. If the sample is greater than the threshold, a positive finding will result; if the value is less than the threshold, it will be negative. This does not include values equal to the threshold so that the threshold can be set to zero; if the threshold is zero, the test is simply looking for any deposit in the transmission pool.

 
{
    "Sample_Threshold": 0.2
}
TyphoidCarrierDiagnostic (typhoid)

The TyphoidCarrierDiagnostic class extends SimpleDiagnostic class and allows for positive test diagnostic when an individual is a chronic typhoid carrier.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Base_Sensitivity

float

0

1

1

The sensitivity of the diagnostic. This sets the proportion of the time that individuals with the condition being tested receive a positive diagnostic test. When set to zero, then individuals who have the condition always receive a false-negative diagnostic test.

 
{
    "Base_Sensitivity": 1
}

Base_Specificity

float

0

1

1

The specificity of the diagnostic. This sets the proportion of the time that individuals without the condition being tested receive a negative diagnostic test. When set to 1, the diagnostic always accurately reflects the lack of having the condition. When set to zero, then individuals who do not have the condition always receive a false-positive diagnostic test.

 
{
    "Base_Specificity": 1
}

Cost_To_Consumer

float

0

3.40282e+38

1

The unit ‘cost’ assigned to the diagnostic. Setting Cost_To_Consumer to zero for all other interventions, and to a non-zero amount for one intervention, provides a convenient way to track the number of times the intervention has been applied in a simulation.

 
{
    "Cost_To_Consumer": 10
}

Days_To_Diagnosis

float

0

3.40282e+38

0

The number of days from test until diagnosis.

 
{
    "Days_To_Diagnosis": 2
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

0

1

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Enable_Is_Symptomatic

boolean

0

1

0

If true (1), requires an infection to be symptomatic to return a positive test.

 
{
    "Base_Sensitivity": 0.92,
    "Base_Specificity": 0.85,
    "Enable_Is_Symptomatic": 1
}

Event_Or_Config

enum

NA

NA

Config

Specifies whether the current intervention (or a positive diagnosis, depending on the intervention class) distributes a nested intervention (the Config option) or an event will be broadcast which may trigger other interventions in the campaign file (the Event option). Possible values are:

  • Event

  • Config

 
{
    "Event_Or_Config": "Config"
}

Intervention_Name

string

NA

NA

NA

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "Intervention_Name": "Typhoid diagnostic test",
        "class": "TyphoidCarrierDiagnostic"
    }
}

New_Property_Value

string

NA

NA

“

An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Positive_Diagnosis_Config

JSON object

NA

NA

NA

The intervention distributed to individuals if they test positive. Only used when Event_Or_Config is set to Config.

 
{
    "Event_Coordinator_Config": {
        "Intervention_Config": {
            "class": "TyphoidCarrierDiagnostic",
            "Event_Or_Config": "Event",
            "Positive_Diagnosis_Event": "Chronic_Carrier"
        },
        "Number_Repetitions": 10,
        "Timesteps_Between_Repetitions": 180,
        "class": "StandardInterventionDistributionEventCoordinator"
    },
    "Event_Name": "Diagnostic",
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 2,
    "Target_Demographic": "Everyone",
    "class": "CampaignEvent"
}

Positive_Diagnosis_Event

enum

NA

NA

“”

If the test is positive, this specifies an event that can trigger another intervention when the event occurs. Only used if Event_Or_Config is set to Event. See Event list for possible values, or create custom events using Custom_Individual_Events.

 
{
    "Event_Coordinator_Config": {
        "Intervention_Config": {
            "class": "TyphoidCarrierDiagnostic",
            "Event_Or_Config": "Event",
            "Positive_Diagnosis_Event": "Chronic_Carrier"
        },
        "Number_Repetitions": 10,
        "Timesteps_Between_Repetitions": 180,
        "class": "StandardInterventionDistributionEventCoordinator"
    },
    "Event_Name": "Diagnostic",
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 2,
    "Target_Demographic": "Everyone",
    "class": "CampaignEvent"
}

Treatment_Fraction

float

0

1

1

The fraction of positive diagnoses that are treated.

 
{
    "Treatment_Fraction": 1
}
TyphoidVaccine (typhoid)

The TyphoidVaccine intervention class identifies contaminated locations by sampling the environment, comparing the value to a threshold, and broadcasting either a positive or negative node event.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Changing_Effect

JSON object

NA

NA

NA

A highly configurable effect that changes over time. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Intervention_Config": {
        "Changing_Effect": {
            "Durability_Map": {
                "Times": [
                    0,
                    10,
                    11,
                    20,
                    21,
                    26,
                    27,
                    33,
                    34,
                    40
                ],
                "Values": [
                    0,
                    0,
                    1,
                    1,
                    0,
                    0,
                    1,
                    1,
                    0,
                    0
                ]
            },
            "Expire_At_Durability_Map_End": 1,
            "Initial_Effect": 1,
            "class": "WaningEffectMapLinear"
        },
        "Effect": 1,
        "Mode": "Dose",
        "Target_Demographic": "Everyone",
        "class": "TyphoidVaccine"
    }
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

0

1

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Effect

float

0

1

1

The efficacy of the Typhoid vaccine intervention. For example, a value of 1 would be 100 percent efficacy for all targeted nodes within the intervention.

 
{
    "Intervention_Config": {
        "Changing_Effect": {
            "Durability_Map": {
                "Times": [
                    0,
                    10,
                    11,
                    20,
                    21,
                    26,
                    27,
                    33,
                    34,
                    40
                ],
                "Values": [
                    0,
                    0,
                    1,
                    1,
                    0,
                    0,
                    1,
                    1,
                    0,
                    0
                ]
            },
            "Expire_At_Durability_Map_End": 1,
            "Initial_Effect": 1,
            "class": "WaningEffectMapLinear"
        },
        "Effect": 1,
        "Mode": "Dose",
        "Target_Demographic": "Everyone",
        "class": "TyphoidVaccine"
    }
}

Intervention_Name

string

NA

NA

NA

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "TyphoidVaccine",
        "Intervention_Name": "Country-wide vaccination campaign"
    }
}

Mode

enum

NA

NA

Shedding

The mode of contact transmission of typhoid targeted by the intervention. Possible values are:

  • Dose (disease acquisition)

    Reduces the amount of contagion an individual is exposed to per exposure event, by a proportion. This is similar to setting Vaccine_Type to AcquisitionBlocking when using SimpleVaccine.

  • Exposures (disease acquisition)

    Reduces the number of exposures to pathogen by a proportion. This is similar to setting Vaccine_Type to AcquisitionBlocking when using SimpleVaccine.

  • Shedding (disease transmission)

    Reduces the amount of contagion shed if a vaccinated individual is infected. This is similar to setting Vaccine_Type to TransmissionBlocking when using SimpleVaccine.

 
{
    "Intervention_Config": {
        "Changing_Effect": {
            "Durability_Map": {
                "Times": [
                    0,
                    10,
                    11,
                    20,
                    21,
                    26,
                    27,
                    33,
                    34,
                    40
                ],
                "Values": [
                    0,
                    0,
                    1,
                    1,
                    0,
                    0,
                    1,
                    1,
                    0,
                    0
                ]
            },
            "Expire_At_Durability_Map_End": 1,
            "Initial_Effect": 1,
            "class": "WaningEffectMapLinear"
        },
        "Effect": 1,
        "Mode": "Dose",
        "Target_Demographic": "Everyone",
        "class": "TyphoidVaccine"
    }
}

New_Property_Value

string

NA

NA

“

An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}
TyphoidWASH (typhoid)

The TyphoidWASH intervention class acts on exposure through either the contact contagion population or the environmental contagion population in the simulation. The intervention can be configured to reduce either exposure dose or exposure frequency for each route, simulating effects of water, sanitation, and hygiene (WASH) interventions.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Changing_Effect

JSON object

NA

NA

NA

A highly configurable effect that changes over time. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Intervention_Config": {
        "Changing_Effect": {
            "Expire_At_Durability_Map_End": 1,
            "Initial_Effect": 1,
            "class": "WaningEffectConstant"
        },
        "Effect": 1,
        "Mode": "Exposures",
        "Use_Property_Targeting": 0,
        "class": "TyphoidWASH"
    }
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Effect

float

0

1

1

The efficacy of the TyphoidWASH intervention. For example, a value of 1 would be 100 percent efficacy for all targeted nodes within the intervention.

 
{
    "Intervention_Config": {
        "Changing_Effect": {
            "Expire_At_Durability_Map_End": 1,
            "Initial_Effect": 1,
            "class": "WaningEffectConstant"
        },
        "Effect": 1,
        "Mode": "Exposures",
        "Use_Property_Targeting": 0,
        "class": "TyphoidWASH"
    }
}

Intervention_Name

string

NA

NA

NA

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "Intervention_Name": "TyphoidWASH intervention campaign",
        "class": "TyphoidWASH"
    }
}

Mode

enum

NA

NA

Shedding

The mode of environmental or contact transmission of typhoid targeted by the intervention. Possible values are:

  • Dose (disease acquisition)

    Reduces the amount of contagion an individual is exposed to per exposure event, by a proportion.

  • Exposures (disease acquisition)

    Reduces the number of exposures to pathogen by a proportion.

  • Shedding (disease transmission)

    Reduces the amount of contagion shed if a vaccinated individual is infected.

 
{
    "Intervention_Config": {
        "Mode": "Exposures",
        "Effect": 1,
        "Use_Property_Targeting": 0,
        "Changing_Effect": {
            "class": "WaningEffectConstant",
            "Initial_Effect": 1,
            "Expire_At_Durability_Map_End": 1
        },
        "class": "TyphoidWASH"
    }
}

New_Property_Value

string

NA

NA

NA

An optional NodeProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Targeted_Individual_Properties

string

NA

NA

NA

Individual Property key-value pairs to be targeted (optional).

 
{
    "Intervention_Config": {
        "Mode": "Exposures",
        "Effect": 1,
        "Use_Property_Targeting": 1,
        "Changing_Effect": {
            "class": "WaningEffectMapLinear",
            "Initial_Effect": 1,
            "Expire_At_Durability_Map_End": 1,
            "Durability_Map": {
                "Times": [
                    0,
                    100,
                    200,
                    300,
                    400,
                    500
                ],
                "Values": [
                    0,
                    1,
                    1,
                    1,
                    0,
                    1
                ]
            }
        },
        "class": "TyphoidWASH",
        "Targeted_Individual_Properties": "Risk:Target_Demographic"
    }
}

Use_Property_Targeting

boolean

0

1

1

Set to 1 (true) – or omit – if you want to use the Targeted_Individual_Property parameter to limit the effect of this intervention to just certain individuals. Set to 0 to apply effect to everyone.

 
{
    "Event_Coordinator_Config": {
        "Intervention_Config": {
            "Mode": "Shedding",
            "Use_Property_Targeting": 0,
            "Effect": 1.0,
            "Changing_Effect": {
                "class": "WaningEffectConstant",
                "Initial_Effect": 1.0,
                "Expire_At_Durability_Map_End": 1
            },
            "class": "TyphoidWASH"
        },
        "class": "StandardInterventionDistributionEventCoordinator"
    },
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 10,
    "class": "CampaignEvent"
}
New campaign parameters (Beta)

Note

These campaign classes and associated parameters are currently in beta release and have not yet been fully tested.

BroadcastCoordinatorEvent (generic)

The BroadcastCoordinatorEvent coordinator class broadcasts the event you specify. This can be used with the campaign class, SurveillanceEventCoordinator, that can monitor and listen for events received from BroadcastCoordinatorEvent and then perform an action based on the broadcasted event. You can also use this for the reporting of the broadcasted events by setting the configuraton parameters, Report_Node_Event_Recorder and Report_Surveillance_Event_Recorder, which listen to events to be recorded.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Broadcast_Event

string

NA

NA

“”

The name of the event to be broadcast. This event must be set in the Custom_Coordinator_Events configuration parameter. This cannot be assigned an empty string for the event.

 
{
    "class": "CampaignEvent",
    "Start_Day": 5,
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Event_Coordinator_Config": {
        "class": "BroadcastCoordinatorEvent",
        "Coordinator_Name": "Coordnator_1",
        "Cost_To_Consumer": 10,
        "Broadcast_Event": "Coordinator_Event_1"
    }
}

Coordinator_Name

string

NA

NA

NA

The unique identifying coordinator name, which is useful with the output report, ReportCoordinatorEventRecorder.csv.

 
{
    "class": "CampaignEvent",
    "Start_Day": 25,
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Event_Coordinator_Config": {
        "class": "BroadcastCoordinatorEvent",
        "Coordinator_Name": "Coordinator_3",
        "Cost_To_Consumer": 30,
        "Broadcast_Event": "Coordinator_Event_3"
    }
}

Cost_To_Consumer

float

0

3.40282e+38

0

The unit cost of broadcasting the event.

 
{
    "class": "CampaignEvent",
    "Start_Day": 15,
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Event_Coordinator_Config": {
        "class": "BroadcastCoordinatorEvent",
        "Coordinator_Name": "Coordnator_2",
        "Cost_To_Consumer": 20,
        "Broadcast_Event": "Coordinator_Event_2"
    }
}
BroadcastNodeEvent (generic)

The BroadcastNodeEvent coordinator class broadcasts node events. This can be used with the campaign class, SurveillanceEventCoordinator, that can monitor and listen for events received from BroadcastNodeEvent and then perform an action based on the broadcasted event. You can also use this for the reporting of the broadcasted events by setting the configuraton parameters, Report_Node_Event_Recorder and Report_Surveillance_Event_Recorder, which listen to events to be recorded.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Broadcast_Event

string

NA

NA

UNINITIALIZED STRING

The name of the node event to broadcast. This event must be set in the Custom_Node_Events configuration parameter. Note that there are no built-in node events for malaria.

 
{
    "class": "CampaignEvent",
    "Start_Day": 5,
    "Nodeset_Config": {
        "class": "NodeSetNodeList",
        "Node_List": [
            1
        ]
    },
    "Event_Coordinator_Config": {
        "class": "StandardInterventionDistributionEventCoordinator",
        "Intervention_Config": {
            "class": "BroadcastNodeEvent",
            "Cost_To_Consumer": 10,
            "Broadcast_Event": "Node_Event_1"
        }
    }
}

Cost_To_Consumer

float

0

999999

0

The unit cost of the intervention campaign that will be assigned to the specified nodes, as configured under Nodeset_Config.

 
{
    "class": "CampaignEvent",
    "Start_Day": 5,
    "Nodeset_Config": {
        "class": "NodeSetNodeList",
        "Node_List": [
            1
        ]
    },
    "Event_Coordinator_Config": {
        "class": "StandardInterventionDistributionEventCoordinator",
        "Intervention_Config": {
            "class": "BroadcastNodeEvent",
            "Cost_To_Consumer": 10,
            "Broadcast_Event": "Node_Event_1"
        }
    }
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of NodeProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Intervention_Name

string

NA

NA

BroadcastNodeEvent

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "BroadcastNodeEvent",
        "Intervention_Name": "Mosquito coil repellent"
    }
}

New_Property_Value

string

NA

NA

NA

An optional NodeProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}
DelayEventCoordinator (generic)

The DelayEventCoordinator coordinator class insert delays into coordinator event chains. This campaign event is typically used with BroadcastCoordinatorEvent to broadcast events after the delays.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Coordinator_Name

string

NA

NA

DelayEventCoordinator

The unique identifying coordinator name, which is useful with the output report, ReportCoordinatorEventRecorder.csv.

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "DelayEventCoordinator_10",
        "Delay_Complete_Event": "Completion_Delayed_Coordinator_Event_1",
        "Delay_Period_Distribution": "FIXED_DURATION",
        "Delay_Period_Fixed": 25,
        "Duration": 100,
        "Start_Trigger_Condition_List": [
            "Coordinator_Event_1"
        ],
        "Stop_Trigger_Condition_List": [],
        "class": "DelayEventCoordinator"
    },
    "Event_Name": "Delay",
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "Target_Demographic": "Everyone",
    "class": "CampaignEvent",
    "comment": "Delay"
}

Delay_Complete_Event

string

NA

NA

NA

The delay completion event to be included in the ReportCoordinatorEventRecorder.csv output report, upon completion of the delay period.

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "DelayEventCoordinator_10",
        "Delay_Complete_Event": "Completion_Delayed_Coordinator_Event_1",
        "Delay_Distribution": "GAUSSIAN_DURATION",
        "Delay_Period_Mean": 25,
        "Delay_Period_Std_Dev": 5,
        "Duration": 100,
        "Start_Trigger_Condition_List": [
            "Coordinator_Event_1"
        ],
        "Stop_Trigger_Condition_List": [],
        "class": "DelayEventCoordinator"
    },
    "Event_Name": "Delay",
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "Target_Demographic": "Everyone",
    "class": "CampaignEvent",
    "comment": "Delay"
}

Delay_Period_Constant

float

0

3.40282E+38

-1

The delay period to use for all interventions, in days, when Delay_Period_Distribution is set to CONSTANT_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "CONSTANT_DISTRIBUTION",
    "Delay_Period_Constant": 8
}

Delay_Period_Distribution

enum

NA

NA

NOT_INITIALIZED

The distribution type to use for assigning the delay period for distributing interventions. Each assigned value is a random draw from the distribution.

Possible values are:

NOT_INITIALIZED

No distribution set.

CONSTANT_DISTRIBUTION

Use the same value for each individual. Set Delay_Period_Constant.

UNIFORM_DISTRIBUTION

Use a uniform distribution with a given minimum and maximum. Set Delay_Period_Max and Delay_Period_Min.

GAUSSIAN_DISTRIBUTION

The distribution is Gaussian (or normal). Values are resampled to ensure >= 0. Set Delay_Period_Gaussian_Mean and Delay_Period_Gaussian_Std_Dev.

EXPONENTIAL_DISTRIBUTION

The distribution is exponential with a given mean. Set Delay_Period_Exponential.

WEIBULL_DISTRIBUTION

Use a Weibull distribution with a given shape and scale. Set Delay_Period_Kappa and Delay_Period_Lambda.

LOG_NORMAL_DISTRIBUTION

Use a log-normal distribution with a given mean and standard deviation of the natural log. Set Delay_Period_Log_Normal_Mu and Delay_Period_Log_Normal_Sigma.

POISSON_DISTRIBUTION

Use a Poisson distribution with a given mean. Set Delay_Period_Poisson_Mean.

DUAL_CONSTANT_DISTRIBUTION

Use a distribution where some individuals are set to a value of zero and the rest to a given value. Set Delay_Period_Proportion_0 and Peak_2_Value. This distribution does not use the parameters set for CONSTANT_DISTRIBUTION.

DUAL_EXPONENTIAL_DISTRIBUTION

Use two exponential distributions with given means. Set Delay_Period_Mean_1, Delay_Period_Mean_2, and Delay_Period_Proportion_1. This distribution does not use the parameters set for EXPONENTIAL_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Delay_Period_Gaussian_Mean": 8,
    "Delay_Period_Gaussian_Std_Dev": 1.5
}

Delay_Period_Exponential

float

0

3.40282E+38

-1

The mean of the delay period, in days, when Delay_Period_Distribution is set to EXPONENTIAL_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "EXPONENTIAL_DISTRIBUTION",
    "Delay_Period_Exponential": 4.25
}

Delay_Period_Gaussian_Mean

float

0

3.40282E+38

-1

The mean of the delay period, in days, when Delay_Period_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Delay_Period_Gaussian_Mean": 8,
    "Delay_Period_Gaussian_Std_Dev": 1.5
}

Delay_Period_Gaussian_Std_Dev

float

1.17549E-38

3.40282E+38

-1

The standard deviation of the delay period, in days, when Delay_Period_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Delay_Period_Gaussian_Mean": 8,
    "Delay_Period_Gaussian_Std_Dev": 1.5
}

Delay_Period_Kappa

float

1.17549E-38

3.40282E+38

-1

The shape value for the delay period, in days, when Delay_Period_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "WEIBULL_DISTRIBUTION",
    "Delay_Period_Kappa": 0.9,
    "Delay_Period_Lambda": 1.5
}

Delay_Period_Lambda

float

1.17549E-38

3.40282E+38

-1

The scale value for the delay period, in days, when Delay_Period_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "WEIBULL_DISTRIBUTION",
    "Delay_Period_Kappa": 0.9,
    "Delay_Period_Lambda": 1.5
}

Delay_Period_Log_Normal_Mu

float

-3.40282e+38

1.70141e+38

3.40282e+38

The mean of the natural log of the delay period, in days, when Delay_Period_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Delay_Period_Log_Normal_Mu": 9,
    "Delay_Period_Log_Normal_Sigma": 2
}

Delay_Period_Log_Normal_Sigma

float

-3.40282e+38

1.70141e+38

3.40282E+38

The standard deviation of the natural log of the delay period, in days, when Delay_Period_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Delay_Period_Log_Normal_Mu": 9,
    "Delay_Period_Log_Normal_Sigma": 2
}

Delay_Period_Max

float

0

3.40282E+38

-1

The maximum delay period, in days, when Delay_Period_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "UNIFORM_DISTRIBUTION",
    "Delay_Period_Min": 2,
    "Delay_Period_Max": 7
}

Delay_Period_Mean_1

float

1.17549E-38

3.40282E+38

-1

The mean of the first exponential distribution, in days, when Delay_Period_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Delay_Period_Mean_1": 4,
    "Delay_Period_Mean_2": 12,
    "Delay_Period_Proportion_1": 0.2
}

Delay_Period_Mean_2

float

1.17549E-38

3.40282E+38

-1

The mean of the second exponential distribution, in days, when Delay_Period_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Delay_Period_Mean_1": 4,
    "Delay_Period_Mean_2": 12,
    "Delay_Period_Proportion_1": 0.2
}

Delay_Period_Min

float

0

3.40282E+38

-1

The minimum delay period, in days, when Delay_Period_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "UNIFORM_DISTRIBUTION",
    "Delay_Period_Min": 2,
    "Delay_Period_Max": 7
}

Delay_Period_Peak_2_Value

float

0

3.40282E+38

-1

The delay period value, in days, to assign to the remaining interventions when Delay_Period_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Delay_Period_Proportion_0": 0.25,
    "Delay_Period_Peak_2_Value": 5
}

Delay_Period_Poisson_Mean

float

0

3.40282E+38

-1

The mean of the delay period, in days, when Delay_Period_Distribution is set to POISSON_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "POISSON_DISTRIBUTION",
    "Delay_Period_Poisson_Mean": 5
}

Delay_Period_Proportion_0

float

0

1

-1

The proportion of interventions to assign a value of zero delay when Delay_Period_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Delay_Period_Proportion_0": 0.25,
    "Delay_Period_Peak_2_Value": 5
}

Delay_Period_Proportion_1

float

0

1

-1

The proportion of interventions in the first exponential distribution when Delay_Period_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Delay_Period_Mean_1": 4,
    "Delay_Period_Mean_2": 12,
    "Delay_Period_Proportion_1": 0.2
}

Duration

float

-1

3.40282e+38

-1

The number of days from when the delay event coordinator was created by the campaign event. Once the number of days has passed, the delay event coordinator will unregister for events and expire. The default value of ‘-1’ = never expire.

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "DelayEventCoordinator_10",
        "Delay_Complete_Event": "Completion_Delayed_Coordinator_Event_1",
        "Delay_Period_Distribution": "FIXED_DURATION",
        "Delay_Period_Fixed": 25,
        "Duration": 100,
        "Start_Trigger_Condition_List": [
            "Coordinator_Event_1"
        ],
        "Stop_Trigger_Condition_List": [],
        "class": "DelayEventCoordinator"
    },
    "Event_Name": "Delay",
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "Target_Demographic": "Everyone",
    "class": "CampaignEvent"
}

Start_Trigger_Condition_List

array of strings

NA

NA

NA

The start trigger event condition list contains events defined in the Custom_Coordinator_Events config parameter that will trigger to start a new delay. If the delay event coordinator has already been triggered and is currently waiting for the duration of a delay, it will then ignore a new delay event. The list cannot be empty.

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "DelayEventCoordinator_10",
        "Delay_Complete_Event": "Completion_Delayed_Coordinator_Event_1",
        "Delay_Period_Distribution": "FIXED_DURATION",
        "Delay_Period_Fixed": 25,
        "Duration": 100,
        "Start_Trigger_Condition_List": [
            "Coordinator_Event_1"
        ],
        "Stop_Trigger_Condition_List": [],
        "class": "DelayEventCoordinator"
    },
    "Event_Name": "Delay",
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "Target_Demographic": "Everyone",
    "class": "CampaignEvent",
    "comment": "Delay"
}

Stop_Trigger_Condition_List

array of strings

NA

NA

NA

The stop trigger event condition list contains events defined in the Custom_Coordinator_Events config parameter that will trigger to stop a delay period, but does not stop the delay event coordinator. The event is not broadcast.

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "DelayEventCoordinator_10",
        "Delay_Complete_Event": "Completion_Delayed_Coordinator_Event_1",
        "Delay_Period_Distribution": "FIXED_DURATION",
        "Delay_Period_Fixed": 25,
        "Duration": 100,
        "Start_Trigger_Condition_List": [
            "Coordinator_Event_1"
        ],
        "Stop_Trigger_Condition_List": [],
        "class": "DelayEventCoordinator"
    },
    "Event_Name": "Delay",
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "Target_Demographic": "Everyone",
    "class": "CampaignEvent",
    "comment": "Delay"
}
SurveillanceEventCoordinator (generic)

The SurveillanceEventCoordinator coordinator class listens for and detects events happening and then responds with broadcasted events when a threshold has been met. This campaign event is typically used with other classes, such as BroadcastCoordinatorEvent, TriggeredEventCoordinator, and DelayEventCoordinator.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Action_List

array of JSON objects

NA

NA

NA

A list of possible actions to take if a particular threshold is met. An action is taken when the specified threshold value is less than the number of incidence counted. If there are multiple actions listed then the action with the highest threshold value, that is also less than the number of incidence counted, is selected. The list cannot be empty.

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "ACF_Counter",
        "Duration": 30,
        "Incidence_Counter": {
            "Counter_Event_Type": "NODE",
            "Counter_Period": 14,
            "Counter_Type": "PERIODIC",
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Trigger_Condition_List": [
                "Node_Event_1",
                "Node_Event_2"
            ]
        },
        "Responder": {
            "Action_List": [
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_2",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 5
                },
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_4",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 2
                }
            ],
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "COUNT"
        },
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [
            "Start_SIA_2",
            "Start_SIA_4"
        ],
        "class": "SurveillanceEventCoordinator"
    },
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "class": "CampaignEvent"
}

Coordinator_Name

string

NA

NA

TriggeredEventCoordinator

The unique identifying coordinator name, which is useful with the output report, ReportSurveillanceEventRecorder.csv.

 
{
    "class": "CampaignEvent",
    "Start_Day": 1,
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Event_Coordinator_Config": {
        "class": "SurveillanceEventCoordinator",
        "Coordinator_Name": "ACF_Counter",
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [
            "Start_SIA_2",
            "Start_SIA_4"
        ],
        "Duration": 30,
        "Incidence_Counter": {
            "Counter_Type": "PERIODIC",
            "Counter_Period": 14,
            "Counter_Event_Type": "NODE",
            "Trigger_Condition_List": [
                "Node_Event_1",
                "Node_Event_2"
            ],
            "Target_Demographic": "Everyone",
            "Demographic_Coverage": 1
        },
        "Responder": {
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "COUNT",
            "Action_List": [
                {
                    "Threshold": 5,
                    "Event_Type": "COORDINATOR",
                    "Event_To_Broadcast": "Ind_Start_SIA_2"
                },
                {
                    "Threshold": 2,
                    "Event_Type": "COORDINATOR",
                    "Event_To_Broadcast": "Ind_Start_SIA_4"
                }
            ]
        }
    }
}

Counter_Event_Type

enum

NA

NA

INDIVIDUAL

Type of events that can be included in Trigger_Condition_List. Possible values are:

  • COORDINATOR

  • INDIVIDUAL

  • NODE

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "ACF_Counter",
        "Duration": 30,
        "Incidence_Counter": {
            "Counter_Event_Type": "NODE",
            "Counter_Period": 14,
            "Counter_Type": "PERIODIC",
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Trigger_Condition_List": [
                "Node_Event_1",
                "Node_Event_2"
            ]
        },
        "Responder": {
            "Action_List": [
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_2",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 5
                },
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_4",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 2
                }
            ],
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "COUNT"
        },
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [
            "Start_SIA_2",
            "Start_SIA_4"
        ],
        "class": "SurveillanceEventCoordinator"
    },
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "class": "CampaignEvent"
}

Counter_Period

float

1

10000

1

When Counter_Type is set to PERIODIC, this is the counter period (in days).

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "ACF_Counter",
        "Duration": 30,
        "Incidence_Counter": {
            "Counter_Event_Type": "NODE",
            "Counter_Period": 14,
            "Counter_Type": "PERIODIC",
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Trigger_Condition_List": [
                "Node_Event_1",
                "Node_Event_2"
            ]
        },
        "Responder": {
            "Action_List": [
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_2",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 5
                },
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_4",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 2
                }
            ],
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "COUNT"
        },
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [
            "Start_SIA_2",
            "Start_SIA_4"
        ],
        "class": "SurveillanceEventCoordinator"
    },
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "class": "CampaignEvent"
}

Counter_Type

enum

NA

NA

PERIODIC

Counter type used for surveillance of events. The counter is triggered by events in Start_Trigger_Condition_List and the counter stops when it receives an event in Stop_Trigger_Condition_List or the listening duration expires. Possible values are:

PERIODIC

Once triggered, events are counted during the period (in days) as set in Counter_Period. At the end of the period, the counter will notify Responder with the data accumulated and then start listening again, continuing forever until it is stopped or the duration expires. For example, if the listening duration is 45 days, Counter_Period is 30, and the counter is triggered on day 20, it will never complete the counter period and trigger the responder.

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "ACF_Counter",
        "Duration": 30,
        "Incidence_Counter": {
            "Counter_Event_Type": "NODE",
            "Counter_Period": 14,
            "Counter_Type": "PERIODIC",
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Trigger_Condition_List": [
                "Node_Event_1",
                "Node_Event_2"
            ]
        },
        "Responder": {
            "Action_List": [
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_2",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 5
                },
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_4",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 2
                }
            ],
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "COUNT"
        },
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [
            "Start_SIA_2",
            "Start_SIA_4"
        ],
        "class": "SurveillanceEventCoordinator"
    },
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "class": "CampaignEvent"
}

Demographic_Coverage

float

0

1

1

The fraction of individuals in the target demographic that are counted.

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "ACF_Counter",
        "Duration": 30,
        "Incidence_Counter": {
            "Counter_Event_Type": "NODE",
            "Counter_Period": 14,
            "Counter_Type": "PERIODIC",
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Trigger_Condition_List": [
                "Node_Event_1",
                "Node_Event_2"
            ]
        },
        "Responder": {
            "Action_List": [
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_2",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 5
                },
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_4",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 2
                }
            ],
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "COUNT"
        },
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [
            "Start_SIA_2",
            "Start_SIA_4"
        ],
        "class": "SurveillanceEventCoordinator"
    },
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "class": "CampaignEvent"
}

Duration

float

-1

3.40282e+38

-1

The number of days from when the surveillance event coordinator was created by the campaign event. Once the number of days has passed, the delay event coordinator will unregister for events and expire. The default value of ‘-1’ = never expire.

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "ACF_Counter",
        "Duration": 30,
        "Incidence_Counter": {
            "Counter_Event_Type": "NODE",
            "Counter_Period": 14,
            "Counter_Type": "PERIODIC",
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Trigger_Condition_List": [
                "Node_Event_1",
                "Node_Event_2"
            ]
        },
        "Responder": {
            "Action_List": [
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_2",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 5
                },
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_4",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 2
                }
            ],
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "COUNT"
        },
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [
            "Start_SIA_2",
            "Start_SIA_4"
        ],
        "class": "SurveillanceEventCoordinator"
    },
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "class": "CampaignEvent"
}

Event_Type

enum

NA

NA

INDIVIDUAL

The type of event to be broadcast. Possible values are:

  • COORDINATOR

  • INDIVIDUAL

  • NODE

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "ACF_Counter",
        "Duration": 30,
        "Incidence_Counter": {
            "Counter_Event_Type": "NODE",
            "Counter_Period": 14,
            "Counter_Type": "PERIODIC",
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Trigger_Condition_List": [
                "Node_Event_1",
                "Node_Event_2"
            ]
        },
        "Responder": {
            "Action_List": [
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_2",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 5
                },
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_4",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 2
                }
            ],
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "COUNT"
        },
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [
            "Start_SIA_2",
            "Start_SIA_4"
        ],
        "class": "SurveillanceEventCoordinator"
    },
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "class": "CampaignEvent"
}

Incidence_Counter

array of JSON objects

NA

NA

NA

List of JSON objects for specifying the conditions and parameters that must be met for an incidence to be counted. Some of the values are specified in the following parameters: Counter_Type, Counter_Period, Counter_Event_Type, Trigger_Condition_List, Node_Property_Restrictions, Property_Restrictions_Within_Node.

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "ACF_Counter",
        "Duration": 30,
        "Incidence_Counter": {
            "Counter_Event_Type": "NODE",
            "Counter_Period": 14,
            "Counter_Type": "PERIODIC",
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Trigger_Condition_List": [
                "Node_Event_1",
                "Node_Event_2"
            ]
        },
        "Responder": {
            "Action_List": [
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_2",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 5
                },
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_4",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 2
                }
            ],
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "COUNT"
        },
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [
            "Start_SIA_2",
            "Start_SIA_4"
        ],
        "class": "SurveillanceEventCoordinator"
    },
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "class": "CampaignEvent"
}

Node_Property_Restrictions

array of JSON objects

NA

NA

NA

A list of the NodeProperty key:value pairs, as defined in the demographics file, that nodes must have to be targeted by the intervention. See NodeProperties and IndividualProperties parameters for more information.

You can specify AND and OR combinations of key:value pairs with this parameter.

The following example restricts the intervention to nodes that are urban and medium risk or rural and low risk.

{
    "Node_Property_Restrictions": [
        {
            "Place": "URBAN",
            "Risk": "MED"
        },
        {
            "Place": "RURAL",
            "Risk": "LOW"
        }
    ]
}

Percentage_Events_To_Count

array of strings

NA

NA

NA

When Threshold_Type equals PERCENTAGE_EVENTS then Percentage_Events_To_Count lists the events that will be counted for the denominator which will then be used with the specified event for Trigger_Condition_List counted for the numerator.

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "ACF_Counter",
        "Duration": -1,
        "Incidence_Counter": {
            "Counter_Event_Type": "NODE",
            "Counter_Period": 30,
            "Counter_Type": "PERIODIC",
            "Demographic_Coverage": 1,
            "Node_Property_Restrictions": [],
            "Property_Restrictions_Within_Node": [],
            "Target_Demographic": "Everyone",
            "Trigger_Condition_List": [
                "Positive_Event_Node"
            ]
        },
        "Responder": {
            "Action_List": [
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_2",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 0.5
                }
            ],
            "Percentage_Events_To_Count": [
                "Negative_Event_Node",
                "Positive_Event_Node"
            ],
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "PERCENTAGE_EVENTS"
        },
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [],
        "class": "SurveillanceEventCoordinator"
    },
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "class": "CampaignEvent"
}

Property_Restrictions

array of JSON objects

NA

NA

NA

A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information.

To specify AND and OR combinations of key:value pairs, use Property_Restrictions_Within_Node. You cannot use both of these parameters in the same event coordinator.

 
{
    "Property_Restrictions": [
        "Risk:HIGH"
    ]
}

Property_Restrictions_Within_Node

array of JSON objects

NA

NA

NA

A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information.

This parameter allows you to specify AND and OR combinations of key:value pairs. You may specify individual property restrictions using either this parameter or Property_Restrictions, but not both.

The following example restricts the intervention to individuals who are urban and high risk or urban and medium risk.

{
    "Property_Restrictions_Within_Node": [
        {
            "Geographic": "URBAN",
            "Risk": "HIGH"
        },
        {
            "Geographic": "URBAN",
            "Risk": "MEDIUM"
        }
    ]
}

Responded_Event

string

NA

NA

“”

A coordinator event, defined in Custom_Coordinator_Events, that is broadcast if Responder responded. More specifically, at the completion of a counting period, if an action is selected, the action events are broadcast and then the Responded_Event is also broadcast. This allows other event coordinators to react to the action events being broadcast.

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "ACF_Counter",
        "Duration": 30,
        "Incidence_Counter": {
            "Counter_Event_Type": "NODE",
            "Counter_Period": 14,
            "Counter_Type": "PERIODIC",
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Trigger_Condition_List": [
                "Node_Event_1",
                "Node_Event_2"
            ]
        },
        "Responder": {
            "Action_List": [
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_2",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 5
                },
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_4",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 2
                }
            ],
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "COUNT"
        },
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [
            "Start_SIA_2",
            "Start_SIA_4"
        ],
        "class": "SurveillanceEventCoordinator"
    },
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "class": "CampaignEvent"
}

Responder

array of JSON objects

NA

NA

NA

List of JSON objects for specifying the threshold type, values, and the actions to take when the specified conditions and parameters have been met, as configured in Incidence_Counter. Some of the values are specified in the following parameters:

  • Action_List

  • Event_To_Broadcast

  • Threshold_Type

  • Threshold

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "ACF_Counter",
        "Duration": 30,
        "Incidence_Counter": {
            "Counter_Event_Type": "NODE",
            "Counter_Period": 14,
            "Counter_Type": "PERIODIC",
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Trigger_Condition_List": [
                "Node_Event_1",
                "Node_Event_2"
            ]
        },
        "Responder": {
            "Action_List": [
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_2",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 5
                },
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_4",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 2
                }
            ],
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "COUNT"
        },
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [
            "Start_SIA_2",
            "Start_SIA_4"
        ],
        "class": "SurveillanceEventCoordinator"
    },
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "class": "CampaignEvent"
}

Start_Trigger_Condition_List

array of strings

NA

NA

NA

The trigger event list, as specified in the Custom_Coordinator_Events config parameter, that will start Incidence_Counter counting events. The surveillance event coordinator will keep counting and responding until it gets a stop event, as defined in Stop_Trigger_Condition_List, or the duration of the surveillance event coordinator has expired, as defined in Duration. The list cannot be empty.

 
{
    "class": "CampaignEvent",
    "Start_Day": 1,
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Event_Coordinator_Config": {
        "class": "SurveillanceEventCoordinator",
        "Coordinator_Name": "ACF_Counter",
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [
            "Start_SIA_2",
            "Start_SIA_4"
        ],
        "Duration": 30,
        "Incidence_Counter": {
            "Counter_Type": "PERIODIC",
            "Counter_Period": 14,
            "Counter_Event_Type": "NODE",
            "Trigger_Condition_List": [
                "Node_Event_1",
                "Node_Event_2"
            ],
            "Target_Demographic": "Everyone",
            "Demographic_Coverage": 1
        },
        "Responder": {
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "COUNT",
            "Action_List": [
                {
                    "Threshold": 5,
                    "Event_Type": "COORDINATOR",
                    "Event_To_Broadcast": "Ind_Start_SIA_2"
                },
                {
                    "Threshold": 2,
                    "Event_Type": "COORDINATOR",
                    "Event_To_Broadcast": "Ind_Start_SIA_4"
                }
            ]
        }
    }
}

Stop_Trigger_Condition_List

array of strings

NA

NA

NA

The broadcast event list, as specified in the Custom_Coordinator_Events config parameter, that will stop Incidence_Counter counting events. The coordinator can start counting again if it receives a start trigger event. The list can be empty.

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "ACF_Counter",
        "Duration": 30,
        "Incidence_Counter": {
            "Counter_Event_Type": "NODE",
            "Counter_Period": 14,
            "Counter_Type": "PERIODIC",
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Trigger_Condition_List": [
                "Node_Event_1",
                "Node_Event_2"
            ]
        },
        "Responder": {
            "Action_List": [
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_2",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 5
                },
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_4",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 2
                }
            ],
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "COUNT"
        },
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [
            "Start_SIA_2",
            "Start_SIA_4"
        ],
        "class": "SurveillanceEventCoordinator"
    },
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "class": "CampaignEvent"
}

Target_Age_Max

float

0

9.3228e+35

9.3228e+35

The upper end of ages targeted for an intervention, in years. Used when Target_Demographic is set to ExplicitAgeRanges or ExplicitAgeRangesAndGender.

 
{
    "Target_Age_Max": 20,
    "Target_Age_Min": 10,
    "Target_Demographic": "ExplicitAgeRanges"
}

Target_Age_Min

float

0

9.3228e+35

0

The lower end of ages targeted for an intervention, in years. Used when Target_Demographic is set to ExplicitAgeRanges or ExplicitAgeRangesAndGender.

 
{
    "Target_Age_Max": 20,
    "Target_Age_Min": 10,
    "Target_Demographic": "ExplicitAgeRanges"
}

Target_Demographic

enum

NA

NA

Everyone

The target demographic group. Possible values are:

  • Everyone

  • ExplicitAgeRanges

  • ExplicitAgeRangesAndGender

  • ExplicitGender

  • ExplicitDiseaseState

 
{
    "Target_Age_Max": 20,
    "Target_Age_Min": 10,
    "Target_Demographic": "ExplicitAgeRanges"
}

Target_Gender

enum

NA

NA

All

Specifies the gender restriction for the intervention. Possible values are:

  • Male

  • Female

  • All

 
{
    "Target_Gender": "Female"
}

Target_Residents_Only

boolean

0

1

0

When set to true (1), only individuals that currently reside in their ‘home’ node will be counted.

 
{
    "Target_Residents_Only": 1
}

Threshold

float

0

3.40282e+38

0

The COUNT or PERCENTAGE, as configured with Threshold_Type, threshold value that must be met before and action from Action_List will be considered.

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "ACF_Counter",
        "Duration": 30,
        "Incidence_Counter": {
            "Counter_Event_Type": "NODE",
            "Counter_Period": 14,
            "Counter_Type": "PERIODIC",
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Trigger_Condition_List": [
                "Node_Event_1",
                "Node_Event_2"
            ]
        },
        "Responder": {
            "Action_List": [
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_2",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 5
                },
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_4",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 2
                }
            ],
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "COUNT"
        },
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [
            "Start_SIA_2",
            "Start_SIA_4"
        ],
        "class": "SurveillanceEventCoordinator"
    },
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "class": "CampaignEvent"
}

Threshold_Type

enum

NA

NA

COUNT

Threshold type to indicate how Responder handles the count of events from Incidence_Counter and the thresholds in Action_List. Possible values are:

COUNT

A raw count of events. Also, with COUNT, setting the x_Base_Population configuration parameter can affect the number count by changing the population but it could be very indirect.

PERCENTAGE

Counts the number of individuals that meet the restrictions and then to divide the total number of events by this number. Note that it is possible for an individual to emit an event that might not be counted in the denominator if their demographic restriction attributes changed between the time of the emitted event and the time the denominator was counted.

PERCENTAGE_EVENTS

Percentage_Events_To_Count lists the events that will be counted for the denominator, which will then be used with the specified event for Trigger_Condition_List to be counted for the numerator.

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "ACF_Counter",
        "Duration": 30,
        "Incidence_Counter": {
            "Counter_Event_Type": "NODE",
            "Counter_Period": 14,
            "Counter_Type": "PERIODIC",
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Trigger_Condition_List": [
                "Node_Event_1",
                "Node_Event_2"
            ]
        },
        "Responder": {
            "Action_List": [
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_2",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 5
                },
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_4",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 2
                }
            ],
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "COUNT"
        },
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [
            "Start_SIA_2",
            "Start_SIA_4"
        ],
        "class": "SurveillanceEventCoordinator"
    },
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "class": "CampaignEvent"
}

Trigger_Condition_List

array of strings

NA

NA

NA

The list of events to count.The list cannot be empty. The type of events contained in the list is determined by Counter_Event_Type. Depending upon the type, the events must be defined in one of the following configuration parameters:

  • Custom_Coordinator_Events

  • Custom_Individual_Events

  • Custom_Node_Events

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "ACF_Counter",
        "Duration": 30,
        "Incidence_Counter": {
            "Counter_Event_Type": "NODE",
            "Counter_Period": 14,
            "Counter_Type": "PERIODIC",
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Trigger_Condition_List": [
                "Node_Event_1",
                "Node_Event_2"
            ]
        },
        "Responder": {
            "Action_List": [
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_2",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 5
                },
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_4",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 2
                }
            ],
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "COUNT"
        },
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [
            "Start_SIA_2",
            "Start_SIA_4"
        ],
        "class": "SurveillanceEventCoordinator"
    },
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "class": "CampaignEvent"
}

Targeting_Config

JSON object

NA

NA

NA

Be more selective of individuals by using the Targeting_Config classes.

 
{
    "Targeting_Config": {
        "class": "HasIntervention",
        "Is_Equal_To": 0,
        "Intervention_Name": "MyVaccine"
    }
}
TriggeredEventCoordinator (generic)

The TriggeredEventCoordinator coordinator class listens for trigger events, begins a series of repetitions of intervention distributions, and then broadcasts an event upon completion. This campaign event is typically used with other classes that broadcast and distribute events, such as BroadcastCoordinatorEvent, DelayEventCoordinator, and SurveillanceEventCoordinator.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Completion_Event

array of strings

NA

NA

NA

The completion event list that will be broadcast every time the triggered event coordinator completes a set of repetitions. The events contained in the list are defined in Custom_Coordinator_Events in the simulation configuration file.

 
{
    "Event_Coordinator_Config": {
        "class": "TriggeredEventCoordinator",
        "Coordinator_Name": "1n2_Vaccinator",
        "Start_Trigger_Condition_List": [
            "Start_Vaccinating_1n2"
        ],
        "Stop_Trigger_Condition_List": [],
        "Number_Repetitions": 1,
        "Timesteps_Between_Repetitions": 1,
        "Duration": -1,
        "Target_Demographic": "Everyone",
        "Demographic_Coverage": 0.05,
        "Intervention_Config": {
            "class": "SimpleVaccine",
            "Cost_To_Consumer": 1,
            "Vaccine_Take": 1,
            "Vaccine_Type": "AcquisitionBlocking",
            "Waning_Config": {
                "class": "WaningEffectBox",
                "Initial_Effect": 0.59,
                "Box_Duration": 730
            }
        },
        "Completion_Event": "Done_Vaccinating_1n2"
    }
}

Coordinator_Name

string

NA

NA

TriggeredEventCoordinator

The unique identifying coordinator name used to identify the different coordinators in reports.

 
{
    "Event_Coordinator_Config": {
        "Completion_Event": "Done_Vaccinating_1n2",
        "Coordinator_Name": "1n2_Vaccinator",
        "Demographic_Coverage": 0.05,
        "Duration": -1,
        "Intervention_Config": {
            "Cost_To_Consumer": 1,
            "Vaccine_Take": 1,
            "Vaccine_Type": "AcquisitionBlocking",
            "Waning_Config": {
                "Box_Duration": 730,
                "Initial_Effect": 0.59,
                "class": "WaningEffectBox"
            },
            "class": "SimpleVaccine"
        },
        "Number_Repetitions": 1,
        "Start_Trigger_Condition_List": [
            "Start_Vaccinating_1n2"
        ],
        "Stop_Trigger_Condition_List": [],
        "Target_Demographic": "Everyone",
        "Timesteps_Between_Repetitions": 1,
        "class": "TriggeredEventCoordinator"
    }
}

Duration

float

-1

3.40282e+38

-1

The time period (in days) that the triggered event coordinator is active before it expires. Once the specified duration has been reached the coordinator will expire whether or not it is in the middle of a set of repetitions. The value of -1 (default) equals to never expire.

 
{
    "Event_Coordinator_Config": {
        "Completion_Event": "Done_Vaccinating_1n2",
        "Coordinator_Name": "1n2_Vaccinator",
        "Demographic_Coverage": 0.05,
        "Duration": -1,
        "Intervention_Config": {
            "Cost_To_Consumer": 1,
            "Vaccine_Take": 1,
            "Vaccine_Type": "AcquisitionBlocking",
            "Waning_Config": {
                "Box_Duration": 730,
                "Initial_Effect": 0.59,
                "class": "WaningEffectBox"
            },
            "class": "SimpleVaccine"
        },
        "Number_Repetitions": 1,
        "Start_Trigger_Condition_List": [
            "Start_Vaccinating_1n2"
        ],
        "Stop_Trigger_Condition_List": [],
        "Target_Demographic": "Everyone",
        "Timesteps_Between_Repetitions": 1,
        "class": "TriggeredEventCoordinator"
    }
}

Intervention_Config

JSON object

NA

NA

NA

The nested JSON of the intervention to be distributed by this event coordinator.

 
{
    "Event_Coordinator_Config": {
        "Completion_Event": "Done_Vaccinating_1n2",
        "Coordinator_Name": "1n2_Vaccinator",
        "Demographic_Coverage": 0.05,
        "Duration": -1,
        "Intervention_Config": {
            "Cost_To_Consumer": 1,
            "Vaccine_Take": 1,
            "Vaccine_Type": "AcquisitionBlocking",
            "Waning_Config": {
                "Box_Duration": 730,
                "Initial_Effect": 0.59,
                "class": "WaningEffectBox"
            },
            "class": "SimpleVaccine"
        },
        "Number_Repetitions": 1,
        "Start_Trigger_Condition_List": [
            "Start_Vaccinating_1n2"
        ],
        "Stop_Trigger_Condition_List": [],
        "Target_Demographic": "Everyone",
        "Timesteps_Between_Repetitions": 1,
        "class": "TriggeredEventCoordinator"
    }
}

Node_Property_Restrictions

array of JSON objects

NA

NA

NA

A list of the NodeProperty key:value pairs, as defined in the demographics file, that nodes must have to be targeted by the intervention. See NodeProperties and IndividualProperties parameters for more information. You can specify AND and OR combinations of key:value pairs with this parameter.

The following configuration restrictions the intervention to nodes that are urban and high risk or have had the first round treatment and are low risk.

{
    "Event_Coordinator_Config": {
        "Completion_Event": "Done_Vaccinating_3n4",
        "Coordinator_Name": "3n4_Vaccinator",
        "Demographic_Coverage": 0.05,
        "Duration": -1,
        "Intervention_Config": {
            "Cost_To_Consumer": 2,
            "Vaccine_Take": 1,
            "Vaccine_Type": "AcquisitionBlocking",
            "Waning_Config": {
                "Box_Duration": 730,
                "Initial_Effect": 0.59,
                "class": "WaningEffectBox"
            },
            "class": "SimpleVaccine"
        },
        "Node_Property_Restrictions": [
            {
                "Place": "Urban",
                "Risk": "High"
            },
            {
                "InterventionStatus": "FirstRound",
                "Risk": "Low"
            }
        ],
        "Number_Repetitions": 3,
        "Property_Restrictions_Within_Node": [],
        "Start_Trigger_Condition_List": [
            "Start_Vaccinating_3n4"
        ],
        "Stop_Trigger_Condition_List": [
            "Stop_Vaccinating_3n4"
        ],
        "Target_Demographic": "Everyone",
        "Timesteps_Between_Repetitions": 10,
        "class": "TriggeredEventCoordinator"
    }
}

Number_Repetitions

integer

-1

10000

1

The number of times an intervention is given, used with Timesteps_Between_Repetitions. A value of -1 implies an infinite number of repetitions.

 
{
    "Event_Coordinator_Config": {
        "Completion_Event": "Done_Vaccinating_3n4",
        "Coordinator_Name": "3n4_Vaccinator",
        "Demographic_Coverage": 0.05,
        "Duration": -1,
        "Intervention_Config": {
            "Cost_To_Consumer": 2,
            "Vaccine_Take": 1,
            "Vaccine_Type": "AcquisitionBlocking",
            "Waning_Config": {
                "Box_Duration": 730,
                "Initial_Effect": 0.59,
                "class": "WaningEffectBox"
            },
            "class": "SimpleVaccine"
        },
        "Number_Repetitions": 3,
        "Start_Trigger_Condition_List": [
            "Start_Vaccinating_3n4"
        ],
        "Stop_Trigger_Condition_List": [
            "Stop_Vaccinating_3n4"
        ],
        "Target_Demographic": "Everyone",
        "Timesteps_Between_Repetitions": 10,
        "class": "TriggeredEventCoordinator"
    }
}

Property_Restrictions

array of JSON objects

NA

NA

NA

A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information.

To specify AND and OR combinations of key:value pairs, use Property_Restrictions_Within_Node. You cannot use both of these parameters in the same event coordinator.

 
{
    "Property_Restrictions": [
        "Risk:HIGH"
    ]
}

Property_Restrictions_Within_Node

array of JSON objects

NA

NA

NA

A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information.

This parameter allows you to specify AND and OR combinations of key:value pairs. You may specify individual property restrictions using either this parameter or Property_Restrictions, but not both.

The following example restricts the intervention to individuals who are urban and high risk or urban and medium risk.

{
    "Property_Restrictions_Within_Node": [
        {
            "Geographic": "URBAN",
            "Risk": "HIGH"
        },
        {
            "Geographic": "URBAN",
            "Risk": "MEDIUM"
        }
    ]
}

Start_Trigger_Condition_List

array of strings

NA

NA

NA

The trigger condition event list that when heard will start a new set of repetitions for the triggered event coordinator. The list cannot be empty. The events contained in the list are defined in Custom_Coordinator_Events in the simulation configuration file.

 
{
    "Event_Coordinator_Config": {
        "Completion_Event": "Done_Vaccinating_1n2",
        "Coordinator_Name": "1n2_Vaccinator",
        "Demographic_Coverage": 0.05,
        "Duration": -1,
        "Intervention_Config": {
            "Cost_To_Consumer": 1,
            "Vaccine_Take": 1,
            "Vaccine_Type": "AcquisitionBlocking",
            "Waning_Config": {
                "Box_Duration": 730,
                "Initial_Effect": 0.59,
                "class": "WaningEffectBox"
            },
            "class": "SimpleVaccine"
        },
        "Number_Repetitions": 1,
        "Start_Trigger_Condition_List": [
            "Start_Vaccinating_1n2"
        ],
        "Stop_Trigger_Condition_List": [],
        "Target_Demographic": "Everyone",
        "Timesteps_Between_Repetitions": 1,
        "class": "TriggeredEventCoordinator"
    }
}

Stop_Trigger_Condition_List

array of strings

NA

NA

NA

The trigger condition event list that when heard will stop any repetitions for the triggered event coordinator until a start trigger condition event list is received. The list can be empty. The events contained in the list are defined in Custom_Coordinator_Events in the simulation configuration file.

 
{
    "Event_Coordinator_Config": {
        "Completion_Event": "Done_Vaccinating_1n2",
        "Coordinator_Name": "1n2_Vaccinator",
        "Demographic_Coverage": 0.05,
        "Duration": -1,
        "Intervention_Config": {
            "Cost_To_Consumer": 1,
            "Vaccine_Take": 1,
            "Vaccine_Type": "AcquisitionBlocking",
            "Waning_Config": {
                "Box_Duration": 730,
                "Initial_Effect": 0.59,
                "class": "WaningEffectBox"
            },
            "class": "SimpleVaccine"
        },
        "Number_Repetitions": 1,
        "Start_Trigger_Condition_List": [
            "Start_Vaccinating_1n2"
        ],
        "Stop_Trigger_Condition_List": [],
        "Target_Demographic": "Everyone",
        "Timesteps_Between_Repetitions": 1,
        "class": "TriggeredEventCoordinator"
    }
}

Target_Age_Max

float

0

9.3228e+35

9.3228e+35

The upper end of ages targeted for an intervention, in years. Used when Target_Demographic is set to ExplicitAgeRanges or ExplicitAgeRangesAndGender.

 
{
    "Target_Age_Max": 20,
    "Target_Age_Min": 10,
    "Target_Demographic": "ExplicitAgeRanges"
}

Target_Age_Min

float

0

9.3228e+35

0

The lower end of ages targeted for an intervention, in years. Used when Target_Demographic is set to ExplicitAgeRanges or ExplicitAgeRangesAndGender.

 
{
    "Target_Age_Max": 20,
    "Target_Age_Min": 10,
    "Target_Demographic": "ExplicitAgeRanges"
}

Target_Demographic

enum

NA

NA

Everyone

The target demographic group. Possible values are:

  • Everyone

  • ExplicitAgeRanges

  • ExplicitAgeRangesAndGender

  • ExplicitGender

  • ExplicitDiseaseState

 
{
    "Target_Age_Max": 20,
    "Target_Age_Min": 10,
    "Target_Demographic": "ExplicitAgeRanges"
}

Target_Gender

enum

NA

NA

All

Specifies the gender restriction for the intervention. Possible values are:

  • Male

  • Female

  • All

 
{
    "Target_Gender": "Female"
}

Target_Residents_Only

boolean

0

1

0

When set to true (1), the intervention is only distributed to individuals that began the simulation in the node (i.e. those that claim the node as their residence).

 
{
    "Target_Residents_Only": 1
}

Timesteps_Between_Repetitions

integer

-1

10000

-1

The repetition interval.

 
{
    "Event_Coordinator_Config": {
        "Completion_Event": "Done_Vaccinating_1n2",
        "Coordinator_Name": "1n2_Vaccinator",
        "Demographic_Coverage": 0.05,
        "Duration": -1,
        "Intervention_Config": {
            "Cost_To_Consumer": 1,
            "Vaccine_Take": 1,
            "Vaccine_Type": "AcquisitionBlocking",
            "Waning_Config": {
                "Box_Duration": 730,
                "Initial_Effect": 0.59,
                "class": "WaningEffectBox"
            },
            "class": "SimpleVaccine"
        },
        "Number_Repetitions": 1,
        "Start_Trigger_Condition_List": [
            "Start_Vaccinating_1n2"
        ],
        "Stop_Trigger_Condition_List": [],
        "Target_Demographic": "Everyone",
        "Timesteps_Between_Repetitions": 1,
        "class": "TriggeredEventCoordinator"
    }
}

Targeting_Config

JSON object

NA

NA

NA

Be more selective of individuals by using the Targeting_Config classes.

 
{
    "Targeting_Config": {
        "class": "HasIntervention",
        "Is_Equal_To": 0,
        "Intervention_Name": "MyVaccine"
    }
}
Deprecated configuration parameters

Base_Population_Scale_Factor has been renamed to x_Base_Population, which is grouped together with the other scale factor parameters beginning with x_. The functional remains the same. Enable_Demographics_Gender has been deprecated. Animal_Reservoir_Type has been replaced with Enable_Infectivity_Reservoir.

The following configuration parameters have been deprecated as a result of the refactoring of distribution parameters for better consistency across the configuration and campaign files.

Parameter

Incubation_Period_Log_Mean

Incubation_Period_Log_Width

Infectious_Period_Mean

Infectious_Period_Std_Dev
Deprecated campaign parameters

The following campaign parameters have been deprecated as a result of the refactoring of distribution parameters for better consistency across the configuration and campaign files.

Parameter

BitingRisk Constant

BitingRisk Risk_Distribution_Type

BitingRisk Exponential_Mean

BitingRisk Gaussian_Mean

BitingRisk Gaussian_Std_Dev

BitingRisk Uniform_Max

BitingRisk Uniform_Min

CommunityHealthWorkerEventCoordinator Initial_Amount

CommunityHealthWorkerEventCoordinator Initial_Amount_Distribution_Type

CommunityHealthWorkerEventCoordinator Initial_Amount_Mean

CommunityHealthWorkerEventCoordinator Initial_Amount_Std_Dev

DelayedIntervention Delay_Distribution

DelayedIntervention Delay_Period

DelayedIntervention Delay_Period_Mean

DelayedIntervention Delay_Period_Scale

DelayedIntervention Delay_Period_Shape

DelayedIntervention Delay_Period_Std_Dev

DelayEventCoordinator Delay_Distribution

DelayEventCoordinator Delay_Period

DelayEventCoordinator Delay_Period_Mean

DelayEventCoordinator Delay_Period_StdDev

HIVDelayedIntervention Delay_Distribution

HIVDelayedIntervention Delay_Period

HIVDelayedIntervention Delay_Period_Mean

HIVDelayedIntervention Delay_Period_Scale

HIVDelayedIntervention Delay_Period_Shape

HIVDelayedIntervention Delay_Period_Std_Dev

HIVMuxer Delay_Distribution

HIVMuxer Delay_Period

HIVMuxer Delay_Period_Mean

HIVMuxer Delay_Period_Scale

HIVMuxer Delay_Period_Shape

HIVMuxer Delay_Period_Std_Dev

MigrateFamily Duration_At_Node_Distribution_Type

MigrateFamily Duration_At_Node_Exponential_Period

MigrateFamily Duration_At_Node_Fixed

MigrateFamily Duration_At_Node_Gausian_Mean

MigrateFamily Duration_At_Node_Gausian_StdDev

MigrateFamily Duration_At_Node_Uniform_Max

MigrateFamily Duration_At_Node_Uniform_Min

MigrateFamily Duration_Before_Leaving_Distribution_Type

MigrateFamily Duration_Before_Leaving_Exponential_Period

MigrateFamily Duration_Before_Leaving_Fixed

MigrateFamily Duration_Before_Leaving_Gausian_Mean

MigrateFamily Duration_Before_Leaving_Gausian_StdDev

MigrateFamily Duration_Before_Leaving_Uniform_Max

MigrateFamily Duration_Before_Leaving_Uniform_Min

MigrateIndividuals Duration_At_Node_Distribution_Type

MigrateIndividuals Duration_At_Node_Exponential_Period

MigrateIndividuals Duration_At_Node_Fixed

MigrateIndividuals Duration_At_Node_Gausian_Mean

MigrateIndividuals Duration_At_Node_Gausian_StdDev

MigrateIndividuals Duration_Before_Leaving_Distribution_Type

MigrateIndividuals Duration_Before_Leaving_Exponential_Period

MigrateIndividuals Duration_Before_Leaving_Fixed

MigrateIndividuals Duration_Before_Leaving_Gausian_Mean

MigrateIndividuals Duration_Before_Leaving_Gausian_StdDev

MigrateIndividuals Duration_Before_Leaving_Uniform_Max

MigrateIndividuals Duration_Before_Leaving_Uniform_Min

MigrateIndividuals Duration_At_Node_Uniform_Max

MigrateIndividuals Duration_At_Node_Uniform_Min

UsageDependentBednet Expiration_Distribution_Type

UsageDependentBednet Expiration_Percentage_Period_1

UsageDependentBednet Expiration_Period

UsageDependentBednet Expiration_Period_1

UsageDependentBednet Expiration_Period_2

UsageDependentBednet Expiration_Period_Mean

UsageDependentBednet Expiration_Period_Std_Dev

EMOD v2.18

The EMOD v2.18 release includes many new features for all supported simulation types. In particular, the TB_SIM simulation type has been deprecated and replaced with TBHIV_SIM, which does not include HIV transmission but adds the ability to model the effect of HIV coinfection on the spread of TB.

New configuration parameters

For the malaria simulation type, the following new configuration parameters are available:

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Genome_Markers

array of strings

NA

NA

NA

A list of the names (strings) of genome marker(s) that represent the genetic components in a strain of an infection.

 
{
    "Genome_Markers": [
        "D6",
        "W2"
    ]
}

Resistance

JSON object

NA

NA

NA

Specifies the drug resistance multiplier.

 
{
    "parameters": {
        "Genome_Markers": [
            "A",
            "B"
        ]
    },
    "Malaria_Drug_Params": {
        "Name": "Artemether_Lumefantrine",
        "Bodyweight_Exponent": 0,
        "Drug_Cmax": 1000,
        "Drug_Decay_T1": 1,
        "Drug_Decay_T2": 1,
        "Drug_Dose_Interval": 1,
        "Drug_Fulltreatment_Doses": 3,
        "Drug_Gametocyte02_Killrate": 2.3,
        "Drug_Gametocyte34_Killrate": 2.3,
        "Drug_GametocyteM_Killrate": 0,
        "Drug_Hepatocyte_Killrate": 0,
        "Drug_PKPD_C50": 100,
        "Drug_Vd": 10,
        "Max_Drug_IRBC_Kill": 3.45,
        "Resistance": {
            "A": {
                "Max_IRBC_Kill_Modifier": 0.05,
                "PKPD_C50_Modifier": 1.0
            },
            "B": {
                "Max_IRBC_Kill_Modifier": 0.25,
                "PKPD_C50_Modifier": 1.0
            }
        }
    }
}

To view all available configuration parameters, see Configuration parameters.

New campaign parameters

The following campaign classes are new for the Generic model and can be used in all other models:

IncidenceEventCoordinator (generic)

The IncidenceEventCoordinator coordinator class distributes interventions based on the number of events counted over a period of time.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Action_List

array of JSON objects

NA

NA

NA

List (array) of JSON objects, including the values specified in the following parameters: Threshold, Event_Type, Event_To_Broadcast.

 
{
    "Action_List": [
        {
            "Event_To_Broadcast": "Action_Event_1",
            "Event_Type": "COORDINATOR",
            "Threshold": 20
        },
        {
            "Event_To_Broadcast": "Action_Event_2",
            "Event_Type": "COORDINATOR",
            "Threshold": 30
        }
    ],
    "Threshold_Type": "COUNT"
}

Count_Events_For_Num_Timesteps

integer

1

2.15E+00

1

If set to true (1), then the waning effect values, as specified in the Effect_List list of objects for the WaningEffectCombo classes, are added together. If set to false (0), the waning effect values are multiplied. The resulting waning effect value cannot be greater than 1.

 
{
    "Incidence_Counter": {
        "Count_Events_For_Num_Timesteps": 5,
        "Trigger_Condition_List": [
            "PropertyChange"
        ],
        "Node_Property_Restrictions": [
            {
                "Risk": "HIGH"
            }
        ],
        "Target_Demographic": "Everyone",
        "Demographic_Coverage": 0.6,
        "Property_Restrictions_Within_Node": [
            {
                "Accessibility": "YES"
            }
        ]
    }
}

Event_To_Broadcast

string

NA

NA

NA

The action event to occur when the specified trigger value in the Threshold parameter is met. At least one action must be defined for Event_To_Broadcast. The events contained in the list can be built-in events (see Event list for possible events).

 
{
    "Threshold_Type": "COUNT",
    "Action_List": [
        {
            "Threshold": 20,
            "Event_To_Broadcast": "Action_Event_1"
        },
        {
            "Threshold": 30,
            "Event_To_Broadcast": "Action_Event_2"
        }
    ]
}

Event_Type

enum

NA

NA

INDIVIDUAL

The type of event to be broadcast. Possible values are:

  • COORDINATOR

  • INDIVIDUAL

  • NODE

 
{
    "Action_List": [
        {
            "Event_To_Broadcast": "Action_Event_1",
            "Event_Type": "COORDINATOR",
            "Threshold": 20
        },
        {
            "Event_To_Broadcast": "Action_Event_2",
            "Event_Type": "COORDINATOR",
            "Threshold": 30
        }
    ],
    "Threshold_Type": "COUNT"
}

Incidence_Counter

array of JSON objects

NA

NA

NA

List of JSON objects for specifying the conditions and parameters that must be met for an incidence to be counted. Some of the values are specified in the following parameters: Count_Events_For_Num_Timesteps, Trigger_Condition_List, Node_Property_Restrictions.

 
{
    "Incidence_Counter": {
        "Count_Events_For_Num_Timesteps": 5,
        "Trigger_Condition_List": [
            "PropertyChange"
        ],
        "Node_Property_Restrictions": [
            {
                "Risk": "HIGH"
            }
        ],
        "Target_Demographic": "Everyone",
        "Demographic_Coverage": 0.6,
        "Property_Restrictions_Within_Node": [
            {
                "Accessibility": "YES"
            }
        ]
    }
}

Node_Property_Restrictions

array of JSON objects

NA

NA

NA

A list of the NodeProperty key:value pairs, as defined in the demographics file, that nodes must have to be targeted by the intervention. See NodeProperties and IndividualProperties parameters for more information. You can specify AND and OR combinations of key:value pairs with this parameter.

The following example restricts the intervention to nodes that are urban and medium risk or rural and low risk.

{
    "Node_Property_Restrictions": [
        {
            "Place": "URBAN",
            "Risk": "MED"
        },
        {
            "Place": "RURAL",
            "Risk": "LOW"
        }
    ]
}

Number_Repetitions

integer

-1

10000

1

The number of times an intervention is given, used with Timesteps_Between_Repetitions. A value of -1 implies an infinite number of repetitions.

 
{
    "class": "IncidenceEventCoordinator",
    "Number_Repetitions": 3
}

Responder

array of JSON objects

NA

NA

NA

List of JSON objects for specifying the threshold type, values, and the actions to take when the specified conditions and parameters have been met, as configured in the Incidence_Counter parameter. Some of the values are specified in the following parameters:

  • Threshold_Type

  • Action_List

  • Threshold

  • Event_To_Broadcast

 
{
    "Responder": {
        "Threshold_Type": "COUNT",
        "Action_List": [
            {
                "Threshold": 5,
                "Event_To_Broadcast": "Action_Event_1"
            },
            {
                "Threshold": 10,
                "Event_To_Broadcast": "Action_Event_2"
            }
        ]
    }
}

Threshold

float

0

3.40E+03

0

The threshold value that triggers the specified action for the Event_To_Broadcast parameter. When you use the Threshold parameter you must also use the Event_To_Broadcast parameter.

 
{
    "Threshold_Type": "COUNT",
    "Action_List": [
        {
            "Threshold": 20,
            "Event_To_Broadcast": "Action_Event_1"
        },
        {
            "Threshold": 30,
            "Event_To_Broadcast": "Action_Event_2"
        }
    ]
}

Threshold_Type

enum

NA

NA

COUNT

Threshold type. Possible values are:

  • COUNT

  • PERCENTAGE.

 
{
    "Threshold_Type": "COUNT",
    "Action_List": [
        {
            "Threshold": 20,
            "Event_To_Broadcast": "Action_Event_1"
        },
        {
            "Threshold": 30,
            "Event_To_Broadcast": "Action_Event_2"
        }
    ]
}

Timesteps_Between_Repetitions

integer

-1

1000

-1

The repetition interval.

 
{
    "class": "IncidenceEventCoordinator",
    "Number_Repetitions": 3,
    "Timesteps_Between_Repetitions": 6
}

Trigger_Condition_List

array of strings

NA

NA

NA

A list of events that will trigger an intervention.

 
{
    "Trigger_Condition_List": [
        "NewClinicalCase",
        "NewSevereCase"
    ]
}

Targeting_Config

JSON object

NA

NA

NA

Be more selective of individuals by using the Targeting_Config classes.

 
{
    "Targeting_Config": {
        "class": "HasIntervention",
        "Is_Equal_To": 0,
        "Intervention_Name": "MyVaccine"
    }
}
MultiNodeInterventionDistributor (generic)

The MultiNodeInterventionDistributor intervention class distributes multiple node-level interventions when the distributor only allows specifying one intervention.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Disqualifying_Properties

array of strings

NA

NA

[]

A list of NodeProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Intervention_Name

string

NA

NA

MultiNodeInterventionDistributor

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "MultiNodeInterventionDistributor",
        "Intervention_Name": "Country-wide vaccination"
    }
}

New_Property_Value

string

NA

NA

NA

An optional NodeProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Node_Intervention_List

array of JSON objects

NA

NA

NA

A list of nested JSON objects for the multi-node-level interventions to be distributed by this intervention.

 
{
    "Intervention_Config": {
        "class": "MultiNodeInterventionDistributor",
        "Node_Intervention_List": [
            {
                "class": "SpaceSpraying",
                "Cost_To_Consumer": 1.0,
                "Habitat_Target": "ALL_HABITATS",
                "Spray_Kill_Target": "SpaceSpray_Indoor",
                "Killing_Config": {
                    "class": "WaningEffectExponential",
                    "Initial_Effect": 1.0,
                    "Decay_Time_Constant": 90
                },
                "Reduction_Config": {
                    "class": "WaningEffectExponential",
                    "Initial_Effect": 1.0,
                    "Decay_Time_Constant": 90
                }
            },
            {
                "class": "NodePropertyValueChanger",
                "Target_NP_Key_Value": "InterventionStatus:RECENT_SPRAY",
                "Daily_Probability": 1.0,
                "Maximum_Duration": 0,
                "Revert": 90
            }
        ]
    }
}
WaningEffectCombo (generic)

The WaningEffectCombo class is used within individual-level interventions and allows for specifiying a list of effects when the intervention only has one WaningEffect defined. These effects can be added or multiplied.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Add_Effects

boolean

0

1

0

The Add_Effects parameter allows you to combine multiple effects from the waning effect classes. If set to true (1), then the waning effect values from the different waning effect objects are added together. If set to false (0), the waning effect values are multiplied. The resulting waning effect value must be greater than 1.

 
{
    "class": "WaningEffectCombo",
    "Add_Effects": 1,
    "Expires_When_All_Expire": 0,
    "Effect_List": [
        {
            "class": "WaningEffectMapLinear",
            "Initial_Effect": 1.0,
            "Expire_At_Durability_Map_End": 1,
            "Durability_Map": {
                "Times": [
                    0.0,
                    1.0,
                    2.0
                ],
                "Values": [
                    0.2,
                    0.4,
                    0.6
                ]
            }
        },
        {
            "class": "WaningEffectBox",
            "Initial_Effect": 0.5,
            "Box_Duration": 5.0
        }
    ]
}

Effect_List

array of JSON objects

NA

NA

NA

A list of nested JSON parameters to indicate how the intervention efficacy wanes over time.

 
{
    "class": "WaningEffectCombo",
    "Add_Effects": 1,
    "Expires_When_All_Expire": 0,
    "Effect_List": [
        {
            "class": "WaningEffectMapLinear",
            "Initial_Effect": 1.0,
            "Expire_At_Durability_Map_End": 1,
            "Durability_Map": {
                "Times": [
                    0.0,
                    1.0,
                    2.0
                ],
                "Values": [
                    0.2,
                    0.4,
                    0.6
                ]
            }
        },
        {
            "class": "WaningEffectBox",
            "Initial_Effect": 0.5,
            "Box_Duration": 5.0
        }
    ]
}

Expires_When_All_Expire

boolean

0

1

0

If set to true (1), then all of the effects, as specified in the Effect_List parameter, must expire for the efficacy of the intervention to expire. If set to false (0), then the efficacy of the intervention will expire as soon as one of the parameters expires.

 
{
    "class": "WaningEffectCombo",
    "Add_Effects": 1,
    "Expires_When_All_Expire": 0,
    "Effect_List": [
        {
            "class": "WaningEffectMapLinear",
            "Initial_Effect": 1.0,
            "Expire_At_Durability_Map_End": 1,
            "Durability_Map": {
                "Times": [
                    0.0,
                    1.0,
                    2.0
                ],
                "Values": [
                    0.2,
                    0.4,
                    0.6
                ]
            }
        },
        {
            "class": "WaningEffectBox",
            "Initial_Effect": 0.5,
            "Box_Duration": 5.0
        }
    ]
}

The following campaign classes are new for the Malaria model:

AdherentDrug (malaria)

The AdherentDrug class extends AntiMalarialDrug class and allows for incorporating different patterns of adherence to taking the drug.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Adherence_Config

JSON object

NA

NA

NA

Configuration for the probability of taking a dose of the specified anti-malarial. Use a waning effect class, Waning effect classes, to specify how this probability changes over time.

 
{
    "Adherence_Config": {
        "class": "WaningEffectMapCount",
        "Initial_Effect": 1.0,
        "Durability_Map": {
            "Times": [
                1.0,
                2.0,
                3.0,
                4.0
            ],
            "Values": [
                0.1,
                0.1,
                0.1,
                0.1
            ]
        }
    }
}

Cost_To_Consumer

float

0

99999

10

The unit cost per drug (unamortized).

 
{
    "Cost_To_Consumer": 10
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to nodes with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Dose_Interval

float

0

1000

1

The number of days to wait between the doses, defined in the Doses parameter.

 
{
    "Dose_Interval": 10
}

Doses

array of strings

NA

NA

NA

This is a two dimensional array of the drug names defined in the config. parameter Malaria_Drug_Params. Each inner array defines the drugs to be taken for a single dose. The number of doses is determined by the number of inner arrays. An empty dose is allowed and counts as if the person did not take any doses that day. If multiple pills of the same drug are taken in the same dose, that drug name can be specified multiple times.

 
{
    "Doses": [
    ["TestDrugA", "TestDrugB"],
    ["TestDrugA"],
    ["TestDrugA", "TestDrugB"],
    ["TestDrugB"]
]
}

Intervention_Name

string

NA

NA

AdherentDrug

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "AdherentDrug",
        "Intervention_Name": "Isoniazid with mediocre adherence"
    }
}

Max_Dose_Consideration_Duration

float

1.0/24.0 (1hr)

3.40E+038

3.40E+038

The maximum number of days that an individual will consider taking the doses of the drug.

 
{
    "class": "AdherentDrug",
    "Cost_To_Consumer": 1,
    "Drug_Type": "TestDrug",
    "Dosing_Type": "FullTreatmentCourse",
    "Adherence_Config": {
        "class": "WaningEffectMapCount",
        "Initial_Effect": 1.0,
        "Durability_Map": {
            "Times": [
                1.0,
                2.0,
                3.0
            ],
            "Values": [
                1.0,
                0.0,
                1.0
            ]
        }
    },
    "Non_Adherence_Options": [
        "NEXT_DOSAGE_TIME"
    ],
    "Non_Adherence_Distribution": [
        1.0
    ],
    "Max_Dose_Consideration_Duration": 4,
    "Took_Dose_Event": "NewClinicalCase"
}

New_Property_Value

string

NA

NA

“

An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Non_Adherence_Distribution

array of floats

0

1

0

The non adherence probability value(s) assigned to the corresponding options in Non_Adherence_Options. The sum of non adherence distribution values must equal a total of 1.

 
{
    "Non_Adherence_Distribution": [
        0.7,
        0.3
    ]
}

Non_Adherence_Options

array of eunums

NA

NA

NEXT_UPDATE

Defines the action the person takes if they do not take a particular dose, are not adherent. Possible values are:

NEXT_UPDATE

The person does not take the dose during this update, but will consider taking during the next update (and may not take the dose then). If this person is infected, the time to the next update depends on Infection_Updates_Per_Timestep.

LOST_TAKE_NEXT

The person lost the dose and so takes the next dose in the pill pack (so the total number of pills is reduced).

NEXT_DOSAGE_TIME

The person missed a dose and waits until the next dosage time to take the pill, in order to remain on the prescribed dosage schedule.

STOP

The person decides to stop taking the pills (or has lost the pill pack). Pills taken prior to STOP will have an effect, no remaining pills will be taken.

 
{
    "AD_Non_Adherence_Options": [
        "NEXT_DOSAGE_TIME",
        "NEXT_UPDATE"
    ]
}

Took_Dose_Event

string

NA

NA

“”

This event is broadcast each time a person takes a dose from the pill pack.

 
{
    "Took_Dose_Event": "TakingDrug"
}
BitingRisk (malaria, vector)

The BitingRisk class is used with individual-level interventions and allows for adjusting the relative risk of being bitten by a vector.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Intervention_Name

string

NA

NA

BitingRisk

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "BitingRisk",
        "Intervention_Name": "Relative biting risk with bednet usage"
    }
}

New_Property_Value

string

NA

NA

UNINITIALIZED STRING

An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Risk_Constant

float

0

3.40282E+38

6

The risk to use for all individuals when Risk_Distribution is set to CONSTANT_DISTRIBUTION.

 
{
    "Risk_Distribution": "CONSTANT_DISTRIBUTION",
    "Risk_Constant": 8
}

Risk_Distribution

enum

NA

NA

NOT_INITIALIZED

The distribution type to use for assigning the relative risk of being bitten by a mosquito to each individual. Each assigned value is a random draw from the distribution.

Possible values are:

NOT_INITIALIZED

No distribution set.

CONSTANT_DISTRIBUTION

Use the same value for each individual. Set Risk_Constant.

UNIFORM_DISTRIBUTION

Use a uniform distribution with a given minimum and maximum. Set Risk_Max and Risk_Min.

GAUSSIAN_DISTRIBUTION

The distribution is Gaussian (or normal). Values are resampled to ensure >= 0. Set Risk_Gaussian_Mean and Risk_Gaussian_Std_Dev.

EXPONENTIAL_DISTRIBUTION

The distribution is exponential with a given mean. Set Risk_Exponential.

WEIBULL_DISTRIBUTION

Use a Weibull distribution with a given shape and scale. Set Risk_Kappa and Risk_Lambda.

LOG_NORMAL_DISTRIBUTION

Use a log-normal distribution with a given mean and width. Set Risk_Log_Normal_Mean and Risk_Log_Normal_Width.

POISSON_DISTRIBUTION

Use a Poisson distribution with a given mean. Set Risk_Poisson_Mean.

DUAL_CONSTANT_DISTRIBUTION

Use a distribution where some individuals are set to a value of zero and the rest to a given value. Set Risk_Proportion_0 and Peak_2_Value. This distribution does not use the parameters set for CONSTANT_DISTRIBUTION.

DUAL_EXPONENTIAL_DISTRIBUTION

Use two exponential distributions with given means. Set Risk_Mean_1, Risk_Mean_2, and Risk_Proportion_1. This distribution does not use the parameters set for EXPONENTIAL_DISTRIBUTION.

 
{
    "Risk_Distribution": "WEIBULL_DISTRIBUTION",
    "Risk_Kappa": 0.9,
    "Risk_Lambda": 1.5
}

Risk_Exponential

float

0

3.40282E+38

-1

The mean of the biting risk when Risk_Distribution is set to EXPONENTIAL_DISTRIBUTION.

 
{
    "Risk_Distribution": "EXPONENTIAL_DISTRIBUTION",
    "Risk_Exponential": 4.25
}

Risk_Gaussian_Mean

float

0

3.40282E+38

-1

The mean of the biting risk when Risk_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Risk_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Risk_Gaussian_Mean": 8,
    "Risk_Gaussian_Std_Dev": 1.5
}

Risk_Gaussian_Std_Dev

float

1.17549E-38

3.40282E+38

-1

The standard deviation of the biting risk when Risk_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Risk_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Risk_Gaussian_Mean": 8,
    "Risk_Gaussian_Std_Dev": 1.5
}

Risk_Kappa

float

1.17549E-38

3.40282E+38

-1

The shape value for the biting risk when Risk_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Risk_Distribution": "WEIBULL_DISTRIBUTION",
    "Risk_Kappa": 0.9,
    "Risk_Lambda": 1.5
}

Risk_Lambda

float

1.17549E-38

3.40282E+38

-1

The scale value for the biting risk when Risk_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Risk_Distribution": "WEIBULL_DISTRIBUTION",
    "Risk_Kappa": 0.9,
    "Risk_Lambda": 1.5
}

Risk_Log_Normal_Mu

float

-3.40282e+38

1.70141e+38

3.40282E+38

The mean of the biting risk when Risk_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Risk_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Risk_Log_Normal_Mu": 9,
    "Risk_Log_Normal_Sigma": 2
}

Risk_Log_Normal_Sigma

float

-3.40282e+38

1.70141e+38

3.40282E+38

The width of the biting risk when Risk_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Risk_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Risk_Log_Normal_Mu": 9,
    "Risk_Log_Normal_Sigma": 2
}

Risk_Max

float

0

3.40282E+38

-1

The maximum biting risk when Risk_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Risk_Distribution": "UNIFORM_DISTRIBUTION",
    "Risk_Min": 2,
    "Risk_Max": 7
}

Risk_Mean_1

float

1.17549E-38

3.40282E+38

-1

The mean of the first exponential distribution when Risk_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Risk_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Risk_Mean_1": 4,
    "Risk_Mean_2": 12,
    "Risk_Proportion_1": 0.2
}

Risk_Mean_2

float

1.17549E-38

3.40282E+38

-1

The mean of the second exponential distribution when Risk_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Risk_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Risk_Mean_1": 4,
    "Risk_Mean_2": 12,
    "Risk_Proportion_1": 0.2
}

Risk_Min

float

0

3.40282E+38

-1

The minimum biting risk when Risk_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Risk_Distribution": "UNIFORM_DISTRIBUTION",
    "Risk_Min": 2,
    "Risk_Max": 7
}

Risk_Peak_2_Value

float

0

3.40282E+38

-1

The biting risk value to assign to the remaining individuals when Risk_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Risk_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Risk_Proportion_0": 0.25,
    "Risk_Peak_2_Value": 5
}

Risk_Poisson_Mean

float

0

3.40282E+38

-1

The mean of the biting risk when Risk_Distribution is set to POISSON_DISTRIBUTION.

 
{
    "Risk_Distribution": "POISSON_DISTRIBUTION",
    "Risk_Poisson_Mean": 5
}

Risk_Proportion_0

float

0

1

-1

The proportion of individuals to assign a value of zero biting risk when Risk_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Risk_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Risk_Proportion_0": 0.25,
    "Risk_Peak_2_Value": 5
}

Risk_Proportion_1

float

0

1

-1

The proportion of individuals in the first exponential distribution when Risk_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Risk_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Risk_Mean_1": 4,
    "Risk_Mean_2": 12,
    "Risk_Proportion_1": 0.2
}
OutbreakIndividualMalaria (malaria)

The OutbreakIndividualMalaria class extends OutbreakIndividual class and allows for specifying a specific strain of infection.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Clade

integer

0

9

0

The clade ID (Clade) of the outbreak infection. Together with the genome ID (Genome) they are a unitary object representing a strain of infection, which allows for differentiation among infections. Values for Clade may range from 0 to Number_of_Clades-1.

Intervention distribution example:

{
    "Enable_Strain_Tracking": 1,
    "Events": [
        {
            "Event_Coordinator_Config": {
                "Demographic_Coverage": 0.001,
                "Intervention_Config": {
                    "Clade": 1,
                    "Genome": 3,
                    "IgnoreImmunity": 1,
                    "class": "Outbreak"
                },
                "Target_Demographic": "Everyone",
                "class": "StandardInterventionDistributionEventCoordinator"
            },
            "Event_Name": "Outbreak",
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 30,
            "class": "CampaignEvent"
        }
    ]
}

Ignore_Immunity

boolean

0

1

1

Individuals will be force-infected (with a specific strain) regardless of actual immunity level when set to true (1).

 
{
    "Intervention_Config": {
        "Antigen": 0,
        "Genome": 0,
        "class": "OutbreakIndividual",
        "Ignore_Immunity": 0
    }
}

Incubation_Period_Override

integer

-1

2147480000.0

-1

The incubation period, in days, that infected individuals will go through before becoming infectious. This value overrides the incubation period set in the configuration file. Set to -1 to honor the configuration parameter settings.

 
{
    "Incubation_Period_Override": 0
}

The following campaign class is new for the TBHIV model:

TBHIVConfigurableTBdrug (tuberculosis)

The TBHIVConfigurableTBdrug class is an individual level intervention for TB treatment. The intervention applies TB drug effects to the progression, associated mortality, transmission and acquisition of TB infections in HIV positive and negative individuals.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Active_Multiplier

array of floats

0

1

1

Multiplier of clearance/inactivation if active TB on drug treatment. Relapse, mortality, and resistance apply only to active.

 
{
    "Active_Multiplier": 1
}

Cost_To_Consumer

float

0

99999

1

The unit cost per drug (unamortized).

 
{
    "Cost_To_Consumer": 10
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

0

1

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Dose_Interval

float

0

99999

1

The interval between doses, in days.

 
{
    "Dose_Interval": 1
}

Drug_Cmax

float

0

10000

1

The maximum drug concentration that can be used, and is in the same units as Drug_PKPD_C50. Since drug concentrations and C50 values are often specified with different unit systems, these two parameters have been designed to be flexible provided that the units are the same for each. This parameter is used when Durability_Profile is set to CONCENTRATION_VERSUS_TIME.

 
{
    "Durability_Profile": "CONCENTRATION_VERSUS_TIME",
    "Drug_Cmax": 200,
    "Drug_PKPD_C50": 6,
    "Drug_Vd": 1
}

Drug_PKPD_C50

float

0

10000

1

The concentration at which drug killing rates are half of the maximum. Must use the same units as Drug_Cmax. This parameter is used when Durability_Profile is set to CONCENTRATION_VERSUS_TIME.

 
{
    "Durability_Profile": "CONCENTRATION_VERSUS_TIME",
    "Drug_Cmax": 200,
    "Drug_PKPD_C50": 6,
    "Drug_Vd": 1
}

Drug_Vd

float

0

10000

1

The volume of drug distribution. This value is the ratio of the volume of the second compartment to the volume of the first compartment in a two-compartment model, and is dimensionless. This parameter is used when Durability_Profile is set to CONCENTRATION_VERSUS_TIME.

 
{
    "Durability_Profile": "CONCENTRATION_VERSUS_TIME",
    "Drug_Cmax": 200,
    "Drug_PKPD_C50": 6,
    "Drug_Vd": 1
}

Durability_Profile

enum

NA

NA

FIXED_DURATION_CONSTANT_EFFECT

The profile of durability decay. Possible values are:

FIXED_DURATION_CONSTANT_EFFECT

The durability does not decay.

CONCENTRATION_VERSUS_TIME

Conditionally loads the following parameters: Drug_Cmax, Drug_PKPD_C50, and Drug_Vd.

 
{
    "Durability_Profile": "CONCENTRATION_VERSUS_TIME",
    "Drug_Cmax": 200,
    "Drug_PKPD_C50": 6,
    "Drug_Vd": 1
}

Fraction_Defaulters

float

0

1

0

The fraction of individuals who will not finish their drug treatment.

 
{
    "Fraction_Defaulters": 0
}

Intervention_Name

string

NA

NA

NA

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "TBHIVConfigurableTBdrug",
        "Intervention_Name": "Isoniazid distribution"
    }
}

Latency_Multiplier

array of floats

0

1

1

Multiplier of clearance/inactivation if latent TB on drug treatment for the TB drug cure rate parameters (TB_Drug_Cure_Rate, TB_Drug_Cure_Rate_MDR, TB_Drug_Cure_Rate_HIV ) and the TB drug inactivation rate parameters (TB_Drug_Inactivation_Rate, TB_Drug_Inactivation_Rate_MDR, TB_Drug_Inactivation_Rate_HIV).

 
{
    "Latency_Multiplier": 1
}

New_Property_Value

string

NA

NA

“

An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Primary_Decay_Time_Constant

float

0

1.00E+00

0

The primary decay time constant (in days) of the decay profile.

 
{
    "Primary_Decay_Time_Constant": 180
}

Remaining_Doses

integer

0

999999

0

The remaining doses in an intervention; enter a negative number for unlimited doses.

 
{
    "Remaining_Doses": 1
}

Secondary_Decay_Time_Constant

float

0

999999

1

The secondary decay time constant of the durability profile. This parameter is used when Durability_Profile is set to CONCENTRATION_VERSUS_TIME.

 
{
    "Durability_Profile": "CONCENTRATION_VERSUS_TIME",
    "Secondary_Decay_Time_Constant": 730
}

TB_Drug_Name

string

NA

NA

NA

The name of TB drug used in the TBHIVConfigurationTBdrug intervention. This must be one of the listed drugs as specificed under TBHIV_Drug_Types in the configuration file.

 
{
    "Active_Multiplier": 1,
    "Durability_Profile": "FIXED_DURATION_CONSTANT_EFFECT",
    "Latency_Multiplier": 1,
    "Remaining_Doses": 1,
    "TB_Drug_Name": "PreDOTSLowNoMDR",
    "class": "TBHIVConfigurableTBdrug"
}

TB_Reduced_Acquire

array of floats

0

1

0

Proportion reduction in acquisition of new TB infection under drug treatment. Only relevant when TB_Enable_Exogenous is enabled to allow exogenous re-infection.

 
{
    "TBHIV_Drug_Params": {
        "ACFDOTS": {
            "TB_Drug_Cure_Rate": 0.25,
            "TB_Drug_Inactivation_Rate": 1e-09,
            "TB_Drug_Inactivation_Rate_HIV": 1e-09,
            "TB_Drug_Inactivation_Rate_MDR": 1e-09,
            "TB_Drug_Mortality_Rate": 1e-09,
            "TB_Drug_Mortality_Rate_HIV": 1e-09,
            "TB_Drug_Mortality_Rate_MDR": 1e-09,
            "TB_Drug_Primary_Decay_Time_Constant": 179,
            "TB_Drug_Relapse_Rate": 1e-09,
            "TB_Drug_Relapse_Rate_HIV": 1e-09,
            "TB_Drug_Relapse_Rate_MDR": 1e-09,
            "TB_Drug_Resistance_Rate": 1e-09,
            "TB_Drug_Resistance_Rate_HIV": 1e-09,
            "TB_Reduced_Acquire": 1,
            "TB_Reduced_Transmit": 1,
            "TB_Drug_Cure_Rate_HIV": 0,
            "TB_Drug_Cure_Rate_MDR": 0
        }
    }
}

TB_Reduced_Transmit

array of floats

0

1

0

Proportion reduction in TB transmission under drug treatment. The value scales the Base_Infectivity parameter.

 
{
    "TBHIV_Drug_Params": {
        "ACFDOTS": {
            "TB_Drug_Cure_Rate": 0.25,
            "TB_Drug_Inactivation_Rate": 1e-09,
            "TB_Drug_Inactivation_Rate_HIV": 1e-09,
            "TB_Drug_Inactivation_Rate_MDR": 1e-09,
            "TB_Drug_Mortality_Rate": 1e-09,
            "TB_Drug_Mortality_Rate_HIV": 1e-09,
            "TB_Drug_Mortality_Rate_MDR": 1e-09,
            "TB_Drug_Primary_Decay_Time_Constant": 179,
            "TB_Drug_Relapse_Rate": 1e-09,
            "TB_Drug_Relapse_Rate_HIV": 1e-09,
            "TB_Drug_Relapse_Rate_MDR": 1e-09,
            "TB_Drug_Resistance_Rate": 1e-09,
            "TB_Drug_Resistance_Rate_HIV": 1e-09,
            "TB_Reduced_Acquire": 1,
            "TB_Reduced_Transmit": 1,
            "TB_Drug_Cure_Rate_HIV": 0,
            "TB_Drug_Cure_Rate_MDR": 0
        }
    }
}
Deprecated demographics parameters

ImmunityDistributionFlag, ImmunityDistribution1, and ImmunityDistribution2 were renamed to SusceptibilityDistributionFlag, SusceptibilityDistribution1, and SusceptibilityDistribution2. In previous versions of EMOD, the naming was counterintuitive to the functionality. For example, setting a value of 1 for the immunity indicated zero immunity/complete susceptibility. Now the parameters more accurately reflect that you are setting a susceptibility value. The functionality is the same.

Deprecated config parameters

Immunity_Transmission_Factor, Immunity_Mortality_Factor, and Immunity_Acquisition_Factor were renamed to Post_Infection_Transmission_Multiplier, Post_Infection_Mortality_Multiplier, and Post_Infection_Acquistion_Multiplier. The functionality is the same.

Deprecated campaign parameters

The TB_SIM simulation type has been deprecated and replaced with TBHIV_SIM, which does not include HIV transmission but adds the ability to model the effect of HIV coinfection on the spread of TB.

The following campaign classes, which have not yet been fully tested with the TBHIV simulation type, have been disabled:

  • HealthSeekingBehaviorUpdate

  • HealthSeekingBehaviorUpdateable

  • AntiTBPropDepDrug

  • SmearDiagnostic

In previous versions of EMOD, you could set the tendency of individuals to seek out health care using HealthSeekingBehaviorUpdateable and then update the value of the Tendency parameter using HealthSeekingBehaviorUpdate. Now, you use individual properties to update individuals when they receive the SimpleHealthSeekingBehavior intervention, as you would to control the flow of individuals through other intervention classes. For example, you could create an individual property with HSBold and HSBnew values in the demographics file and assign all individuals to HSBold. Then you could distribute the first SimpleHealthSeekingBehavior (with one Tendency value) to all HSBold individuals and use New_Property_Value to assign them to HSBnew after receiving the intervention. The next SimpleHealthSeekingBehavior intervention (with a different Tendency value) could be distributed, setting Disqualifying_Properties to HSBold and, if desired, using New_Property_Value to reassign HSBold to those individuals.

Similarly, AntiTBPropDepDrug was disabled and superseded with TBHIVConfigurableTBDrug, which allows for drug effects based on HIV status and where dependence on IndividualProperties is configured through Property_Restrictions. In addition, AntiTBPropDepDrub can be replaced with AntiTBDrug, also using Property_Restrictions and new property values to target particular individuals with drug interventions for tuberculosis without HIV coinfections.

SmearDiagnostic was disabled and can be replaced with DiagnosticTreatNeg. While SmearDiagnostic would only broadcast when an individual had a positive smear diagnostic, DiagnosticTreatNeg has the added benefit of broadcasting negative and default diagnostic test events.

TB_Drug_Clearance_Rate_HIV and TB_Drug_Clearance_Rate_MDR parameters have been renamed to TB_Drug_Cure_Rate_HIV and TB_Drug_Cure_Rate_MDR.

Error messages

When attempting to run an intervention using one of the disabled tuberculosis classes, such as AntiTBPropDepDrug, HealthSeekingBehaviorUpdate, HealthSeekingBehaviorUpdateable, and SmearDiagnostic, you will receive an error similar to the following: “00:00:01 [0] [I] [JsonConfigurable] Using the default value ( “Intervention_Name” : “HealthSeekingBehaviorUpdateable” ) for unspecified parameter. 00:00:02 [0] [E] [Eradication] 00:00:02 [0] [E] [Eradication] GeneralConfigurationException: Exception in SimulationEventContext.cpp at 242 in Kernel::SimulationEventContextHost::LoadCampaignFromFile. Array out of bounds”

Note

These campaign classes have been disabled because they have not yet been fully tested with the TBHIV simulation type.

EMOD schema and simulation types

In the schema for the Simulation_Type parameter the enum values list additional simulation types that are not supported by EMOD.

EMOD supports the following simulation types for modeling a variety of diseases:

  • Generic disease (GENERIC_SIM), which can be used for modeling a variety of diseases such as influenza or measles

  • Vector-borne diseases (VECTOR_SIM), which can be used for modeling vector-borne diseases such as dengue

  • Malaria (MALARIA_SIM), which adds features specific to malaria biology and treatment

  • Tuberculosis with HIV coinfection (TBHIV_SIM), which can be used for modeling TB transmission, with the option to add HIV coinfection as a contributing factor

  • Sexually transmitted infections (STI_SIM), which adds features for sexual relationship networks

  • HIV (HIV_SIM), which adds features specific to HIV biology and treatment

  • Environmental transmission (ENVIRONMENTAL_SIM), which adds features for diseases transmitted through contaminated food or water

  • Typhoid (TYPHOID_SIM), which adds features specific to typhoid biology and treatment

For information about building the schema, see Generate a list of all available parameters (a schema).

EMOD v2.13

The EMOD v2.13 release includes many new features for all supported simulation types.

New configuration parameters

For the malaria simulation type, the following new configuration parameters are available:

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Custom_Reports_Filename

string

NA

NA

UNINITIALIZED STRING

The name of the file containing custom report configuration parameters. Omitting this parameter or setting it to RunAllCustomReports will load all reporters found that are valid for the given simulation type. The file must be in JSON format.

 
{  
    "Custom_Reports_Filename": "custom_reports.json"
}

Cycle_Arrhenius_1

float

0

1.00E+15

4.09E+10

The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the mosquito feeding cycle rate. This duration is a decreasing function of temperature. The variable a1 is a temperature-independent scale factor on feeding rate. Temperature_Dependent_Feeding_Cycle must be set to ARRHENIUS_DEPENDENCE.

 
{
    "Temperature_Dependent_Feeding_Cycle": "ARRHENIUS_DEPENDENCE",
    "Vector_Species_Params": {
        "arabiensis": {
            "Cycle_Arrhenius_1": 99,
            "Cycle_Arrhenius_2": 88
        }
    }
}

Cycle_Arrhenius_2

float

0

1.00E+15

7740

The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the mosquito feeding cycle rate. This duration is a decreasing function of temperature. The variable a2 is a temperature-independent scale factor on feeding rate. Temperature_Dependent_Feeding_Cycle must be set to ARRHENIUS_DEPENDENCE.

 
{
    "Temperature_Dependent_Feeding_Cycle": "ARRHENIUS_DEPENDENCE",
    "Vector_Species_Params": {
        "arabiensis": {
            "Cycle_Arrhenius_1": 99,
            "Cycle_Arrhenius_2": 88
        }
    }
}

Cycle_Arrhenius_Reduction_Factor

float

0

1

1

The scale factor applied to cycle duration (from oviposition to oviposition) to reduce the duration when primary follicles are at stage II rather than I in the case of newly emerged females. Temperature_Dependent_Feeding_Cycle must be set to ARRHENIUS_DEPENDENCE.

 
{
    "Temperature_Dependent_Feeding_Cycle": "ARRHENIUS_DEPENDENCE",
    "Vector_Species_Params": {
        "funestus": {
            "Cycle_Arrhenius_Reduction_Factor": 0.44
        }
    }
}

Drought_Egg_Hatch_Delay

float

0

1

0.33

Proportion of regular egg hatching that still occurs when habitat dries up. Enable_Drought_Egg_Hatch_Delay must be set to 1.

 
{ 
    "Enable_Drought_Egg_Hatch_Delay": 1,
    "Drought_Egg_Hatch_Delay": 0.33
}

Egg_Arrhenius1

float

0

1.00E+10

6.16E+07

The Arrhenius equation, math:a_1^{-a_2/T}, with T in degrees Kelvin, parameterizes the daily rate of mosquito egg hatching. This duration is a decreasing function of temperature. The variable a1 is a temperature-independent scale factor on hatching rate. Enable_Temperature_Dependent_Egg_Hatching must be set to 1.

 
{
    "Enable_Temperature_Dependent_Egg_Hatching": 1,     
    "Egg_Arrhenius1": 61599956,
    "Egg_Arrhenius2": 5754
}

Egg_Arrhenius2

float

0

1.00E+10

5754.03

The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the daily rate of mosquito egg hatching. This duration is a decreasing function of temperature. The variable a2 is a temperature-dependent scale factor on hatching rate. Enable_Temperature_Dependent_Egg_Hatching must be set to 1.

 
{
    "Enable_Temperature_Dependent_Egg_Hatching": 1,     
    "Egg_Arrhenius1": 61599956,
    "Egg_Arrhenius2": 5754
}

Egg_Hatch_Density_Dependence

enum

NA

NA

NO_DENSITY_DEPENDENCE

The effect of larval density on egg hatching rate. Possible values are:

DENSITY_DEPENDENCE

Egg hatching is reduced when the habitat is nearing its carrying capacity.

NO_DENSITY_DEPENDENCE

Egg hatching is not dependent upon larval density.

 
{
    "Egg_Hatch_Density_Dependence": "NO_DENSITY_DEPENDENCE"
}

Enable_Drought_Egg_Hatch_Delay

boolean

0

1

0

Controls whether or not eggs hatch at delayed rates, set by Drought_Egg_Hatch_Delay, when the habitat dries up completely.

 
{
    "Enable_Drought_Egg_Hatch_Delay": 1, 
    "Drought_Egg_Hatch_Delay": 0.33
}

Enable_Egg_Mortality

boolean

0

1

0

Controls whether or not to include a daily mortality rate on the egg population, which is independent of climatic factors.

 
{
    "Enable_Egg_Mortality": 1,
}

Enable_Temperature_Dependent_Egg_Hatching

boolean

0

1

0

Controls whether or not temperature has an effect on egg hatching, defined by Egg_Arrhenius_1 and Egg_Arrhenius_2.

 
{  
    "Enable_Temperature_Dependent_Egg_Hatching": 1,  
    "Egg_Arrhenius1": 61599956.864, 
    "Egg_Arrhenius2": 5754.033
}

Enable_Vector_Aging

boolean

0

1

0

Controls whether or not vectors undergo senescence as they age.

 
{
    "Enable_Vector_Aging": 1
}

Incubation_Period_Log_Mean

float

0

3.40E+38

6

The mean of log normal for the incubation period distribution. Incubation_Period_Distribution must be set to LOG_NORMAL_DURATION.

 
{
    "Incubation_Period_Distribution": "LOG_NORMAL_DURATION",
    "Incubation_Period_Log_Mean": 5.758,
    "Incubation_Period_Log_Width": 0.27
}

Incubation_Period_Log_Width

float

0

3.40E+38

1

The log width of log normal for the incubation period distribution. Incubation_Period_Distribution must be set to LOG_NORMAL_DURATION.

 
{
    "Incubation_Period_Distribution": "LOG_NORMAL_DURATION",
    "Incubation_Period_Log_Mean": 5.758,
    "Incubation_Period_Log_Width": 0.27
}

Infectivity_Exponential_Baseline

float

0

1

0

The scale factor applied to Base_Infectivity at the beginning of a simulation, before the infectivity begins to grow exponentially. Infectivity_Scale_Type must be set to EXPONENTIAL_FUNCTION_OF_TIME.

 
{
    "Infectivity_Exponential_Baseline": 0.1, 
    "Infectivity_Exponential_Delay": 90, 
    "Infectivity_Exponential_Rate": 45, 
    "Infectivity_Scale_Type": "EXPONENTIAL_FUNCTION_OF_TIME"
}

Infectivity_Exponential_Delay

float

0

3.40E+38

0

The number of days before infectivity begins to ramp up exponentially. Infectivity_Scale_Type must be set to EXPONENTIAL_FUNCTION_OF_TIME.

 
{
    "Infectivity_Exponential_Baseline": 0.1, 
    "Infectivity_Exponential_Delay": 90, 
    "Infectivity_Exponential_Rate": 45, 
    "Infectivity_Scale_Type": "EXPONENTIAL_FUNCTION_OF_TIME"
}

Infectivity_Exponential_Rate

float

0

3.40E+38

0

The daily rate of exponential growth to approach to full infectivity after the delay set by Infectivity_Exponential_Delay has passed. Infectivity_Scale_Type must be set to EXPONENTIAL_FUNCTION_OF_TIME.

 
{
    "Infectivity_Exponential_Rate": 45
}

Memory_Usage_Halting_Threshold_Working_Set_MB

integer

0

1.00E+06

8000

The maximum size (in MB) of working set memory before the system throws an exception and halts.

 
{
    "Memory_Usage_Halting_Threshold_Working_Set_MB": 4000
}

Memory_Usage_Warning_Threshold_Working_Set_MB

integer

0

1.00E+06

7000

The maximum size (in MB) of working set memory before memory usage statistics are written to the log regardless of log level.

 
{
    "Memory_Usage_Warning_Threshold_Working_Set_MB": 3500
}

Nighttime_Feeding_Fraction

float

0

1

1

The fraction of feeds on humans (for a specific mosquito species) that occur during the nighttime. Thus the fraction of feeds on humans that occur during the day equals 1 - (value of this parameter).

 
{
    "Vector_Species_Params": {
        "arabiensis": {
            "Acquire_Modifier": 0.2, 
            "Adult_Life_Expectancy": 10, 
            "Anthropophily": 0.95, 
            "Aquatic_Arrhenius_1": 84200000000, 
            "Aquatic_Arrhenius_2": 8328, 
            "Aquatic_Mortality_Rate": 0.1, 
            "Cycle_Arrhenius_1": 0, 
            "Cycle_Arrhenius_2": 0, 
            "Cycle_Arrhenius_Reduction_Factor": 0, 
            "Days_Between_Feeds": 3, 
            "Egg_Batch_Size": 100, 
            "Immature_Duration": 4, 
            "Indoor_Feeding_Fraction": 0.5, 
            "Infected_Arrhenius_1": 117000000000, 
            "Infected_Arrhenius_2": 8336, 
            "Infected_Egg_Batch_Factor": 0.8, 
            "Infectious_Human_Feed_Mortality_Factor": 1.5, 
            "Larval_Habitat_Types": {
                "TEMPORARY_RAINFALL": 11250000000
            }, 
            "Nighttime_Feeding_Fraction": 1, 
            "Transmission_Rate": 0.5
        }
    }
}

Serialization_Time_Steps

array of integers

0

2.15E+09

The list of time steps after which to save the serialized state. 0 (zero) indicates the initial state before simulation, n indicates after the nth time step. By default, no serialized state is saved.

 
{
    "Serialization_Time_Steps": [
        0, 
        10
    ]
}

Serialized_Population_Filenames

array of strings

NA

NA

NA

Array of filenames with serialized population data. The number of filenames must match the number of cores used for the simulation. The file must be in .dtk format.

 
{
    "Serialized_Population_Filenames": [
        "state-00010.dtk"
    ]
}

Serialized_Population_Path

string

NA

NA

.

The root path for the serialized population files.

 
{
    "Serialized_Population_Path": "../00_Generic_Version_1_save/output"
}

Temperature_Dependent_Feeding_Cycle

enum

NA

NA

NO_TEMPERATURE_DEPENDENCE

The effect of temperature on the duration between blood feeds. Possible values are:

NO_TEMPERATURE_DEPENDENCE

No relation to temperature; days between feeds will be constant and specified by Days_Between_Feeds for each species.

ARRHENIUS_DEPENDENCE

Use the Arrhenius equation with parameters Cycle_Arrhenius_1 and Cycle_Arrhenius_2.

BOUNDED_DEPENDENCE

Use an equation bounded at 10 days at 15C and use Days_Between_Feeds to set the duration at 30C.

 
{
    "Temperature_Dependent_Feeding_Cycle": "BOUNDED_DEPENDENCE"
}

To view all available configuration parameters, see Configuration parameters.

New demographics parameters

In all simulation types, you can now assign properties like risk or quality of care to nodes using NodeProperties, which are configured much like IndividualProperties. In addition, a new property type is available for both nodes and individuals called InterventionStatus, which is used by the campaign file to distribute interventions based on the other interventions an individual or node has received. This property type was previously available only for individuals in the HIV simulation type and was known as the CascadeState. The relevant campaign parameters are described in the next section.

For more information, see the table below.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

NodeProperties

array of objects

NA

NA

NA

An array that contains parameters that add properties to nodes in a simulation. For example, you can define values for intervention status, risk, and other properties and assign values to different nodes.

 
{
    "NodeProperties": [{
        "Property": "Risk",
        "Values": ["HIGH", "MEDIUM", "LOW"],
        "Initial_Distribution": [0.1, 0.4, 0.5]
    }]
}

Property

enum

NA

NA

NA

The individual or node property type for which you will assign arbitrary values to create groups. You can then move individuals or nodes into or out of different groups or target interventions to particular groups. Possible values are:

Age_Bin

Assign individuals to age bins. Use with Age_Bin_Edges_In_Years. Cannot be used with NodeProperties.

Accessibility

Tag individuals or nodes based on their accessibility.

Geographic

Tag individuals or nodes based on geographic characteristics.

HasActiveTB

Tag individuals or nodes based on active TB status. Typically used only with HIV ART staging interventions.

InterventionStatus

Tag individuals or nodes based on intervention status, so that receiving an intervention can affect how other interventions are distributed. Use with Disqualifying_Properties and New_Property_Value in the campaign file.

Place

Tag individuals or nodes based on place.

Risk

Tag individuals or nodes based on disease risk.

QualityofCare

Tag individuals or nodes based on the quality of medical care.

 
{
    "Defaults": {
        "IndividualProperties": [{
            "Property": "Age_Bin",
            "Age_Bin_Edges_In_Years": [ 0, 6, 10, 20, -1 ]
        }]
    }
}

{
    "NodeProperties": [{
        "Property": "InterventionStatus",
        "Values": ["NONE", "RECENT_SPRAY"],
        "Initial_Distribution": [1.0, 0.0]
    }]
}
New campaign parameters

The addition of NodeProperties in the demographics file also necessitated the addition of Node_Property_Restrictions to control how interventions are distributed based on the property values assigned to each node.

The new campaign parameters Disqualifying_Properties and New_Property_Value were added to every intervention to control how interventions are distributed based on properties assigned to individuals or nodes. Disqualifying_Properties prevents an intervention from being distributed to individuals or nodes with certain property values. New_Property_Value updates the property value after they receive an intervention.

These are generally used with the the property type InterventionStatus to control how interventions are distributed based on the interventions already received. For example, a household may be ineligible for clinical treatment for a length of time if they already received treatment during a drug campaign. This functionality was previously only available for individuals in the HIV simulation type and used parameters previously called Abort_States and Valid_Cascade_States.

The following event coordinators and intervention classes are new for this simulation type.

CommunityHealthWorkerEventCoordinator

The CommunityHealthWorkerEventCoordinator coordinator class is used to model a health care worker’s ability to distribute interventions to the nodes and individual in their coverage area. This coordinator distributes a limited number of interventions per day, and can create a backlog of individuals or nodes requiring the intervention. For example, individual-level interventions could be distribution of drugs and node-level interventions could be spraying houses with insecticide.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Amount_In_Shipment

integer

1

2147480000.0

2147480000.0

The number of interventions (such as vaccine doses) that a health worker or clinic receives in a shipment. Interventions can only be distributed if they are in stock; stock is updated every Days_Between_Shipments with the Amount_In_Shipment.

 
{
    "Amount_In_Shipment": 10
}

Days_Between_Shipments

float

1

3.40282e+38

3.40E+38

The number of days to wait before a clinic or health worker receives a new shipment of interventions (such as vaccine doses). Interventions can only be distributed if they are in stock; stock is updated every Days_Between_Shipments with the Amount_In_Shipment.

 
{
    "Days_Between_Shipments": 30
}

Demographic_Coverage

float

0

1

1

The fraction of individuals in the target demographic that will receive this intervention.

 
{
    "Demographic_Coverage": 1
}

Duration

float

0

3.40282e+38

3.40E+38

The number of days for an event coordinator to be active before it expires.

 
{
    "Duration": 65
}

Initial_Amount_Constant

float

0

3.40282E+38

-1

The initial amount to use for all health workers when Initial_Amount_Distribution is set to CONSTANT_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "CONSTANT_DISTRIBUTION",
    "Initial_Amount_Constant": 8
}

Initial_Amount_Distribution

enum

NA

NA

NOT_INITIALIZED

The distribution type to use for assigning the initial amount of interventions in stock. Each assigned value is a random draw from the distribution.

Possible values are:

NOT_INITIALIZED

No distribution set.

CONSTANT_DISTRIBUTION

Use the same value for each individual. Set Initial_Amount_Constant.

UNIFORM_DISTRIBUTION

Use a uniform distribution with a given minimum and maximum. Set Initial_Amount_Max and Initial_Amount_Min.

GAUSSIAN_DISTRIBUTION

The distribution is Gaussian (or normal). Values are resampled to ensure >= 0. Set Initial_Amount_Gaussian_Mean and Initial_Amount_Gaussian_Std_Dev.

EXPONENTIAL_DISTRIBUTION

The distribution is exponential with a given mean. Set Initial_Amount_Exponential.

WEIBULL_DISTRIBUTION

Use a Weibull distribution with a given shape and scale. Set Initial_Amount_Kappa and Initial_Amount_Lambda.

LOG_NORMAL_DISTRIBUTION

Use a log-normal distribution with a given mean and standard deviation of the natural log. Set Initial_Amount_Log_Normal_Mu and Initial_Amount_Log_Normal_Sigma.

POISSON_DISTRIBUTION

Use a Poisson distribution with a given mean. Set Initial_Amount_Poisson_Mean.

DUAL_CONSTANT_DISTRIBUTION

Use a distribution where some individuals are set to a value of zero and the rest to a given value. Set Initial_Amount_Proportion_0 and Peak_2_Value. This distribution does not use the parameters set for CONSTANT_DISTRIBUTION.

DUAL_EXPONENTIAL_DISTRIBUTION

Use two exponential distributions with given means. Set Initial_Amount_Mean_1, Initial_Amount_Mean_2, and Initial_Amount_Proportion_1. This distribution does not use the parameters set for EXPONENTIAL_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Initial_Amount_Mean_1": 4,
    "Initial_Amount_Mean_2": 12,
    "Initial_Amount_Proportion_1": 0.2
}

Initial_Amount_Exponential

float

0

3.40282E+38

-1

The mean of the initial amount of intervention in stock when Initial_Amount_Distribution is set to EXPONENTIAL_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "EXPONENTIAL_DISTRIBUTION",
    "Initial_Amount_Exponential": 4.25
}

Initial_Amount_Gaussian_Mean

float

0

3.40282E+38

-1

The mean of the initial amount of intervention in stock when Initial_Amount_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Initial_Amount_Gaussian_Mean": 8,
    "Initial_Amount_Gaussian_Std_Dev": 1.5
}

Initial_Amount_Gaussian_Std_Dev

float

1.17549E-38

3.40282E+38

-1

The standard deviation of the initial amount of intervention in stock when Initial_Amount_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Initial_Amount_Gaussian_Mean": 8,
    "Initial_Amount_Gaussian_Std_Dev": 1.5
}

Initial_Amount_Kappa

float

1.17549E-38

3.40282E+38

-1

The shape value for the initial amount of intervention in stock when Initial_Amount_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "WEIBULL_DISTRIBUTION",
    "Initial_Amount_Kappa": 0.9,
    "Initial_Amount_Lambda": 1.5
}

Initial_Amount_Lambda

float

1.17549E-38

3.40282E+38

-1

The scale value for the initial amount of intervention in stock when Initial_Amount_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "WEIBULL_DISTRIBUTION",
    "Initial_Amount_Kappa": 0.9,
    "Initial_Amount_Lambda": 1.5
}

Initial_Amount_Log_Normal_Mu

float

-3.40282e+38

1.70141e+38

3.40282E+38

The mean of the natural log of the initial amount of intervention in stock when Initial_Amount_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Initial_Amount_Log_Normal_Mu": 9,
    "Initial_Amount_Log_Normal_Sigma": 2
}

Initial_Amount_Log_Normal_Sigma

float

-3.40282e+38

3.40282E+38

1

The standard deviation of the natural log of the initial amount of intervention in stock when Initial_Amount_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Initial_Amount_Log_Normal_Mu": 9,
    "Initial_Amount_Log_Normal_Sigma": 2
}

Initial_Amount_Max

float

0

3.40282E+38

-1

The maximum initial amount of intervention in stock when Initial_Amount_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "UNIFORM_DISTRIBUTION",
    "Initial_Amount_Min": 2,
    "Initial_Amount_Max": 7
}

Initial_Amount_Mean_1

float

1.17549E-38

3.40282E+38

-1

The mean of the first exponential distribution when Initial_Amount_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Initial_Amount_Mean_1": 4,
    "Initial_Amount_Mean_2": 12,
    "Initial_Amount_Proportion_1": 0.2
}

Initial_Amount_Mean_2

float

1.17549E-38

3.40282E+38

-1

The mean of the second exponential distribution when Initial_Amount_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Initial_Amount_Mean_1": 4,
    "Initial_Amount_Mean_2": 12,
    "Initial_Amount_Proportion_1": 0.2
}

Initial_Amount_Min

float

0

3.40282E+38

-1

The minimum of initial amount of intervention in stock when Initial_Amount_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "UNIFORM_DISTRIBUTION",
    "Initial_Amount_Min": 2,
    "Initial_Amount_Max": 7
}

Initial_Amount_Peak_2_Value

float

0

3.40282E+38

-1

The initial amount in stock to assign to the remaining health workers when Initial_Amount_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Initial_Amount_Proportion_0": 0.25,
    "Initial_Amount_Peak_2_Value": 5
}

Initial_Amount_Poisson_Mean

float

0

3.40282E+38

-1

The mean of the initial amount of intervention in stock when Initial_Amount_Distribution is set to the POISSON_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "POISSON_DISTRIBUTION",
    "Initial_Amount_Poisson_Mean": 5
}

Initial_Amount_Proportion_0

float

0

1

-1

The proportion of health workers to assign a value of zero stock when Initial_Amount_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Initial_Amount_Proportion_0": 0.25,
    "Initial_Amount_Peak_2_Value": 5
}

Initial_Amount_Proportion_1

float

0

1

-1

The proportion of health workers in the first exponential distribution when Initial_Amount_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Initial_Amount_Mean_1": 4,
    "Initial_Amount_Mean_2": 12,
    "Initial_Amount_Proportion_1": 0.2
}

Intervention_Config

JSON object

NA

NA

NA

The nested JSON of the actual intervention to be distributed by this event coordinator.

 
{
    "Intervention_Config": {
        "class": "BroadcastEvent",
        "Broadcast_Event": "EventFromIntervention"
    }
}

Max_Distributed_Per_Day

integer

1

2147480000.0

2147480000.0

The maximum number of interventions (such as vaccine doses) that can be distributed by health workers or clinics in a given day.

 
{
    "Max_Distributed_Per_Day": 1
}

Max_Stock

integer

0

2147480000.0

2147480000.0

The maximum number of interventions (such as vaccine doses) that can be stored by a health worker or clinic. If the amount of interventions in a new shipment and current stock exceeds this value, only the number of interventions specified by this value will be stored.

 
{
    "Max_Stock": 12
}

Node_Property_Restrictions

array of JSON objects

NA

NA

NA

A list of the NodeProperty key:value pairs, as defined in the demographics file, that nodes must have to be targeted by the intervention. See NodeProperties and IndividualProperties parameters for more information.

You can specify AND and OR combinations of key:value pairs with this parameter.

The following example restricts the intervention to nodes that are urban and medium risk or rural and low risk.

{
    "Node_Property_Restrictions": [
        {
            "Place": "URBAN",
            "Risk": "MED"
        },
        {
            "Place": "RURAL",
            "Risk": "LOW"
        }
    ]
}

Property_Restrictions

array of JSON objects

NA

NA

NA

A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information.

To specify AND and OR combinations of key:value pairs, use Property_Restrictions_Within_Node. You cannot use both of these parameters in the same event coordinator.

 
{
    "Property_Restrictions": [
        "Risk:HIGH"
    ]
}

Property_Restrictions_Within_Node

array of JSON objects

NA

NA

NA

A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information.

This parameter allows you to specify AND and OR combinations of key:value pairs. You may specify individual property restrictions using either this parameter or Property_Restrictions, but not both.

The following example restricts the intervention to individuals who are urban and high risk or urban and medium risk.

{
    "Property_Restrictions_Within_Node": [
        {
            "Risk": "HIGH",
            "Geographic": "URBAN"
        },
        {
            "Risk": "MEDIUM",
            "Geographic": "URBAN"
        }
    ]
}

Target_Age_Min

float

0

9.3228e+35

0

The lower end of ages targeted for an intervention, in years. Used when Target_Demographic is set to ExplicitAgeRanges or ExplicitAgeRangesAndGender.

 
{
    "Target_Age_Max": 20,
    "Target_Age_Min": 10,
    "Target_Demographic": "ExplicitAgeRanges"
}

Target_Demographic

enum

NA

NA

Everyone

The target demographic group. Possible values are:

  • Everyone

  • ExplicitAgeRanges

  • ExplicitAgeRangesAndGender

  • ExplicitGender

  • ExplicitDiseaseState

 
{
    "Target_Age_Max": 20,
    "Target_Age_Min": 10,
    "Target_Demographic": "ExplicitAgeRanges"
}

Target_Gender

enum

NA

NA

All

Specifies the gender restriction for the intervention. Possible values are:

  • Male

  • Female

  • All

 
{
    "Target_Gender": "Male"
}

Target_Residents_Only

boolean

0

1

0

When set to true (1), the intervention is only distributed to individuals that began the simulation in the node (i.e. those that claim the node as their residence).

 
{
    "Target_Residents_Only": 1
}

Trigger_Condition_List

array of strings

NA

NA

NA

The list of events that are of interest to the community health worker (CHW). If one of these events occurs, the individual or node is put into a queue to receive the CHW’s intervention. The CHW processes the queue when the event coordinator is updated. See Event list for possible values.

 
{
    "Trigger_Condition_List": [
        "ListenForEvent"
    ]
}

Waiting_Period

float

0

3.40282e+38

3.40E+38

The number of days a person or node can be in the queue waiting to get the intervention from the community health worker (CHW). If a person or node is in the queue, they will not be re-added to the queue if the event that would add them to the queue occurs. This allows them to maintain their priority, however they could be removed from the queue due to this waiting period.

 
{
    "Waiting_Period": 15
}

Targeting_Config

JSON object

NA

NA

NA

Be more selective of individuals by using the Targeting_Config classes.

 
{
    "Targeting_Config": {
        "class": "HasIntervention",
        "Is_Equal_To": 0,
        "Intervention_Name": "MyVaccine"
    }
}
ImportPressure

The ImportPressure intervention class extends the ImportCases outbreak event. Rather than importing a deterministic number of cases on a scheduled day, ImportPressure applies a set of per-day rates of importation of infected individuals, over a corresponding set of durations. ImportPressure inherits from Outbreak; the Antigen and Genome parameters are defined as they are for all Outbreak events.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Daily_Import_Pressures

array of floats

0

3.40282e+38

0

The rate of per-day importation for each node that the intervention is distributed to.

 
{
    "Intervention_Config": {
        "Antigen": 0,
        "Genome": 0,
        "Durations": [
            100,
            100,
            100,
            100,
            100,
            100,
            100
        ],
        "Daily_Import_Pressures": [
            0.1,
            5.0,
            0.2,
            1.0,
            2.0,
            0.0,
            10.0
        ],
        "class": "ImportPressure"
    }
}

Durations

array of integers

0

2147480000.0

1

The durations over which to apply import pressure.

 
{
    "Intervention_Config": {
        "Antigen": 0,
        "Genome": 0,
        "Durations": [
            100,
            100,
            100,
            100,
            100,
            100,
            100
        ],
        "Daily_Import_Pressures": [
            0.1,
            5.0,
            0.2,
            1.0,
            2.0,
            0.0,
            10.0
        ],
        "class": "ImportPressure"
    }
}

Genome

integer

0

16777215

0

The genome ID (Genome) of the outbreak infection. Together with the clade ID (Clade) they are a unitary object representing a strain of infection, which allows for differentiation among infections. Values for Genome may range from -1 to 2Log2_Number_of_Genomes_per_Clade-1

Intervention distribution example:

{
    "Enable_Strain_Tracking": 1,
    "Events": [
        {
            "Event_Coordinator_Config": {
                "Demographic_Coverage": 0.001,
                "Intervention_Config": {
                    "Clade": 1,
                    "Genome": 3,
                    "IgnoreImmunity": 1,
                    "class": "OutbreakIndividual"
                },
                "Target_Demographic": "Everyone",
                "class": "StandardInterventionDistributionEventCoordinator"
            },
            "Event_Name": "OutbreakIndividual",
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 30,
            "class": "CampaignEvent"
        }
    ]
}

Import_Age

float

0

43800

365

The age (in days) of infected import cases.

 
{
    "Import_Age": 10000
}

Incubation_Period_Override

integer

-1

2147480000.0

-1

The incubation period, in days, that infected individuals will go through before becoming infectious. This value overrides the incubation period set in the configuration file. Set to -1 to honor the configuration parameter settings.

 
{
    "Incubation_Period_Override": 0
}

Number_Cases_Per_Node

integer

0

2147480000.0

1

The number of new cases of Outbreak to import (per node).

Note

This will increase the population and there is no control over demographics of these individuals.

 
{
    "Intervention_Config": {
        "Antigen": 0,
        "Genome": 0,
        "Outbreak_Source": "ImportCases",
        "Number_Cases_Per_Node": 10,
        "class": "Outbreak"
    }
}
IndividualImmunityChanger

The IndividualImmunityChanger intervention class acts essentially as a MultiEffectVaccine, with the exception of how the behavior is implemented. Rather than attaching a persistent vaccine intervention object to an individual’s intervention list (as a campaign-individual-multieffectboostervaccine does), the IndividualImmunityChanger directly alters the immune modifiers of the individual’s susceptibility object and is then immediately disposed of. Any immune waning is not governed by Waning effect classes, as MultiEffectVaccine is, but rather by the immunity waning parameters in the configuration file.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Boost_Acquire

float

0

1

0

Specifies the boosting effect on acquisition immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. This does not replace current immunity, it builds multiplicatively on top of it.

 
{
    "Boost_Acquire": 0.7
}

Boost_Mortality

float

0

1

0

Specifies the boosting effect on mortality immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. This does not replace current immunity, it builds multiplicatively on top of it.

 
{
    "Boost_Mortality": 1.0
}

Boost_Threshold_Acquire

float

0

1

0

Specifies how much acquisition immunity is required before the vaccine changes from a prime to a boost.

 
{
    "Boost_Threshold_Acquire": 0.0
}

Boost_Threshold_Mortality

float

0

1

0

Specifies how much mortality immunity is required before the vaccine changes from a prime to a boost.

 
{
    "Boost_Threshold_Mortality": 0.0
}

Boost_Threshold_Transmit

float

0

1

0

Specifies how much transmission immunity is required before the vaccine changes from a prime to a boost.

 
{
    "Boost_Threshold_Transmit": 0.0
}

Boost_Transmit

float

0

1

0

Specifies the boosting effect on transmission immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. This does not replace current immunity, it builds multiplicatively on top of it.

 
{
    "Boost_Transmit": 0.5
}

Cost_To_Consumer

float

0

999999

1

The unit cost per vaccine (unamortized).

 
{
    "Cost_To_Consumer": 10.0
}

Prime_Acquire

float

0

1

0

Specifies the priming effect on acquisition immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine.

 
{
    "Prime_Acquire": 0.1
}

Prime_Mortality

float

0

1

0

Specifies the priming effect on mortality immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine.

 
{
    "Prime_Mortality": 0.3
}

Prime_Transmit

float

0

1

0

Specifies the priming effect on transmission immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine.

 
{
    "Prime_Transmit": 0.2
}
MultiEffectBoosterVaccine

The MultiEffectBoosterVaccine intervention class is derived from MultiEffectVaccine and preserves many of the same parameters. Upon distribution and successful take, the vaccine’s effect in each immunity compartment (acquisition, transmission, and mortality) is determined by the recipient’s immune state. If the recipient’s immunity modifier in the corresponding compartment is above a user-specified threshold, then the vaccine’s initial effect will be equal to the corresponding priming parameter. If the recipient’s immune modifier is below this threshold, then the vaccine’s initial effect will be equal to the corresponding boost parameter. After distribution, the effect wanes, just like a MultiEffectVaccine. The behavior is intended to mimic biological priming and boosting.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Acquire_Config

JSON object

NA

NA

NA

The configuration for multi-effect vaccine acquisition. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Acquire_Config": {
        "Initial_Effect": 0.9,
        "Decay_Time_Constant": 2525,
        "class": "WaningEffectExponential"
    }
}

Boost_Acquire

float

0

1

0

Specifies the boosting effect on acquisition immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. This does not replace current immunity, it builds multiplicatively on top of it.

 
{
    "Boost_Acquire": 0.7
}

Boost_Mortality

float

0

1

0

Specifies the boosting effect on mortality immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. This does not replace current immunity, it builds multiplicatively on top of it.

 
{
    "Boost_Mortality": 1.0
}

Boost_Threshold_Acquire

float

0

1

0

Specifies how much acquisition immunity is required before the vaccine changes from a prime to a boost.

 
{
    "Boost_Threshold_Acquire": 0.0
}

Boost_Threshold_Mortality

float

0

1

0

Specifies how much mortality immunity is required before the vaccine changes from a prime to a boost.

 
{
    "Boost_Threshold_Mortality": 0.0
}

Boost_Threshold_Transmit

float

0

1

0

Specifies how much transmission immunity is required before the vaccine changes from a prime to a boost.

 
{
    "Boost_Threshold_Transmit": 0.0
}

Boost_Transmit

float

0

1

0

Specifies the boosting effect on transmission immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. This does not replace current immunity, it builds multiplicatively on top of it.

 
{
    "Boost_Transmit": 0.5
}

Cost_To_Consumer

float

0

999999

10

The unit cost per vaccine (unamortized).

 
{
    "Cost_To_Consumer": 10.0
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Intervention_Name

string

NA

NA

MultiEffectBoosterVaccine

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "MultiEffectBoosterVaccine",
        "Intervention_Name": "Tetanus booster shot"
    }
}

Mortality_Config

JSON object

NA

NA

NA

The configuration for multi-effect vaccine mortality. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Mortality_Config": {
        "Initial_Effect": 1.0,
        "Decay_Time_Constant": 2525,
        "class": "WaningEffectExponential"
    }
}

New_Property_Value

string

NA

NA

NA

An optional IndividualProperty key:value pair that will be assigned when the intervention is first updated. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Prime_Acquire

float

0

1

0

Specifies the priming effect on acquisition immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine.

 
{
    "Prime_Acquire": 0.1
}

Prime_Mortality

float

0

1

0

Specifies the priming effect on mortality immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine.

 
{
    "Prime_Mortality": 0.3
}

Prime_Transmit

float

0

1

0

Specifies the priming effect on transmission immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine.

 
{
    "Prime_Transmit": 0.2
}

Transmit_Config

JSON object

NA

NA

NA

The configuration for multi-effect vaccine transmission. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Transmit_Config": {
        "Initial_Effect": 0.6,
        "Decay_Time_Constant": 2525,
        "class": "WaningEffectExponential"
    }
}

Vaccine_Take

float

0

1

1

The rate at which delivered vaccines will successfully stimulate an immune response and achieve the desired efficacy. For example, if it is set to 0.9, there will be a 90 percent chance that the vaccine will start with the specified efficacy, and a 10 percent chance that it will have no efficacy at all.

 
{
    "Intervention_Config": {
        "class": "SimpleVaccine",
        "Cost_To_Consumer": 10,
        "Vaccine_Type": "AcquisitionBlocking",
        "Vaccine_Take": 1,
        "Efficacy_Is_Multiplicative": 0,
        "Waning_Config": {
            "class": "WaningEffectConstant",
            "Initial_Effect": 0.3
        }
    }
}
NodePropertyValueChanger

The NodePropertyValueChanger intervention class sets a given node property to a new value. You can also define a duration in days before the node property reverts back to its original value, the probability that a node will change its node property to the target value, and the number of days over which nodes will attempt to change their individual properties to the target value. This node-level intervention functions in a similar manner as the individual-level intervention, PropertyValueChanger.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Daily_Probability

float

0

1

1

The daily probability that the node’s property value changes to the Target_NP_Key_Value.

 
{
    "Intervention_Config": {
        "class": "PropertyValueChanger",
        "Disqualifying_Properties": [],
        "New_Property_Value": "",
        "Target_Property_Key": "Risk",
        "Target_Property_Value": "LOW",
        "Daily_Probability": 1.0,
        "Maximum_Duration": 0,
        "Revert": 0
    }
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of NodeProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to nodes with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Intervention_Name

string

NA

NA

NodePropertyValueChanger

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Name": "Diagnostic_Sample"
}

Maximum_Duration

float

-1

3.40E+38

3.40E+38

The maximum amount of time in days nodes have to update the property value. This timing works in conjunction with Daily_Probability.

 
{
    "Intervention_Config": {
        "class": "PropertyValueChanger",
        "Disqualifying_Properties": [],
        "New_Property_Value": "",
        "Target_Property_Key": "Risk",
        "Target_Property_Value": "LOW",
        "Daily_Probability": 1.0,
        "Maximum_Duration": 0,
        "Revert": 0
    }
}

New_Property_Value

string

NA

NA

NA

An optional NodeProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Revert

float

0

10000

0

The number of days before the node’s property value reverts back to the initial node property value.

 
{
    "Intervention_Config": {
        "class": "PropertyValueChanger",
        "Disqualifying_Properties": [],
        "New_Property_Value": "",
        "Target_Property_Key": "Risk",
        "Target_Property_Value": "LOW",
        "Daily_Probability": 1.0,
        "Maximum_Duration": 0,
        "Revert": 0
    }
}

Target_NP_Key_Value

string

NA

NA

NA

The NodeProperty key:value pair, as defined in the demographics file, to assign to the node.

 
{
    "Target_NP_Key_Value": "InterventionStatus:NONE"
}
SimpleBoosterVaccine

The SimpleBoosterVaccine intervention class is derived from SimpleVaccine and preserves many of the same parameters. The behavior is much like SimpleVaccine, except that upon distribution and successful take, the vaccine’s effect is determined by the recipient’s immune state. If the recipient’s immunity modifier in the corresponding channel (acquisition, transmission, or mortality) is above a user-specified threshold, then the vaccine’s initial effect will be equal to the corresponding priming parameter. If the recipient’s immune modifier is below this threshold, then the vaccine’s initial effect will be equal to the corresponding boosting parameter. After distribution, the effect wanes, just like SimpleVaccine. In essence, this intervention provides a SimpleVaccine intervention with one effect to all naive (below- threshold) individuals, and another effect to all primed (above-threshold) individuals; this behavior is intended to mimic biological priming and boosting.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Boost_Effect

float

0

1

1

Specifies the boosting effect on [acquisition/transmission/mortality] immunity for previously exposed individuals (either natural or vaccine-derived). This does not replace current immunity, it builds multiplicatively on top of it.

 
{
    "Intervention_Config": {
        "Cost_To_Consumer": 10.0,
        "Vaccine_Take": 1,
        "Vaccine_Type": "MortalityBlocking",
        "Prime_Effect": 0.25,
        "Boost_Effect": 0.45,
        "Boost_Threshold": 0.0,
        "Waning_Config": {
            "Box_Duration": 10,
            "Initial_Effect": 1,
            "class": "WaningEffectBox"
        },
        "class": "SimpleBoosterVaccine"
    }
}

Boost_Threshold

float

0

1

0

Specifies how much immunity is required before the vaccine changes from a priming effect to a boosting effect.

 
{
    "Intervention_Config": {
        "Cost_To_Consumer": 10.0,
        "Vaccine_Take": 1,
        "Vaccine_Type": "MortalityBlocking",
        "Prime_Effect": 0.25,
        "Boost_Effect": 0.45,
        "Boost_Threshold": 0.0,
        "Waning_Config": {
            "Box_Duration": 10,
            "Initial_Effect": 1,
            "class": "WaningEffectBox"
        },
        "class": "SimpleBoosterVaccine"
    }
}

Cost_To_Consumer

float

0

999999

10

The unit cost per vaccine (unamortized).

 
{
    "Cost_To_Consumer": 10.0
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Efficacy_Is_Multiplicative

boolean

NA

NA

1

The overall vaccine efficacy when individuals receive more than one vaccine. When set to true (1), the vaccine efficacies are multiplied together; when set to false (0), the efficacies are additive.

 
{
    "Intervention_Config": {
        "class": "SimpleVaccine",
        "Cost_To_Consumer": 10,
        "Vaccine_Type": "AcquisitionBlocking",
        "Vaccine_Take": 1,
        "Efficacy_Is_Multiplicative": 0,
        "Waning_Config": {
            "class": "WaningEffectConstant",
            "Initial_Effect": 0.3
        }
    }
}

Intervention_Name

string

NA

NA

SimpleBoosterVaccine

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "SimpleBoosterVaccine",
        "Intervention_Name": "Tetanus booster shot"
    }
}

New_Property_Value

string

NA

NA

NA

An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Prime_Effect

float

0

1

1

Specifies the priming effect on [acquisition/transmission/mortality] immunity for naive individuals (without natural or vaccine-derived immunity).

 
{
    "Intervention_Config": {
        "Cost_To_Consumer": 10.0,
        "Vaccine_Take": 1,
        "Vaccine_Type": "MortalityBlocking",
        "Prime_Effect": 0.25,
        "Boost_Effect": 0.45,
        "Boost_Threshold": 0.0,
        "Waning_Config": {
            "Box_Duration": 10,
            "Initial_Effect": 1,
            "class": "WaningEffectBox"
        },
        "class": "SimpleBoosterVaccine"
    }
}

Vaccine_Take

float

0

1

1

The rate at which delivered vaccines will successfully stimulate an immune response and achieve the desired efficacy. For example, if it is set to 0.9, there will be a 90 percent chance that the vaccine will start with the specified efficacy, and a 10 percent chance that it will have no efficacy at all.

 
{
    "Intervention_Config": {
        "class": "SimpleVaccine",
        "Cost_To_Consumer": 10,
        "Vaccine_Type": "AcquisitionBlocking",
        "Vaccine_Take": 1,
        "Efficacy_Is_Multiplicative": 0,
        "Waning_Config": {
            "class": "WaningEffectConstant",
            "Initial_Effect": 0.3
        }
    }
}

Vaccine_Type

enum

NA

NA

Generic

The type of vaccine to distribute in a vaccine intervention. Possible values are:

Generic

The vaccine can reduce transmission, acquisition, and mortality.

TransmissionBlocking

The vaccine will reduce pathogen transmission.

AcquisitionBlocking

The vaccine will reduce the acquisition of the pathogen by reducing the force of infection experienced by the vaccinated individual.

MortalityBlocking

The vaccine reduces the disease-mortality rate of a vaccinated individual.

 
{
    "Intervention_Config": {
        "class": "SimpleVaccine",
        "Cost_To_Consumer": 10,
        "Vaccine_Type": "AcquisitionBlocking",
        "Vaccine_Take": 1,
        "Efficacy_Is_Multiplicative": 0,
        "Waning_Config": {
            "class": "WaningEffectConstant",
            "Initial_Effect": 0.3
        }
    }
}

Waning_Config

JSON object

NA

NA

NA

The configuration of how the intervention efficacy wanes over time. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Waning_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 1,
        "class": "WaningEffectBox"
    }
}
UsageDependentBednet (malaria)

The UsageDependentBednet intervention class is similar to SimpleBednet, as it distributes insecticide-treated nets to individuals in the simulation. However, bednet ownership and bednet usage are distinct in this intervention. As in SimpleBednet, net ownership is configured through the demographic coverage, and the blocking and killing rates of mosquitoes are time-dependent. Use of bednets is age-dependent and can vary seasonally. Once a net has been distributed to someone, the net usage is determined by the product of the seasonal and age-dependent usage probabilities until the net-retention counter runs out, and the net is discarded.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Blocking_Config

JSON object

NA

NA

NA

Configures the rate of blocking for indoor mosquito feeds on individuals with an ITN. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Blocking_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0,
        "class": "WaningEffectBox"
    }
}

Cost_To_Consumer

float

0

999999

3.75

The unit cost per bednet (unamortized)

 
{
    "Cost_To_Consumer": 4.5
}

Discard_Event

enum

NA

NA

NA

The event that is broadcast when an individual discards their bed net, either by replacing an existing net or due to the expiration timer. See Event list for possible values, or you can use a custom event (defined in the Custom_Individudual_Events list in the config file).

 
{
    "Received_Event": "Bednet_Got_New_One",
    "Using_Event": "Bednet_Using",
    "Discard_Event": "Bednet_Discarded",
    "Expiration_Distribution_Type": "FIXED_DURATION",
    "Expiration_Period": 50
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Expiration_Period_Constant

float

0

3.40282E+38

-1

The expiration period to use for all bednets, in days, when Expiration_Period_Distribution is set to CONSTANT_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "CONSTANT_DISTRIBUTION",
    "Expiration_Period_Constant": 8
}

Expiration_Period_Distribution

enum

NA

NA

NOT_INITIALIZED

The distribution type to use for assigning the expiration period to a usage-dependent bednet. Each assigned value is a random draw from the distribution.

Possible values are:

NOT_INITIALIZED

No distribution set.

CONSTANT_DISTRIBUTION

Use the same value for each individual. Set Expiration_Period_Constant.

UNIFORM_DISTRIBUTION

Use a uniform distribution with a given minimum and maximum. Set Expiration_Period_Max and Expiration_Period_Min.

GAUSSIAN_DISTRIBUTION

The distribution is Gaussian (or normal). Values are resampled to ensure >= 0. Set Expiration_Period_Gaussian_Mean and Expiration_Period_Gaussian_Std_Dev.

EXPONENTIAL_DISTRIBUTION

The distribution is exponential with a given mean. Set Expiration_Period_Exponential.

WEIBULL_DISTRIBUTION

Use a Weibull distribution with a given shape and scale. Set Expiration_Period_Kappa and Expiration_Period_Lambda.

LOG_NORMAL_DISTRIBUTION

Use a log-normal distribution with a given mean and width. Set Expiration_Period_Log_Normal_Mu and Expiration_Period_Log_Normal_Sigma.

POISSON_DISTRIBUTION

Use a Poisson distribution with a given mean. Set Expiration_Period_Poisson_Mean.

DUAL_CONSTANT_DISTRIBUTION

Use a distribution where some individuals are set to a value of zero and the rest to a given value. Set Expiration_Period_Proportion_0 and Peak_2_Value. This distribution does not use the parameters set for CONSTANT_DISTRIBUTION.

DUAL_EXPONENTIAL_DISTRIBUTION

Use two exponential distributions with given means. Set Expiration_Period_Mean_1, Expiration_Period_Mean_2, and Expiration_Period_Proportion_1. This distribution does not use the parameters set for EXPONENTIAL_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Duration_Before_Leaving_Log_Normal_Mu": 9,
    "Duration_Before_Leaving_Log_Normal_Sigma": 2
}

Expiration_Period_Exponential

float

0

3.40282E+38

-1

The mean of the expiration period, in days, when Expiration_Period_Distribution is set to EXPONENTIAL_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "EXPONENTIAL_DISTRIBUTION",
    "Expiration_Period_Exponential": 4.25
}

Expiration_Period_Gaussian_Mean

float

0

3.40282E+38

-1

The mean of the expiration period, in days, when Expiration_Period_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Expiration_Period_Gaussian_Mean": 8,
    "Expiration_Period_Gaussian_Std_Dev": 1.5
}

Expiration_Period_Gaussian_Std_Dev

float

1.17549E-38

3.40282E+38

-1

The standard deviation of the expiration period, in days, when Expiration_Period_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Expiration_Period_Gaussian_Mean": 8,
    "Expiration_Period_Gaussian_Std_Dev": 1.5
}

Expiration_Period_Kappa

float

1.17549E-38

3.40282E+38

-1

The shape value for the expiration period, in days, when Expiration_Period_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "WEIBULL_DISTRIBUTION",
    "Expiration_Period_Kappa": 0.9,
    "Expiration_Period_Lambda": 1.5
}

Expiration_Period_Lambda

float

1.17549E-38

3.40282E+38

-1

The scale value for the expiration period, in days, when Expiration_Period_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "WEIBULL_DISTRIBUTION",
    "Expiration_Period_Kappa": 0.9,
    "Expiration_Period_Lambda": 1.5
}

Expiration_Period_Log_Normal_Mu

float

-3.40282e+38

1.70141e+38

3.40282E+38

The mean of the expiration period, in days, when Expiration_Period_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Expiration_Period_Log_Normal_Mu": 9,
    "Expiration_Period_Log_Normal_Sigma": 2
}

Expiration_Period_Log_Normal_Sigma

float

-3.40282e+38

1.70141e+38

3.40282E+38

The width of the expiration period, in days, when Expiration_Period_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Expiration_Period_Log_Normal_Mu": 9,
    "Expiration_Period_Log_Normal_Sigma": 2
}

Expiration_Period_Max

float

0

3.40282E+38

-1

The maximum expiration period, in days, when Expiration_Period_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "UNIFORM_DISTRIBUTION",
    "Expiration_Period_Min": 2,
    "Expiration_Period_Max": 7
}

Expiration_Period_Mean_1

float

1.17549E-38

3.40282E+38

-1

The mean of the first exponential distribution, in days, when Expiration_Period_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Expiration_Period_Mean_1": 4,
    "Expiration_Period_Mean_2": 12,
    "Expiration_Period_Proportion_1": 0.2
}

Expiration_Period_Mean_2

float

1.17549E-38

3.40282E+38

-1

The mean of the second exponential distribution, in days, when Expiration_Period_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Expiration_Period_Mean_1": 4,
    "Expiration_Period_Mean_2": 12,
    "Expiration_Period_Proportion_1": 0.2
}

Expiration_Period_Min

float

0

3.40282E+38

-1

The minimum expiration period, in days, when Expiration_Period_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "UNIFORM_DISTRIBUTION",
    "Expiration_Period_Min": 2,
    "Expiration_Period_Max": 7
}

Expiration_Period_Peak_2_Value

float

0

3.40282E+38

-1

The expiration period value to assign to the remaining bednets, in days, when Expiration_Period_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Expiration_Period_Proportion_0": 0.25,
    "Expiration_Period_Peak_2_Value": 5
}

Expiration_Period_Poisson_Mean

float

0

3.40282E+38

-1

The mean of the expiration period, in days, when Expiration_Period is set to POISSON_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "POISSON_DISTRIBUTION",
    "Expiration_Period_Poisson_Mean": 5
}

Expiration_Period_Proportion_0

float

0

1

-1

The proportion of bednets to assign a value of zero days until expiration when Expiration_Period_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Expiration_Period_Proportion_0": 0.25,
    "Expiration_Period_Peak_2_Value": 5
}

Expiration_Period_Proportion_1

float

0

1

-1

The proportion of bednets in the first exponential distribution.

 
{
    "Expiration_Period_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Expiration_Period_Mean_1": 4,
    "Expiration_Period_Mean_2": 12,
    "Expiration_Period_Proportion_1": 0.2
}

Insecticide_Name

string

NA

NA

UNINITIALIZED STRING

The name of the insecticide defined in <config.Insecticides> for this intervention. If insecticides are being used, this must be defined as one of those values; if they are not being used this can be empty. It cannot have a value if you did not configure <config.Insecticides>.

 
{
    "Insecticide_Name": "carbamate"
}

Intervention_Name

string

NA

NA

UsageDependentBednet

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "UsageDependentBednet",
        "Intervention_Name": "IRS bednet that wears over time"
    }
}

Killing_Config

JSON object

NA

NA

NA

The configuration of the rate at which mosquitoes die, conditional on a successfully blocked feed. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Killing_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0.53429,
        "class": "WaningEffectBox"
    }
}

New_Property_Value

string

NA

NA

NA

An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Received_Event

enum

NA

NA

NA

This parameter broadcasts when a new net is received, either the first net or a replacement net. See Event list for possible values, or you can use a custom event (defined in the Custom_Individudual_Events list in the config file).

 
{
    "Received_Event": "Bednet_Got_New_One",
    "Using_Event": "Bednet_Using",
    "Discard_Event": "Bednet_Discarded"
}

Repelling_Config

JSON object

NA

NA

NA

Defines a waning effect that determines the probability that a vector is repelled due to the intervention. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Repelling_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0,
        "class": "WaningEffectBox"
    }
}

Usage_Config_List

array of JSON objects

NA

NA

NA

The list of WaningEffects whose effects are multiplied together to get the usage effect. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Usage_Config_List": [
        {
            "class": "WaningEffectConstant",
            "Initial_Effect": 1.0
        }
    ]
}

Using_Event

enum

NA

NA

NA

This parameter broadcasts each time step in which a bed net is used. See Event list for possible values, or you can use a custom event (defined in the Custom_Individudual_Events list in the config file).

 
{
    "Received_Event": "Bednet_Got_New_One",
    "Using_Event": "Bednet_Using",
    "Discard_Event": "Bednet_Discarded"
}
Malaria model

Several habitat parameters in the malaria model have been upgraded, creating more flexibility in the model and enabling the user to have more control over habitat customization. These changes allow the model to more accurately capture real-world habitat availability and how it affects different mosquito species.

LINEAR_SPLINE habitat type

This new option under Larval_Habitat_Types increases model customization by allowing the user to specify an arbitrary functional form (derived from data) for larval habitat availability throughout the year, instead of relying on climatological parameters such as rainfall or temperature. For more information, see Larval habitat parameters.

LarvalHabitatMultiplier by species

This new feature in demographics allows larval habitat availability to be modified independently for each species sharing a particular habitat type. Prior versions of EMOD applied the same modifier to all species within a shared habitat; this upgrade enables you to apply modifiers to individual species within a habitat. For more information, see NodeAttributes parameters.

ScaleLarvalHabitat by species

This new intervention enables species-specific effects of habitat interventions within shared habitat types, such that habitat availability is modified on a per-species level.

Breaking changes

PIECEWISE_MONTHLY has been changed to LINEAR_SPLINE.

EMOD installation

Epidemiological MODeling software (EMOD) can be installed on computers running Windows (Windows 10, Windows Server 12, and Windows HPC Server 12 (64-bit)) or Linux (CentOS 7.1). For either operating system, there are relatively few prerequisites required and the EMOD executable (Eradication.exe) or Eradication binary for Linux is pre-built from the latest EMOD source code release. After completing installation, you can run simulations locally or on an HPC cluster using real-world data.

Note

If you want to download and modify the EMOD source code and build the Eradication.exe or Eradication binary for Linux yourself, see EMOD source code installation.

Install EMOD on Windows

To install EMOD on Windows computers, follow the steps below. You will install the pre-built Eradication.exe and all software necessary to run simulations locally. Optionally, you can install Python virtual environments, software to plot the output of simulations, and EMOD input files for various regions.

The EMOD executable (Eradication.exe) is tested using Windows 10, Windows Server 12, and Windows HPC Server 12 (64-bit). Windows HPC Server is used for testing remote simulations on a high-performance computing (HPC) cluster and the other Windows operating systems are used to test local simulations.

Note

If you want to download and modify the EMOD source code and build the Eradication.exe yourself, see EMOD source code installation.

Install EMOD

  1. Install the Microsoft HPC Pack 2019 Client Utilities Redistributable Package (64-bit). See https://www.microsoft.com/en-us/download/details.aspx?id=101361 for instructions.

  2. Install the Microsoft MPI v10. See https://www.microsoft.com/en-us/download/details.aspx?id=100593 for instructions, being sure to run the MSMpiSetup.exe file.

  3. Install the Microsoft Visual C++ 2022 Redistributable (64-bit). See https://docs.microsoft.com/en-us/cpp/windows/latest-supported-vc-redist for instructions.

  4. On GitHub on the EMOD releases page, download the EMOD executable (Eradication.exe). Save to a local drive, such as the desktop.

Warning

Double-clicking Eradication.exe will not run the EMOD software or launch an installer. You must run EMOD from the command line or a script. See Running a simulation for more information.

(Optional) Install plotting software

None of the following plotting software is required to run simulations with EMOD, but it is useful for creating graphs from and evaluating the model output. In addition, EMOD provides many Python scripts for analyzing data.

Note

IDM does not provide support or guarantees for any third-party software, even software that we recommend you install. Send feedback if you encounter any issues, but any support must come from the makers of those software packages and their user communities.

Python and Python packages

Python is required to install many of the software packages described below.

  1. In a web browser, go to https://www.python.org/downloads/release/python-399/ to install Python 3.9 64-bit.

  2. Scroll down and download one of the x86-64 bit installers (you may use the executable installer or the web-based installer.)

  3. Double-click the executable file and, in the installer window, select the Add Python 3.9 to PATH checkbox and click Customize installation.

  4. On the Optional Features window that opens, leave all default values selected and click Next. The Python package manager, pip, is used to install other Python packages.

  5. If you are running EMOD locally, IDM recommends that you select the Advanced Options window to customize the installation directory to “C:\Python39”.

    You may install Python in another location, but the Python plotting scripts included in the EMOD scenarios zip file assume that Python is installed directly under the C: drive. If you install it elsewhere, you may need to edit those scripts when using the scenarios to learn about EMOD functionality.

  6. Click Install. When installation is complete, click Close.

  7. To verify installation, open a Command Prompt window and type the following:

    python --version
Python virtual environments

Python virtual environments enable you to isolate your Python environments from one another and give you the option to run multiple versions of Python on the same computer. When using a virtual environment, you can indicate the version of Python you want to use and the packages you want to install, which will remain separate from other Python environments. You may use virtualenv, but venv is recommended and included with Python 3.3+.

  1. Navigate to the location where you want to create the virtual environment directory and enter the following to create a environment called “idm”, or other desired name:

    python -m venv idm

  2. To activate the virtual environment, enter the following:

    idm\Scripts\activate

  3. Verify that the command prompt displays “idm” at the beginning, which indicates that you are in the idm virtual environment. For example:

    (idm) my-computer:idm

  4. To verify you are using Python 3.9 64-bit, enter the following:

    python --version

  5. When you are done working in this virtual environment, deactivate the environment. The environment will be saved and you can reactivate it at any time. To deactivate, enter the following:

    deactivate
NumPy

We recommended that you download some of the NumPy Python package from http://www.lfd.uci.edu/~gohlke/pythonlibs, a page compiled by Christoph Gohlke, University of California, Irvine. The libraries there include linear algebra packages that are not included by default with the standard Windows packages. They are compiled Windows binaries, including the 64-bit versions required by EMOD. The naming convention used lists the Python version after “cp”, for example “cp39-cp39m”, and the Windows bit version after “win”, for example “win_amd64”.

The NumPy package adds support for large, multi-dimensional arrays and matrices to Python.

  1. Go to http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy and select the WHL file for NumPy 1.22.1 (64-bit) that is compatible with Python 3.9 64-bit.

  2. Save the file to your Python installation directory.

  3. Open a Command Prompt window and navigate to the Python installation directory, then enter the following, substituting the name of the specific NumPy WHL file you downloaded:

    pip install numpy-1.x.x+mkl-cp39-cp39m-win_amd64.whl
Python packages

The Python packages dateutil, six, and pyparsing provide text parsing and datetime functionality.

Note

Be sure NumPy is installed before you install Matplotlib.

  1. Open a Command Prompt window and enter the following:

    pip install python-dateutil
pip install pyparsing
pip install matplotlib
pip install future
R

R is a free software environment for statistical computing and graphics.

MATLAB

MATLAB is a high-level language and interactive environment for numerical computation, visualization, and programming. The MATLAB Statistics and Machine Learning Toolbox™ provides functions and applications to describe, analyze and model data using statistics and machine learning algorithms.

  1. Go to http://www.mathworks.com/products/matlab/ and install MATLAB R2021b.

  2. If desired, go to https://www.mathworks.com/products/statistics.html and install the MATLAB Statistics and Machine Learning Toolbox™ R2021b.

(Optional) Download input files

IDM provides input files that describe the demographics, migration patterns, and climate of many different locations across the world. You can download these files from the EMOD-InputData repository, which uses large file storage (LFS) to manage the binaries and large JSON (JavaScript Object Notation) files. A standard Git clone of the repository will only retrieve the metadata for these files managed with LFS. To retrieve the actual data, follow the steps below.

  1. Install the Git LFS plugin, if it is not already installed.

    • For Windows users, download the plugin from https://git-lfs.github.com.

    • For CentOS users, the plugin is included with the PrepareLinuxEnvironment.sh script.

  2. Using a Git client or Command Prompt window, clone the input data repository to retrieve the metadata:

    git clone https://github.com/InstituteforDiseaseModeling/EMOD-InputData.git

  3. Navigate to the directory where you downloaded the metadata for the input files.

  4. Download the actual data on your local machine:

    git lfs fetch

  5. Replace the metadata in the files with the actual data:

    git lfs checkout

Install EMOD on Linux

To install EMOD on Linux computers, follow the steps below. You will install the pre-built Eradication binary for Linux and all supporting software needed to run simulations locally. Optionally, you can install Python virtual environments, software to plot the output of simulations, and EMOD input files for various regions.

The Eradication binary for Linux is tested and supported on a CentOS 7.1 virtual machine on Azure. It has also been successfully built and run on Ubuntu, SUSE, and Arch, but has not been tested and is not supported on those Linux distributions.

Note

If you want to download and modify the EMOD source code and build the Eradication binary for Linux yourself, see EMOD source code installation.

Install EMOD using a script

The setup script installs most prerequisite software, including Python and the Python packages dateutil, six, pyparsing, NumPy, and matplotlib. Other prerequisites, such as Boost 1.77.0 and Microsoft MPI v10, are declared by the script as required. Because the installation instructions for these packages will vary based on the particular Linux distribution you are running, installation instructions are not included here.

Note

IDM does not provide support or guarantees for any third-party software, even software that we recommend you install. Send feedback if you encounter any issues, but any support must come from the makers of those software packages and their user communities.

The script provides the option to install input files that describe the demographics, migration patterns, and climate of many different locations across the world. While the script installs a pre-built version of the Eradication binary for Linux, it also provides the option of installing the EMOD source code. For information on building the Eradication binary for Linux from source code, see EMOD source code installation.

Before you begin, you must have the following:

  • sudo privileges to install packages

  • 15 GB free in your home directory (if you install the EMOD source code and input files)

  • An Internet connection

  1. On GitHub on the EMOD releases page, download and run the PrepareLinuxEnvironment.sh script.

    Respond to the prompts for information while the script is running.

    1. Set the EMOD_ROOT environment variable to the path to the EMOD source path:

      EMOD_ROOT=~/IDM/EMOD

    2. Include Scripts and . in the path:

      export PATH=$PATH:.:$EMOD_ROOT/Scripts

    3. If you want to run simulations in the same session that you updated EMOD_ROOT and the Scripts path, reload the .bashrc file using source .bashrc.

  1. Download the Eradication binary for Linux for CentOS 7.1 (not Eradication.exe for Windows). See on EMOD releases on GitHub. Save to a local drive, such as the desktop.

(Optional) Install Python virtual environments

Python virtual environments enable you to isolate your Python environments from one another and give you the option to run multiple versions of Python on the same computer. When using a virtual environment, you can indicate the version of Python you want to use and the packages you want to install, which will remain separate from other Python environments. You may use virtualenv, but venv is recommended and included with Python 3.3+.

  1. Navigate to the location where you want to create the virtual environment directory and enter the following to create a environment called “idm”, or other desired name:

    python -m venv idm

  2. To activate the virtual environment, enter the following:

    source idm/bin/activate

  3. Verify that the command prompt displays “idm” at the beginning, which indicates that you are in the idm virtual environment. For example:

    (idm) my-computer:idm

  4. To verify you are using Python 3.9 64-bit, enter the following:

    python --version

  5. When you are done working in this virtual environment, deactivate the environment. The environment will be saved and you can reactivate it at any time. To deactivate it, enter the following:

    deactivate
(Optional) Install plotting software

None of the following plotting software is required to run simulations with EMOD, but they are useful for creating graphs from and evaluating the model output. In addition, EMOD provides many Python scripts for analyzing data.

R

R is a free software environment for statistical computing and graphics.

MATLAB

MATLAB is a high-level language and interactive environment for numerical computation, visualization, and programming. The MATLAB Statistics and Machine Learning Toolbox™ provides functions and applications to describe, analyze and model data using statistics and machine learning algorithms.

  1. Go to http://www.mathworks.com/products/matlab/ and install MATLAB R2021b.

  2. If desired, go to https://www.mathworks.com/products/statistics.html and install the MATLAB Statistics and Machine Learning Toolbox™ R2021b.

(Optional) Download input files

IDM provides input files that describe the demographics, migration patterns, and climate of many different locations across the world. You can download these files from the EMOD-InputData repository, which uses large file storage (LFS) to manage the binaries and large JSON (JavaScript Object Notation) files. A standard Git clone of the repository will only retrieve the metadata for these files managed with LFS. To retrieve the actual data, follow the steps below.

  1. Install the Git LFS plugin, if it is not already installed.

    • For Windows users, download the plugin from https://git-lfs.github.com.

    • For CentOS users, the plugin is included with the PrepareLinuxEnvironment.sh script.

  2. Using a Git client or Command Prompt window, clone the input data repository to retrieve the metadata:

    git clone https://github.com/InstituteforDiseaseModeling/EMOD-InputData.git

  3. Navigate to the directory where you downloaded the metadata for the input files.

  4. Download the actual data on your local machine:

    git lfs fetch

  5. Replace the metadata in the files with the actual data:

    git lfs checkout

Overview of EMOD software

The Institute for Disease Modeling (IDM) develops detailed simulations of disease transmission through the use of quantitative software modeling. The agent-based model, Epidemiological MODeling software (EMOD), helps determine the combination of health policies and intervention strategies that can lead to disease eradication. EMOD calculates how diseases may spread in particular areas and is used to analyze the effects of current and future health policies and intervention strategies. It supports infectious disease campaign planning, data gathering, new product development, and policy decisions. IDM shares this modeling software with the research community to advance the understanding of disease dynamics.

EMOD is quite different from a deterministic compartmental model, which uses an ordinary differential equation (ODE) to determine the rates at which proportions of the population move from each compartment (or disease state) to another. EMOD is an agent-based model (ABM) that simulates the simultaneous interactions of agents in an effort to recreate complex phenomena. Each agent, whether it is a human or a vector, can be assigned a variety of properties to represent age, gender, parasite density, and much more. Their behavior and interactions with one another are determined by using decision rules based on their properties. These models have strong predictive power and are able to leverage spatial and temporal dynamics.

EMOD is also stochastic, meaning that there is randomness built into the model. Infection and recovery processes are represented as probabilistic Bernoulli random draws. In other words, when a susceptible person comes into contact with a pathogen, they are not guaranteed to become infected. Instead, you can imagine flipping a coin that has a λ chance of coming up tails S(t) times and every person who gets a head is considered infected. This randomness better approximates what happens in reality. It also means that you must run many simulations to determine the probability of particular outcomes.

Simulation types

EMOD supports the following simulation types for modeling a variety of diseases:

  • Generic disease (GENERIC_SIM), which can be used for modeling a variety of diseases such as influenza or measles

  • Vector-borne diseases (VECTOR_SIM), which can be used for modeling vector-borne diseases such as dengue

  • Malaria (MALARIA_SIM), which adds features specific to malaria biology and treatment

  • Tuberculosis with HIV coinfection (TBHIV_SIM), which can be used for modeling TB transmission, with the option to add HIV coinfection as a contributing factor

  • Sexually transmitted infections (STI_SIM), which adds features for sexual relationship networks

  • HIV (HIV_SIM), which adds features specific to HIV biology and treatment

  • Environmental transmission (ENVIRONMENTAL_SIM), which adds features for diseases transmitted through contaminated food or water

  • Typhoid (TYPHOID_SIM), which adds features specific to typhoid biology and treatment

The illustration below shows how the simulation types are built upon one another. With a few exceptions, all parameters available to configure the simulation in the generic simulation type are inherited by the vector simulation type. The vector simulation type adds additional parameters specific to the biology of vector-borne diseases, which in turn are inherited by the malaria simulation type, which adds parameters specific to malaria biology and treatment, and so on. Therefore, depending on the simulation type you select, different parameters are available for you to use. In addition, simulation types for broader classes of disease can be extended to build your own disease-specific model.

_images/simulation-types.png

Simulation type inheritance

EMOD utilizes object-oriented programming design principles such as interfaces, factories, and observers to achieve a highly modular architecture, thus enabling comparisons of structural assumptions and different levels of model detail. For example, parameters specific to the intrahost biology of disease progression and to behavioral propensities that drive health care seeking have been separated into different inheritance classes. Thus, modules or sub-modules can be interchanged while leaving other portions of the model intact.

EMOD can produce statistically significant results over a broad set of parameters and scenarios. One strength of EMOD is the ability to incorporate interventions aimed at controlling or eradicating disease. Quantitative analysis of the simulated output enables disease eradication efforts to be more driven by data. The IDM research team has published many articles related to modeling and the modeling concepts underpinning EMOD. For a list of published articles, see IDM Publications.

This section provides an overview of EMOD and files needed to run simulations. The architecture diagram below shows, at a high level, how the system functions. If you run simulations in parallel on a multi-node cluster, there is also a Message Passing Interface (MPI) component used to pass data between multiple instances of EMOD.

_images/architecture.png

High-level EMOD system architecture

Input files

EMOD takes a number of files as inputs for running simulations. The input files indicate the transmission mode, population size, age distribution, disease interventions, and many more qualities that affect disease transmission. The EMOD executable (Eradication.exe) takes these input files and uses them to run a simulation of the disease dynamics.

Many of the input files are formatted as JSON (JavaScript Object Notation) files. JSON is a simple text format that uses key/value pairs to encode data. In EMOD, the key is a parameter name and the value is the setting for that parameter. For example, “Base_Incubation_Period”: 5 sets the incubation period for the disease being modeled to be five days. JSON files are easy to read and edit. For more information about JSON, see EMOD parameter reference. Some of the inputs are compiled binary files.

Primary files

The primary files used for EMOD simulations are the following:

demographics file

Defines the geography and the population being modeled. Each geographic node has a location, population, and certain characteristics assigned to it. These files are often reused across multiple simulations and disease scenarios.

configuration file

Defines many aspects of the simulation, including disease characteristics like infectivity and transmission mode and simulation characteristics like simulation length and additional input files to use.

campaign file

Defines the events that occur during the simulation. Primarily, these are the various disease interventions that will take place, but they also include the disease outbreaks.

Supplementary files

There are other optional files that EMOD can use as inputs to the simulation. These files are not necessary for every simulation or every disease. For example, climate files are only used for vector-borne diseases because weather affects mosquito populations. Migration files are only used for multi-node simulations where human or vector movement is important. Because EMOD is stochastic, it requires running many simulations which may require a lot of processing power. Load-balancing and serialization files are for managing computing resources.

These files use both a JSON file for metadata and an associated binary file that contains the actual data. You will typically use these input files in their default state.

Input file IDs

The Institute for Disease Modeling (IDM) provides collections of climate and demographics files for many different locations in the world for download on GitHub. For instructions, see EMOD installation. Some input files were prepared using CIESIN Gridded Population of the World (GPW) population distribution and a corresponding spatial resolution grid (for example, 2.5 arc minutes) to define the initial population and extent of the nodes for country-wide input files. Therefore, the naming convention for this files usually leads with the geographic location, followed by the spatial resolution, and input file type.

All input files except configuration and campaign files include the parameter IdReference in the metadata, which is used to generate the NodeID associated with each node in a simulation. The values for IdReference and NodeID must be the same across all input files used in a simulation. See Demographics parameters for more information about NodeID generation.

Demographics file

Demographics files are JSON-formatted files that contain information on the demographics of the population in each node in the simulation. For example, the longitude and latitude, the number of individuals, and the distribution for age, gender, immunity, risk, and mortality. Once you have it configured for a given population and location, you will likely reuse it across many different simulation scenarios.

In addition, demographics files are useful for creating heterogeneity a population. You can define values for accessibility, age bins, geography, risk, and other properties and assign values to individuals or nodes. For example, you might want to divide a population up into different bins based on age so you can target interventions to individuals in a particular age range. Another common use is to configure treatment coverage to be higher for individuals in easily accessible regions and lower for individuals in areas that are difficult to access. for more information, see Individual and node properties.

You can also simulate a complex health care system using property values that represent the intervention status for each individual. For example, consider a disease that has a second-line treatment that is not used unless the first-line treatment has proven ineffective. You can assign a property value after receiving the first-line treatment and prevent anyone from receiving the second-line treatment unless they have that property value and are still symptomatic. For more information, see Cascade of care.

EMOD assumes homogeneous mixing and disease transmission for the generic simulation type. Using the individual properties in the demographics file, you can also use the HINT feature to add heterogeneous transmission to your generic model. You cannot manually configure heterogeneous transmission using HINT with other simulation types because the heterogeneity in transmission for specific diseases and disease classes is configured by transmission parameters specific to each disease model. For more information, see Property-based heterogeneous disease transmission (HINT).

You do have the option to run a simulation without a demographics file if you set Enable_Demographics_Builtin to 1 in the configuration file. However, this option is primarily used for software testing. It will not provide meaningful simulation data as it does not represent the population of a real geographic location.

The demographics file structure and parameters are described in more detail in Demographics parameters.

Demographics overlay files

You can specify multiple demographics files, which function as a “base layer” file and one or more “overlay” files that override the base layer configuration. Overlay files can change the value of parameters already specified in the base layer or add new parameters. Support for multiple demographics layers allows for the following scenarios:

  • Separating different sets of parameters and values into individual layers (for example, to separate those that are useful for specific diseases into different layers)

  • Adding new parameters for a simulation into a new layer for easier prototyping

  • Overriding certain parameters of interest in a new layer

  • Overriding certain parameters for a particular sub-region

  • Simulating subsets of a larger region for which input files have been constructed

To use an overlay file:

  1. Select the demographics file to use as the base layer file. All nodes to be included in the simulation must be listed in this file.

  2. In the metadata, make note of the IdReference value.

    You may change this value if you desire, but all input files for a simulation must have the same IdReference value. For more information about this parameter and the structure of demographics files in general, see Demographics parameters.

  3. Create one or more overlay files. Keep the following things in mind:

    • In the metadata, the value for IdReference must match the value in the base layer file and all other input files except configuration and campaign.

    • Any nodes listed in an overlay but not in the base layer will not be simulated.

    • If the demographics files include any JSON array elements, the entire array is overridden. You cannot add or remove individual elements in an array using an overlay file.

    • Overriding a parameter value on a node will not affect the other parameter values applied to that node.

    • Values set in the Defaults section of an overlay will be applied to all nodes listed in that file, not all nodes in the entire simulation. Therefore, an overlay file that includes a Defaults section but no Nodes section will not have any effect.

  4. Place all demographics files in the directory where the other input files are stored.

  5. In the configuration file, set Demographics_Filenames to an array that contains a comma-delimited list of demographics files, listing the base layer file first.

An example base layer demographics file and an overlay file is below. You can see that the overlay adds the TransmissionMatrix for Heterogeneous Intra-Node Transmission (HINT) to only three of the five nodes (which correspond to Washington state counties).

{
    "Metadata": {
        "Author": "ewenger",
        "NodeCount": 5,
        "Tool": "table_to_demographics.py",
        "IdReference": "SampleContent",
        "DateCreated": "2013-08-01 15:37:16.853000"
    },
    "Defaults": {
        "NodeAttributes": {
            "BirthRate_DESCRIPTION": "Replacement of stable age distribution: Birth_Rate_Dependence=DEMOGRAPHIC_DEP_RATE (i.e. 14-45 year-old PossibleMothers)",
            "BirthRate": 0.00017675,

            "Airport": 0,
            "Region": 1,
            "Altitude": 0,
            "Seaport": 0
        },
        "IndividualAttributes": {

            "AgeDistribution_DESCRIPTION": "Box between age 0 and 60 years: Age_Initialization_Distribution_Type=DISTRIBUTION_SIMPLE",
            "AgeDistributionFlag": 1,
            "AgeDistribution1": 0,
            "AgeDistribution2": 21900,

            "PrevalenceDistribution_DESCRIPTION": "No initial infections",
            "PrevalenceDistributionFlag": 0,
            "PrevalenceDistribution1": 0,
            "PrevalenceDistribution2": 0,

            "RiskDistributionFlag": 0,
            "RiskDistribution1": 1,
            "RiskDistribution2": 0,

            "SusceptibilityDistributionFlag": 0,
            "SusceptibilityDistribution1": 1,
            "SusceptibilityDistribution2": 0,

            "MigrationHeterogeneityDistributionFlag": 0,
            "MigrationHeterogeneityDistribution1": 1,
            "MigrationHeterogeneityDistribution2": 0,

            "MortalityDistribution_DESCRIPTION": "WA state (1999-2010).  Source: wonder.cdc.gov",
            "MortalityDistribution": {
                "NumDistributionAxes": 2,
                "AxisNames": [ "gender", "age" ],
                "AxisUnits": [ "male=0,female=1", "years" ],
                "AxisScaleFactors": [ 1, 365 ],
                "NumPopulationGroups": [ 2, 15 ],
                "PopulationGroups": [
                    [ 0, 1 ],
                    [ 0, 1, 2.5, 7.5, 12.5, 17.5, 22.5, 30, 40, 50, 60, 70, 80, 90, 120 ]
                ],
                "ResultUnits": "annual deaths per 1000 individuals",
                "ResultScaleFactor": 0.00000273972602739726027397260273973,
                "ResultValues": [
                    [ 4.8, 0.2, 0.1, 0.2, 0.5, 1.1, 1.1, 1.7, 4.1, 9.2, 19.8, 53.7, 154.2, 1000, 1000 ],
                    [ 4.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.5, 1.1, 2.6, 5.5, 14.3, 40.1, 129.3, 1000, 1000 ]
                ]
            }
        }
    },
    "Nodes": [
        {
            "County": "Adams",
            "NodeAttributes": {
                "Latitude": 47.1274,
                "InitialPopulation": 19200,
                "Longitude": -118.38
            },
            "NodeID": 1
        },
        {
            "County": "Asotin",
            "NodeAttributes": {
                "Latitude": 46.3393,
                "InitialPopulation": 21800,
                "Longitude": -117.0482
            },
            "NodeID": 2
        },
        {
            "County": "Benton",
            "NodeAttributes": {
                "Latitude": 46.2068,
                "InitialPopulation": 183400,
                "Longitude": -119.7689
            },
            "NodeID": 3
        },
        {
            "County": "Chelan",
            "NodeAttributes": {
                "Latitude": 47.4235,
                "InitialPopulation": 73600,
                "Longitude": -120.3103
            },
            "NodeID": 4
        },
        {
            "County": "Clallam",
            "NodeAttributes": {
                "Latitude": 48.1181,
                "InitialPopulation": 72350,
                "Longitude": -123.4307
            },
            "NodeID": 5
        }
    ]
}

{
    "Metadata": {
        "Author": "cwiswell",
        "NodeCount": 10,
        "Tool": "table_to_demographics.py",
        "IdReference": "SampleContent",
        "DateCreated": "2013-08-01 15:37:16.853000"
    },
    "Defaults": {
        "IndividualProperties": [
            {
                "Property": "Accessibility",
                "Values": [ "VaccineTake", "VaccineRefuse"],
                "Initial_Distribution": [ 0.85, 0.15],
                "TransmissionMatrix": {
                    "Route": "Contact",
                    "Matrix": [
                        [1.1, 0.3],
                        [0.3, 5.0]
                    ]
                }
            },
            {
                "Property": "Age_Bin",
                "Age_Bin_Edges_In_Years": [ 0, 5, 13, -1 ],
                "TransmissionMatrix": {
                    "Route": "Contact",
                    "Matrix": [
                        [1.4, 1.0, 1.0],
                        [1.0, 2.5, 0.7],
                        [1.0, 0.7, 1.0]
                    ]
                }
            }
        ]
    },
    "Nodes": [
        {
            "NodeID": 1
        },
        {
            "NodeID": 3
        },
        {
            "NodeID": 5
        }
    ]
}
Configuration file

The primary means of configuring the disease simulation is the configuration file. This required file is a JSON (JavaScript Object Notation) formatted file that is typically named config.json. The configuration file controls many different aspects of the simulation. For example,

  • The names of the campaign file and other input files to use

  • How to use additional demographics, climate, and migration data (such as enabling features or scaling values)

  • General disease attributes such as infectivity, immunity, mortality, and so on

  • Attributes specific to the disease type being modeled, such as infectivity and mortality

  • The reports to output from the simulation

Although you can create configuration files entirely from scratch, it is often easier to start from an existing configuration file and modify it to meet your needs. You can download sets of configuration and campaign files that illustrate how to model different disease scenarios at EMOD scenarios. For more information, see Tutorials and simulation examples.

The simplest method of working with the configuration files is to use a text editor to directly edit the parameters or parameter values in the JSON file. However, you may want to use Python or another scripting language to make large modifications. See Example scripts to modify a configuration file for more information.

For a complete list of configuration parameters that are available to use with this simulation type, see Configuration parameters. For more information about JSON, see EMOD parameter reference.

Flattened configuration files

A flattened configuration file is generally a single-depth JSON file with configuration parameters listed alphabetically. This is the configuration file format that Eradication.exe and Eradication binary for Linux both require for running simulations.

However, there may be some hierarchical elements in the flattened version. For example, Vector_Species_Params and Malaria_Drug_Params have nested JSON objects.

Below is an example of a flattened configuration file:

{
  "parameters": {
    "Age_Initialization_Distribution_Type": "DISTRIBUTION_SIMPLE",
    "Base_Infectivity": 0.3,
    "Climate_Model": "CLIMATE_OFF",
    "Config_Name": "1_Generic_MinimalConfig",
    "Custom_Reports_Filename": "",
    "Default_Geography_Initial_Node_Population": 100,
    "Default_Geography_Torus_Size": 3,
    "Enable_Default_Reporting": 1,
    "Enable_Demographics_Builtin": 1,
    "Enable_Demographics_Reporting": 0,
    "Enable_Demographics_Risk": 0,
    "Enable_Disease_Mortality": 0,
    "Enable_Heterogeneous_Intranode_Transmission": 0,
    "Enable_Immunity": 0,
    "Enable_Initial_Prevalence": 0,
    "Enable_Initial_Susceptibility_Distribution": 0,
    "Enable_Interventions": 0,
    "Enable_Maternal_Infection_Transmission": 0,
    "Enable_Maternal_Protection": 0,
    "Enable_Property_Output": 0,
    "Enable_Skipping": 0,
    "Enable_Spatial_Output": 0,
    "Enable_Superinfection": 0,
    "Enable_Susceptibility_Scaling": 0,
    "Enable_Vital_Dynamics": 0,
    "Geography": "NONE",
    "Incubation_Period_Constant": 3,
    "Incubation_Period_Distribution": "CONSTANT_DISTRIBUTION",
    "Individual_Sampling_Type": "TRACK_ALL",
    "Infection_Updates_Per_Timestep": 1,
    "Infectious_Period_Distribution": "EXPONENTIAL_DISTRIBUTION",
    "Infectious_Period_Exponential": 7,    
    "Listed_Events": [],
    "Load_Balance_Filename": "",
    "Maternal_Infection_Transmission_Probability": 0,
    "Migration_Model": "NO_MIGRATION",
    "Node_Grid_Size": 0.042,
    "Number_Basestrains": 1,
    "Number_Substrains": 1,
    "Population_Density_Infectivity_Correction": "CONSTANT_INFECTIVITY",
    "Population_Scale_Type": "USE_INPUT_FILE",
    "Report_Event_Recorder": 0,
    "Run_Number": 1,
    "Simulation_Duration": 180,
    "Simulation_Timestep": 1,
    "Simulation_Type": "GENERIC_SIM",
    "Start_Time": 0,
    "Symptomatic_Infectious_Offset": 0
  }
}
Hierarchical configuration files

A hierarchical file allows you to organize parameters into logical groups by nesting them within JSON objects, making them easier to manage. If you use hierarchical configuration files, you must flatten them prior to running a simulation. The names you use to label these logical categories are unimportant; the scripts used to flatten the files will search through the hierarchies and retain only the “leaf” values in the resulting flattened file. See Configuration overlay files for more information on flattening files.

Below is an example of a hierarchical configuration file:

{
  "parameters": {
    "CAMPAIGNS": {
      "Campaign_Filename": "campaign.json",
      "Enable_Interventions": 1,
      "Listed_Events": [],
      "PKPD_Model": "FIXED_DURATION_CONSTANT_EFFECT"
    },
    "CLIMATE": {
      "Climate_Model": "CLIMATE_OFF"
    },
    "DEMOGRAPHICS": {
      "Age_Initialization_Distribution_Type": "DISTRIBUTION_SIMPLE",
      "Birth_Rate_Dependence": "DEMOGRAPHIC_DEP_RATE",
      "Birth_Rate_Time_Dependence": "NONE",
      "Default_Geography_Initial_Node_Population": 1000,
      "Default_Geography_Torus_Size": 10,
      "Demographics_Filenames": [
        "NO_DEFAULT_DEMOGRAPHICS"
      ],
      "Enable_Aging": 1,
      "Enable_Birth": 1,
      "Enable_Demographics_Birth": 0,
      "Enable_Demographics_Builtin": 0,
      "Enable_Demographics_Reporting": 0,
      "Enable_Demographics_Risk": 0,
      "Enable_Natural_Mortality": 1,
      "Enable_Vital_Dynamics": 1,
      "IMMUNITY": {
        "Acquisition_Blocking_Immunity_Decay_Rate": 0.1,
        "Acquisition_Blocking_Immunity_Duration_Before_Decay": 60,
        "Enable_Immune_Decay": 1,
        "Enable_Immunity": 1,
        "Immunity_Initialization_Distribution_Type": "DISTRIBUTION_OFF",
        "Post_Infection_Acquisition_Multiplier": 0,
        "Post_Infection_Transmission_Multiplier": 0,
        "Susceptibility_Scaling_Type": "CONSTANT_SUSCEPTIBILITY",
        "Transmission_Blocking_Immunity_Decay_Rate": 0.1,
        "Transmission_Blocking_Immunity_Duration_Before_Decay": 60
      },
      "MORTALITY": {
        "Base_Mortality": 0,
        "Death_Rate_Dependence": "NONDISEASE_MORTALITY_BY_AGE_AND_GENDER",
        "Enable_Disease_Mortality": 0,
        "Mortality_Blocking_Immunity_Decay_Rate": 0.001,
        "Mortality_Blocking_Immunity_Duration_Before_Decay": 60,
        "Mortality_Time_Course": "DAILY_MORTALITY",
        "Post_Infection_Mortality_Multiplier": 0
      },
      "Minimum_Adult_Age_Years": 15,
      "Population_Density_C50": 30,
      "Population_Scale_Type": "USE_INPUT_FILE",
      "SAMPLING": {
        "Base_Individual_Sample_Rate": 1,
        "Individual_Sampling_Type": "TRACK_ALL",
        "Max_Node_Population_Samples": 40,
        "Sample_Rate_0_18mo": 1,
        "Sample_Rate_10_14": 1,
        "Sample_Rate_15_19": 1,
        "Sample_Rate_18mo_4yr": 1,
        "Sample_Rate_20_Plus": 1,
        "Sample_Rate_5_9": 1,
        "Sample_Rate_Birth": 2
      },
      "x_Base_Population": 1
    },
    "DISEASE": {
      "Enable_Superinfection": 0,
      "INCUBATION": {
        "Incubation_Period_Constant": 3,
        "Incubation_Period_Distribution": "CONSTANT_DISTRIBUTION"
      },
      "INFECTIOUSNESS": {
        "Base_Infectivity": 0.3,
        "Infectious_Period_Distribution": "EXPONENTIAL_DISTRIBUTION",
        "Infectious_Period_Exponential": 7,        
        "Population_Density_Infectivity_Correction": "CONSTANT_INFECTIVITY"
      },
      "Infection_Updates_Per_Timestep": 1,
      "Max_Individual_Infections": 1,
      "TRANSMISSION": {
        "Enable_Maternal_Infection_Transmission": 0,
        "Maternal_Transmission_Probability": 0
      }
    },
    "FUDGE_FACTORS": {
      "x_Air_Migration": 1,
      "x_Birth": 1,
      "x_Local_Migration": 1,
      "x_Other_Mortality": 1,
      "x_Regional_Migration": 1,
      "x_Sea_Migration": 1,
      "x_Temporary_Larval_Habitat": 1
    },
    "HPC": {
      "Load_Balance_Filename": ""
    },
    "INTRANODE_TRANSMISSION": {
      "Enable_Heterogeneous_Intranode_Transmission": 0
    },
    "MIGRATION": {
      "Migration_Model": "NO_MIGRATION"
    },
    "OUTPUT": {
      "Custom_Reports_Filename": "NoCustomReports",
      "Enable_Default_Reporting": 1,
      "Enable_Property_Output": 0,
      "Enable_Spatial_Output": 0,
      "Report_Event_Recorder": 0
    },
    "POLIO": {},
    "PRIMARY": {
      "Config_Name": "DEFAULT_CONFIG_NAME_SHOULD_BE_SET",
      "ENUMS": {
        "Simulation_Type": "GENERIC_SIM"
      },
      "Node_Grid_Size": 0.042,
      "Run_Number": 0,
      "Simulation_Duration": 365,
      "Simulation_Timestep": 1,
      "Start_Time": 0
    },
    "STRAIN_TRACKING": {
      "Number_Basestrains": 1,
      "Number_Substrains": 1
    },
    "Symptomatic_Infectious_Offset": 0
  }
}
Configuration overlay files

You can use two configuration files when setting up a simulation. One file contains default parameter settings and an overlay file contains additional parameters or different parameter values that override the values in the default file.

Overlay files allow you to easily separate a subset of parameters that are of particular interest from the rest of the parameters needed to run a simulation. You can easily modify the parameters in the overlay file without needing to maintain a complete configuration file. This can be especially helpful when you want to experiment with the values set in certain parameters of interest without modifying the rest of the settings. You can have one default file and many different overlay files for different configurations. It also allows you to easily update the default values across multiple simulations.

These files must be flattened into a single file and the values in the overlay file will override those in the default file. When using overlay files, the parameters are often organized into logical groups using the hierarchical file format. See Configuration file for more information. When using any kind of hierarchical file, whether or not you are using an overlay file, it must also be flattened using the Python script below.

To flatten two configuration files:

  1. Create the default configuration file in JSON. You may, though it is not required, organize the parameters into logical categories of nested JSON objects to make managing the parameters easier. See Configuration parameters for a complete list of all parameters that are available. See the example default configuration file below.

    {
  "parameters": {
    "CAMPAIGNS": {
      "Campaign_Filename": "campaign.json",
      "Enable_Interventions": 1,
      "Listed_Events": [],
      "PKPD_Model": "FIXED_DURATION_CONSTANT_EFFECT"
    },
    "CLIMATE": {
      "Climate_Model": "CLIMATE_OFF"
    },
    "DEMOGRAPHICS": {
      "Age_Initialization_Distribution_Type": "DISTRIBUTION_SIMPLE",
      "Birth_Rate_Dependence": "DEMOGRAPHIC_DEP_RATE",
      "Birth_Rate_Time_Dependence": "NONE",
      "Default_Geography_Initial_Node_Population": 1000,
      "Default_Geography_Torus_Size": 10,
      "Demographics_Filenames": [
        "NO_DEFAULT_DEMOGRAPHICS"
      ],
      "Enable_Aging": 1,
      "Enable_Birth": 1,
      "Enable_Demographics_Birth": 0,
      "Enable_Demographics_Builtin": 0,
      "Enable_Demographics_Reporting": 0,
      "Enable_Demographics_Risk": 0,
      "Enable_Natural_Mortality": 1,
      "Enable_Vital_Dynamics": 1,
      "IMMUNITY": {
        "Acquisition_Blocking_Immunity_Decay_Rate": 0.1,
        "Acquisition_Blocking_Immunity_Duration_Before_Decay": 60,
        "Enable_Immune_Decay": 1,
        "Enable_Immunity": 1,
        "Immunity_Initialization_Distribution_Type": "DISTRIBUTION_OFF",
        "Post_Infection_Acquisition_Multiplier": 0,
        "Post_Infection_Transmission_Multiplier": 0,
        "Susceptibility_Scaling_Type": "CONSTANT_SUSCEPTIBILITY",
        "Transmission_Blocking_Immunity_Decay_Rate": 0.1,
        "Transmission_Blocking_Immunity_Duration_Before_Decay": 60
      },
      "MORTALITY": {
        "Base_Mortality": 0,
        "Death_Rate_Dependence": "NONDISEASE_MORTALITY_BY_AGE_AND_GENDER",
        "Enable_Disease_Mortality": 0,
        "Mortality_Blocking_Immunity_Decay_Rate": 0.001,
        "Mortality_Blocking_Immunity_Duration_Before_Decay": 60,
        "Mortality_Time_Course": "DAILY_MORTALITY",
        "Post_Infection_Mortality_Multiplier": 0
      },
      "Minimum_Adult_Age_Years": 15,
      "Population_Density_C50": 30,
      "Population_Scale_Type": "USE_INPUT_FILE",
      "SAMPLING": {
        "Base_Individual_Sample_Rate": 1,
        "Individual_Sampling_Type": "TRACK_ALL",
        "Max_Node_Population_Samples": 40,
        "Sample_Rate_0_18mo": 1,
        "Sample_Rate_10_14": 1,
        "Sample_Rate_15_19": 1,
        "Sample_Rate_18mo_4yr": 1,
        "Sample_Rate_20_Plus": 1,
        "Sample_Rate_5_9": 1,
        "Sample_Rate_Birth": 2
      },
      "x_Base_Population": 1
    },
    "DISEASE": {
      "Enable_Superinfection": 0,
      "INCUBATION": {
        "Incubation_Period_Constant": 3,
        "Incubation_Period_Distribution": "CONSTANT_DISTRIBUTION"
      },
      "INFECTIOUSNESS": {
        "Base_Infectivity": 0.3,
        "Infectious_Period_Distribution": "EXPONENTIAL_DISTRIBUTION",
        "Infectious_Period_Exponential": 7,        
        "Population_Density_Infectivity_Correction": "CONSTANT_INFECTIVITY"
      },
      "Infection_Updates_Per_Timestep": 1,
      "Max_Individual_Infections": 1,
      "TRANSMISSION": {
        "Enable_Maternal_Infection_Transmission": 0,
        "Maternal_Transmission_Probability": 0
      }
    },
    "FUDGE_FACTORS": {
      "x_Air_Migration": 1,
      "x_Birth": 1,
      "x_Local_Migration": 1,
      "x_Other_Mortality": 1,
      "x_Regional_Migration": 1,
      "x_Sea_Migration": 1,
      "x_Temporary_Larval_Habitat": 1
    },
    "HPC": {
      "Load_Balance_Filename": ""
    },
    "INTRANODE_TRANSMISSION": {
      "Enable_Heterogeneous_Intranode_Transmission": 0
    },
    "MIGRATION": {
      "Migration_Model": "NO_MIGRATION"
    },
    "OUTPUT": {
      "Custom_Reports_Filename": "NoCustomReports",
      "Enable_Default_Reporting": 1,
      "Enable_Property_Output": 0,
      "Enable_Spatial_Output": 0,
      "Report_Event_Recorder": 0
    },
    "POLIO": {},
    "PRIMARY": {
      "Config_Name": "DEFAULT_CONFIG_NAME_SHOULD_BE_SET",
      "ENUMS": {
        "Simulation_Type": "GENERIC_SIM"
      },
      "Node_Grid_Size": 0.042,
      "Run_Number": 0,
      "Simulation_Duration": 365,
      "Simulation_Timestep": 1,
      "Start_Time": 0
    },
    "STRAIN_TRACKING": {
      "Number_Basestrains": 1,
      "Number_Substrains": 1
    },
    "Symptomatic_Infectious_Offset": 0
  }
}

  2. Create the overlay configuration file in JSON. This file must include the parameter Default_Config_Path, set to the path to the default configuration file, relative to the location of the flatten_config.py script in the EMOD Regression folder. Again, you may organize the parameters into logical categories if you desire. See the example overlay configuration file below.

    {
     "Default_Config_Path": "defaults/generic-default-config.json",
     "parameters": {
          "DEMOGRAPHICS": {
              "Enable_Demographics_Builtin": 0,
              "Birth_Rate_Dependence": "POPULATION_DEP_RATE",
              "Death_Rate_Dependence" : "NOT_INITIALIZED",
              "Enable_Vital_Dynamics": 0,
              "Sample_Rate_Birth": 1,
              "Enable_Demographics_Reporting": 1
          },
          "DISEASE": {
              "Base_Incubation_Period": 0,
              "Base_Infectious_Period": 4,
              "Base_Infectivity": 3.5,
              "Enable_Immune_Decay": 0
          },
          "PRIMARY": {
               "Config_Name": "00_Generic_DEFAULT",
               "Demographics_Filenames": ["demographics.json"],
               "Geography": "",
               "Run_Number": 1,
               "Simulation_Duration": 90
          }
     }
}

  3. In a Command Prompt window, navigate to the Regression folder.

  4. Run the flatten_config.py script, providing the relative path to the overlay file:

    python flatten_config.py experiment/parameter_overrides.json

  5. Open the resulting config.json file in the same folder as parameter_overrides.json and see that it has been flattened into a single layer with all parameters listed alphabetically and any logical categories removed. Eradication.exe will not accept a configuration file with nested JSON objects.

    {
  "parameters": {
    "Age_Initialization_Distribution_Type": "DISTRIBUTION_SIMPLE",
    "Base_Infectivity": 0.3,
    "Climate_Model": "CLIMATE_OFF",
    "Config_Name": "1_Generic_MinimalConfig",
    "Custom_Reports_Filename": "",
    "Default_Geography_Initial_Node_Population": 100,
    "Default_Geography_Torus_Size": 3,
    "Enable_Default_Reporting": 1,
    "Enable_Demographics_Builtin": 1,
    "Enable_Demographics_Reporting": 0,
    "Enable_Demographics_Risk": 0,
    "Enable_Disease_Mortality": 0,
    "Enable_Heterogeneous_Intranode_Transmission": 0,
    "Enable_Immunity": 0,
    "Enable_Initial_Prevalence": 0,
    "Enable_Initial_Susceptibility_Distribution": 0,
    "Enable_Interventions": 0,
    "Enable_Maternal_Infection_Transmission": 0,
    "Enable_Maternal_Protection": 0,
    "Enable_Property_Output": 0,
    "Enable_Skipping": 0,
    "Enable_Spatial_Output": 0,
    "Enable_Superinfection": 0,
    "Enable_Susceptibility_Scaling": 0,
    "Enable_Vital_Dynamics": 0,
    "Geography": "NONE",
    "Incubation_Period_Constant": 3,
    "Incubation_Period_Distribution": "CONSTANT_DISTRIBUTION",
    "Individual_Sampling_Type": "TRACK_ALL",
    "Infection_Updates_Per_Timestep": 1,
    "Infectious_Period_Distribution": "EXPONENTIAL_DISTRIBUTION",
    "Infectious_Period_Exponential": 7,    
    "Listed_Events": [],
    "Load_Balance_Filename": "",
    "Maternal_Infection_Transmission_Probability": 0,
    "Migration_Model": "NO_MIGRATION",
    "Node_Grid_Size": 0.042,
    "Number_Basestrains": 1,
    "Number_Substrains": 1,
    "Population_Density_Infectivity_Correction": "CONSTANT_INFECTIVITY",
    "Population_Scale_Type": "USE_INPUT_FILE",
    "Report_Event_Recorder": 0,
    "Run_Number": 1,
    "Simulation_Duration": 180,
    "Simulation_Timestep": 1,
    "Simulation_Type": "GENERIC_SIM",
    "Start_Time": 0,
    "Symptomatic_Infectious_Offset": 0
  }
}
Example scripts to modify a configuration file

Direct editing of the configuration file in a text editor may be sufficient for small and infrequent changes, but you will likely find that scripting tools are more powerful and reliable for both creating and modifying files.

The following example shows how to read a configuration file to a Python dictionary, modify a parameter, and write it back out to the file:

import json

# load the current config.json
config_file = open( "config.json" )
config_json = json.load( config_file )
config_file.close()

# modify one of the parameter values, e.g. "base_infectivity"
config_json["parameters"]["base_infectivity"] = 0.5

# write the modified config file
modified_file = open( "modified_config.json", "w" )
json.dump( config_json, modified_file, sort_keys=True, indent=4 )
modified_file.close()

The following example shows how to modify a configuration file in MATLAB:

addpath Matlab
addpath Matlab\test

% load the simulation configuration file into MATLAB structure
configjson = loadJson( "config.json" );

% modify one of the values
configjson.parameters.Base_Infectivity = 08.5;

% save the new configuration to file
saveJson( "modified_config.json", configjson );
Parameter sweeps

Parameter sweeps iteratively update the values of parameters to exhaustively search through the parameter space for a simulation. EMOD does not currently support automated parameter sweeps. However, you can write your own code, such as a Python or MATLAB script, that iterates through the values you want for a particular parameter. This topic describes how to perform a parameter sweep.

For example, you can run simulations using a Python script that uses the parameter values specified in a JSON parameter sweep file to iteratively update the configuration or campaign parameter values. The IDM test team performs parameter sweeps as part of regression testing. See the following examples to see how this is implemented.

The following JSON example illustrates sweeping through configuration parameter values.

{
    "sweep" :
    {
        "path": "43_Vector_Garki_MultiCore_VectorMigration",
        "param_name" : "Run_Number",
        "param_values" : [ 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144 ]
    }
}

The following Python example illustrates how to update the configuration file with the above values. This is excerpted from the regression_test.py script in the Regression directory.

elif "sweep" in reglistjson:
    print( "Running sweep...\n" )
    param_name = reglistjson["sweep"]["param_name"]

    for param_value in reglistjson["sweep"]["param_values"]:
        os.chdir(ru.cache_cwd)
        sim_timestamp = str(datetime.datetime.now()).replace('-', '_' ).replace( ' ', '_' ).replace( ':', '_' ).replace( '.', '_' )
        if regression_id == None:
            regression_id = sim_timestamp

        configjson = ru.flattenConfig( os.path.join( reglistjson["sweep"]["path"], "param_overrides.json" ) )
        if configjson is None:
            print("Error flattening config.  Skipping " + simcfg["path"])
            ru.final_warnings += "Error flattening config.  Skipped " + simcfg["path"] + "\n"
            continue

        # override sweep parameter
        configjson["parameters"][param_name] = param_value

        campjson_file = open( os.path.join( reglistjson["sweep"]["path"],"campaign.json" ) )
        campjson = json.loads( campjson_file.read().replace( "u'", "'" ).replace( "'", '"' ).strip( '"' ) )
        campjson_file.close()
        configjson["campaign_json"] = str(campjson)

        report_fn = os.path.join( reglistjson["sweep"]["path"],"custom_reports.json" )
        if os.path.exists( report_fn ) == True:
            reportjson_file = open( report_fn )
            reportjson = json.loads( reportjson_file.read().replace( "u'", "'" ).replace( "'", '"' ).strip( '"' ) )
            reportjson_file.close()
            configjson["custom_reports_json"] = str(reportjson)
        else:
            configjson["custom_reports_json"] = None

        thread = runner.commissionFromConfigJson( sim_timestamp, configjson, reglistjson["sweep"]["path"], None, 'sweep' )
        ru.reg_threads.append( thread )
else:
    print "Unknown state"
    sys.exit(0)
Campaign file

The campaign file is an optional JSON (JavaScript Object Notation) file that distributes outbreaks and contains all parameters that define the collection of interventions that make up a disease control or eradication campaign. For example, campaign parameters can control the following:

  • Size and location of outbreaks

  • Target demographic (age, location, gender, etc.) for interventions

  • Diagnostic tests to use

  • The cost and timing of interventions

Much of the power and flexibility of EMOD comes from the customizable campaign interventions. Briefly, campaigns are created through the hierarchical organization of parameter groups. It is hierarchically organized into logical groups of parameters that can have arbitrary levels of nesting. Typically, the file is named campaign.json. The relative path to this file is specified by Campaign_Filename in the configuration file.

To distribute an intervention, you must configure the following nested JSON objects:

campaign event

Campaign events determine when and where an intervention is distributed during a campaign. “When” can be the number of days after the beginning of the simulation or at a point during a particular calendar year. “Where” is the geographic node or nodes in which the event takes place.

event coordinator

Event coordinators are nested within the campaign event JSON object and determine who receives the intervention. “Who” is determined by filtering on age, gender, or on the individual properties configured in the demographics files, such as risk group or sociodemographic category. See Individual and node properties.

individual-level intervention

Individual-level interventions determine what will be distributed to individuals to reduce the spread of a disease. For example, distributing vaccines or drugs are individual-level interventions. In the schema, these are labeled as IndividualTargeted.

It is also possible (but not required) to configure why a particular intervention is distributed by adding trigger conditions to the intervention. For example, interventions can be triggered by notifications broadcast after some an event, such as Births (the individual’s own birth), GaveBirth, NewInfectionEvent, and more. It’s also possible to have one intervention trigger another intervention by asking the first intervention to broadcast a unique string, and having the second intervention be triggered upon receipt of that string. See Event list.

Individual-level interventions can be used as part of configuring a cascade of care along with the individual properties set in the demographics file. Use Disqualifying_Properties to disqualify individuals who would otherwise receive the intervention and New_Property_Value to assign a new value when the intervention is received. For example, you can assign a property value after receiving the first-line treatment for a disease and prevent anyone from receiving the second-line treatment unless they have that property value and are still symptomatic.

node-level intervention

Node-level interventions determine what will be distributed to each node to reduce the spread of a disease. For example, spraying larvicide in a village to kill mosquito larvae is a node-level malaria intervention. Sometimes this can be an intermediate intervention that schedules another intervention. Node-level disease outbreaks are also configured as “interventions”. In the schema, these are labeled as NodeTargeted.

It is also possible (but not required) to configure why a particular intervention is distributed by adding trigger conditions to the intervention. For example, interventions can be triggered by notifications broadcast after some an event, such as Births, NewInfectionEvent, and more. It’s also possible to have one intervention trigger another intervention by asking the first intervention to broadcast a unique string, and having the second intervention be triggered upon receipt of that string. See Event list.

Creating campaigns describes some ways to configure a campaign file to target individuals with particular characteristics, repeat interventions, and more. See Campaign parameters for a comprehensive list and description of all parameters available to use in the campaign file for this simulation type.

Although you can create campaign files entirely from scratch, it is often easier to start from an existing campaign file and modify it to meet your needs. You can download sets of configuration and campaign files that illustrate how to model different disease scenarios at EMOD scenarios. For more information, see Tutorials and simulation examples.

Campaign overlay files

You can use two campaign files when setting up a simulation. One file contains default campaign settings and an overlay file contains additional parameters or different parameter values that override the values in the default file. The files must be flattened into a single file before running a simulation.

Overlay files allow you to easily separate a subset of parameters that are of particular interest from the rest of the parameters needed to run a simulation. You can easily modify the parameters in the overlay file without needing to maintain a complete campaign file. This can be especially helpful when you want to experiment with different interventions without modifying the rest of the settings. You can have one default file and many different overlay files for different intervention settings. It also allows you to easily update the default values across multiple simulations.

In addition to being used for model experimentation, overlay files are used when testing the software functionality after making source code changes. If you run the EMOD regression tests using regression_test.py, campaign files will be flattened as part of those tests. However, this may take several hours if run locally. More guidance on modifying the EMOD source code is in the “Advance the model” section.

The EMOD Regression directory contains many different subdirectories that contain configuration, campaign, and other associated files to run simulations used in regression testing. Some subdirectories include a campaign overlay file (campaign_overrides.json), which has been created by combining campaign_overrides.json with one of the default files in Regression/defaults. The naming of these files is an arbitrary convention used at IDM; you may name this files anything you choose. However, it may be useful to see some examples of these files to understand how they are used.

To flatten campaign files:

  1. Create the default campaign file in JSON. You may, though it is not required, organize the parameters into logical categories of nested JSON objects to make managing the parameters easier. See Campaign parameters for a complete list of all parameters that are available. See the example default campaign file below.

    {
    "Campaign_Name": "Initial Seeding",
    "Use_Defaults": 1,
    "Events": [
        {
            "Event_Name": "Outbreak",
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 30,
            "class": "CampaignEvent",
            "Event_Coordinator_Config": {
                "Demographic_Coverage": 0.001,
                "Target_Demographic": "Everyone",
                "class": "StandardInterventionDistributionEventCoordinator",
                "Intervention_Config": {
                    "Antigen": 0,
                    "Genome": 0,
                    "Outbreak_Source": "PrevalenceIncrease",
                    "class": "OutbreakIndividual"
                }
            }
        }
    ]
}

  2. Create the overlay campaign file in JSON. This file must include the parameter Default_Campaign_Path, set to the path to the default campaign file, relative to the location of the flatten_campaign.py script in the EMOD Regression folder. Again, you may organize these into logical categories if you desire. See the example overlay campaign file below.

    {
    "Campaign_Name": "Vaccine",
    "Default_Campaign_Path": "defaults/generic-default-campaign.json",
    "Events": [
        {
            "VACCINATION": "BEGIN",
            "Event_Name": "SimpleVaccine",
            "Event_Coordinator_Config": {
                "Demographic_Coverage": 0.5,
                "Intervention_Config": {
                    "Cost_To_Consumer": 10,
                    "Waning_Config": {
                        "class": "WaningEffectMapLinear",
                        "Initial_Effect" : 1.0,
                        "Expire_At_Durability_Map_End" : 0,
                        "Durability_Map" :
                        {
                            "Times"  : [   0,  30,  60,  90, 120 ],
                            "Values" : [ 0.9, 0.3, 0.9, 0.6, 1.0 ]
                        }
                    },
                    "Vaccine_Take": 1,
                    "Vaccine_Type": "AcquisitionBlocking",
                    "class": "SimpleVaccine"
                },
                "Number_Repetitions": 3,
                "Target_Demographic": "Everyone",
                "Timesteps_Between_Repetitions": 7,
                "class": "StandardInterventionDistributionEventCoordinator"
            },
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 1,
            "class": "CampaignEvent",
            "VACCINATION": "END"
        }
    ]
}

  3. In a Command Prompt window, navigate to the Regression folder.

  4. Run the flatten_campaign.py script, providing the relative path to the overlay file and the path to and name of the new flattened campaign file that will be saved, using the arguments as shown below:

    python flatten_campaign.py --overlay experiment/campaign_overlay.json --saveto experiment/campaign.json

  5. Open the resulting campaign.json file and see that it has been flattened into a single file with nested JSON objects and any logical categories retained.

    {
    "Campaign_Name": "Vaccine",
    "Default_Campaign_Path": "defaults/generic-default-campaign.json",
    "Use_Defaults": 1,
    "Events":
    [
        {
            "VACCINATION": "BEGIN",
            "Event_Name": "SimpleVaccine",
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 1,
            "class": "CampaignEvent",
            "Event_Coordinator_Config": {
                "Demographic_Coverage": 0.5,
                "Number_Repetitions": 3,
                "Target_Demographic": "Everyone",
                "Timesteps_Between_Repetitions": 7,
                "class": "StandardInterventionDistributionEventCoordinator",
                "Intervention_Config": {
                    "Cost_To_Consumer": 10,
                    "Waning_Config": {
                        "class": "WaningEffectMapLinear",
                        "Initial_Effect" : 1.0,
                        "Expire_At_Durability_Map_End" : 0,
                        "Durability_Map" : {
                            "Times"  : [   0,  30,  60,  90, 120 ],
                            "Values" : [ 0.9, 0.3, 0.9, 0.6, 1.0 ]
                        }
                    },
                    "Vaccine_Take": 1,
                    "Vaccine_Type": "AcquisitionBlocking",
                    "class": "SimpleVaccine"
                }
            },
            "VACCINATION": "END"
        },
        {
            "Event_Name": "Outbreak",
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 30,
            "class": "CampaignEvent",
            "Event_Coordinator_Config": {
                "Demographic_Coverage": 0.001,
                "Target_Demographic": "Everyone",
                "class": "StandardInterventionDistributionEventCoordinator",
                "Intervention_Config": {
                    "Antigen": 0,
                    "Genome": 0,
                    "Outbreak_Source": "PrevalenceIncrease",
                    "class": "OutbreakIndividual"
                }
            }
        }
    ]
}

After the overlay files and default files are combined into a single campaign file, you can run a simulation using the EMOD executable (Eradication.exe).

Migration files

Migration files describe the rate of migration of individuals out of a geographic node. There are additional configuration parameters you can set to define the migration patterns and return time. There are four types of migration files that can be used by EMOD, namely, local migration, regional migration, air migration and sea migration files. The demographics file can be configured to exclude some nodes from certain types of travel.

Local migration describes foot travel into adjacent nodes. Regional migration describes migration that occurs on a road or rail network. Air migration describes migration that occurs by plane; you must indicate that a node has an airport for air migration from that node to occur. Sea migration describes migration that occurs by ship travel; you must indicate that a node has a seaport for sea migration from that node to occur. For both air and sea migration, it’s possible to originate in a node with an airport or seaport and migrate to a node without one, but the reverse is not true. Unlike the other migration files, the sea migration file only contains information for the nodes that are seaports.

To use the migration files, in the configuration file you must set Migration_Model to a valid migration type and indicate the path to the migration files you want to use. You must also set a parameter to enable each type of migration you want to include in the simulation. There are additional parameters in the configuration file you can use to scale or otherwise modify the data included in the migration files. The migration rate can be differentially set by age and/or gender. Additionally, a modifier can be applied for the migration rates to follow a distribution curve in the population. For more information, see Migration parameters.

Migration data is contained in a set of two files, a metadata file with header information and a binary data file. Both files are required. To create these files see, How to create migration files.

JSON metadata file

The metadata file is a JSON-formatted file that includes a metadata section and a node offsets section. The Metadata section contains a JSON object with parameters, some of which are strictly informational and some of which are used by Eradication.exe. However, the informational ones may still be important to understand the provenance and meaning of the data.

Parameters

The following parameters can be included in the migration metadata file:

Parameter

Data type

Description

AgesYears

array

An array that defines the age bins by which to separate the population and define migration rates.

Author

string

The author of the file.

DatavalueCount

integer

(Used by EMOD.) The number of outbound data values per node (max 100). The number must be the same across every node in the binary file. If you are using an older file that does not include this parameter, each migration type has the following maximum data values:

  • LOCAL_MIGRATION: 8

  • REGIONAL_MIGRATION: 30

  • AIR_MIGRATION: 60

  • SEA_MIGRATION: 5

DateCreated

string

The day the file was created.

GenderDataType

enum

Whether age data is provided for each gender separately or is the same for both. Accepted values are ONE_FOR_BOTH_GENDERS and ONE_FOR_EACH_GENDER.

IdReference

string

(Used by EMOD.) A unique, user-selected string that indicates the method used by EMOD for generating NodeID values in the input files. For more information, see Input files.

InterpolationType

enum

The method by which to interpolate the age-dependent rate data. Accepted values are LINEAR_INTERPOLATION and PIECEWISE_CONSTANT.

MigrationType

enum

The type of migration the data describes. Accepted values are:

  • LOCAL_MIGRATION

  • AIR_MIGRATION

  • REGIONAL_MIGRATION

  • SEA_MIGRATION

NodeCount

integer

(Used by EMOD.) The number of nodes to expect in this file.

NodeOffsets

string

(Used by EMOD.) A string that is NodeCount \(\times\) 16 characters long. For each node, the first 8 characters are the origin NodeID in hexadecimal. The second 8 characters are the byte offset in hex to the location in the binary file where the destination NodeIDs and migration rates appear.

Tool

string

The script used to create the file.
Example 
{
    "Metadata": {
        "Tool": "PythonApplication1.py", 
        "IdReference": "ABC", 
        "DateCreated": "Wed Dec  2 14:08:48 2015", 
        "InterpolationType": "PIECEWISE_CONSTANT", 
        "GenderDataType": "SAME_FOR_BOTH_GENDERS", 
        "NodeCount": 4, 
        "MigrationType": "LOCAL_MIGRATION", 
        "AgesYears": [
            14.99, 
            15, 
            45, 
            75, 
            105
        ], 
        "DatavalueCount": 3
    }, 
    "NodeOffsets": "000000010000000000000002000000240000000300000048000000040000006C"
}
Binary file

The binary file contains the migration rate data. Migration rate determines the average time until an individual takes a trip out of the node. This time is drawn from an exponential distribution with the parameter \(\lambda\) as the number of trips per day. Therefore, a migration rate of 0.1 can be viewed as 10 days until migration, on average. You can adjust this base rate using the Scalars and multipliers parameters.

The data in the binary file is laid out in a sequential stream of 4-byte integers that identify the origin and destination nodes followed by a stream of 8-byte floats that contain the migration rate for those node pairs laid out in the same order. Therefore, the length of the stream is defined by DatavalueCount. For each source node, there must be DatavalueCount \(\times\) (4 bytes + 8 bytes).

The following image shows how a binary file with a DatavalueCount value of 8 would be laid out.

_images/localMigrationBFF.jpg
How to create migration files

You can create the JSON metadata and binary migration files needed by EMOD to run simulations from either CSV or JSON data using Python scripts provided by IDM. You can assign the same probability of migration to each individual in a node (vector or human), or you can assign different migration rates based on age and/or gender (human only).

Note

The IdReference must match the value in the demographics file. Each node can be connected a maximum of 100 destination nodes.

For both scripts, use one of the following migration types:

  • LOCAL_MIGRATION

  • AIR_MIGRATION

  • REGIONAL_MIGRATION

  • SEA_MIGRATION

For regional migration, you may want to set up migration such that if a node is not part of the network, the migration of individuals to and from that node considers the closest road hub city. You can do this by constructing a Voronoi tiling based on road hubs, with each non-hub connected to the hub of its tile.

Create from CSV input

To use the same average migration rate for every individual in a node, create the migration files from a CSV input file.

  1. Create a CSV file with the following three columns (do not include column headers):

    From_Node_ID

    The origin node, which must match a node ID in the demographics file. You do not need to have the same number of entries for each From_Node_ID.

    To_Node_ID

    The destination node, which must match a node ID in the demographics file.

    Rate

    The average number of trips per day.

  2. Run the convert_txt_to_bin.py script using the command format below:

    python convert_txt_to_bin.py [input-migration-csv] [output-bin] [migration-type] [idreference]

This will create both the metadata and binary file needed by EMOD.

Create from JSON input

To vary the average migration rate based on age and/or gender, create the migration files from a JSON input file.

  1. Create a JSON file with the structure described in the sections below.

  2. Run the convert_json_to_bin.py script using the command format below:

    python convert_json_to_bin.py [input-json] [output-bin] [migration-type]

This will create both the metadata and binary file needed by EMOD.

JSON parameters

The following parameters are used in the JSON file for migration file generation.

Parameter

Data type

Description

IdReference

string

The metadata identifier used to generate the NodeID associated with each node in a simulation. The values for IdReference and NodeID must be the same across all input files used in a simulation.

Interpolation_Type

enum

The method by which to interpolate the age dependent rate data. Accepted values are LINEAR_INTERPOLATION and PIECEWISE_CONSTANT.

Gender_Data_Type

enum

Whether age data is provided for each gender separately or is the same for both. Accepted values are ONE_FOR_BOTH_GENDERS and ONE_FOR_EACH_GENDER.

Ages_Years

array

An array that defines the age bins by which to separate the population and define migration rates. The first value defines the upper bound of a bin starting at zero.

Node_Data

JSON object

The structure that contains the migration rate data for each node.

From_Node_ID

integer

The origin node for which to define migration rate.

Rate_Data

array

The structure that contains migration rate data for a single destination node.

To_Node_ID

integer

The destination node for which to define migration rate.

Avg_Num_Trips_Per_Day_Both

array

The array that lists the average number of trips per day for each age bin defined in AgesYears (male and female).

Avg_Num_Trips_Per_Day_Female

array

The array that lists the average number of trips per day for each female age bin defined in AgesYears.

Avg_Num_Trips_Per_Day_Male

array

The array that lists the average number of trips per day for each male age bin defined in AgesYears.
Example file 
{
    "IdReference": "ABC",
    "Interpolation_Type": "PIECEWISE_CONSTANT",
    "Gender_Data_Type": "ONE_FOR_EACH_GENDER",
    "Ages_Years": [14.99, 15, 45, 75, 105],
    "Node_Data": [{
        "From_Node_ID": 1,
        "Rate_Data": [{
            "To_Node_ID": 2,
            "Avg_Num_Trips_Per_Day_Male": [0.0, 0.1, 0.2, 0.3, 0.0],
            "Avg_Num_Trips_Per_Day_Female": [0.0, 0.3, 0.2, 0.1, 0.0]
        }]
    }, {
        "From_Node_ID": 2,
        "Rate_Data": [{
            "To_Node_ID": 1,
            "Avg_Num_Trips_Per_Day_Male": [0.0, 0.2, 0.5, 0.3, 0.0],
            "Avg_Num_Trips_Per_Day_Female": [0.0, 0.5, 0.3, 0.2, 0.0]
        }]
    }]
}
Vector migration files

Vector migration files describe the rate of migration of vectors out of a geographic node analogously to human migration (see Migration files for more information), although vector migration does not support gender and age parameters. Vector migration is one way, such that each trip made by a vector is independent of previous trips made by the vector. The rates in the file are used to control whether or not a vector will migrate: the rate specified is used to get a “time to leave on trip” value from an exponential distribution. If the value is less than one day, then the vector will migrate.

In order to use a vector migration file, the configuration parameter Vector_Sampling_Type must be set to either TRACK_ALL or SAMPLE_IND_VECTORS. If default geography is used (the configuration parameter Enable_Demographics_Builtin is set to 1, and Default_Geography_Initial_Node_Population and Default_Geography_Torus_Size are configured), vectors can only migrate (or not) using Enable_Vector_Migration, and vector migration files are not used.

Vectors can migrate regionally or locally, and there are multiple modifiers available to influence migration to particular nodes over others. See Configuration parameters for more information on the parameters governing vector migration.

JSON metadata file

The metadata file is a JSON-formatted file that includes a metadata section and a node offsets section. The Metadata section contains a JSON object with parameters, some of which are strictly informational and some of which are used by Eradication.exe. However, the informational ones may still be important to understand the provenance and meaning of the data.

Parameters

The following parameters can be included in the vector migration metadata file:

Parameter

Data type

Description

Author

string

The author of the file.

DatavalueCount

integer

(Used by EMOD.) The number of outbound data values per node (max 100). The number must be the same across every node in the binary file.

DateCreated

string

The day the file was created.

IdReference

string

(Used by EMOD.) A unique, user-selected string that indicates the method used by EMOD for generating NodeID values in the input files. For more information, see Input files.

NodeCount

integer

(Used by EMOD.) The number of nodes to expect in this file.

NodeOffsets

string

(Used by EMOD.) A string that is NodeCount \(\times\) 16 characters long. For each node, the first 8 characters are the origin NodeID in hexadecimal. The second 8 characters are the byte offset in hex to the location in the binary file where the destination NodeIDs and migration rates appear.

Tool

string

The script used to create the file.
Example 
{
    "Metadata": {
        "Author": "JDoe",
        "Tool": "convert_txt_to_bin.py",
        "IdReference": "Household-Scenario-Small",
        "DateCreated": "Fri Aug 21 08:20:26 2015",
        "NodeCount": 7,
        "MigrationType": "LOCAL_MIGRATION",
        "DatavalueCount": 8
    },
    "NodeOffsets": "0000000100000000000000020000006000000003000000C00000000400000120000000050000018000000006000001E00000000700000240"
}
Climate files

There are two general types of climate files usable by EMOD, namely, climate files generated through actual data, referred to as “climate by data,” and climate files generated from the Koppen classification system, referred to as “climate by Koppen.” Climate data (both types) is contained in a set of two files, a metadata file with header information (<name>.bin.json) and a binary data file (<name>.bin).

The metadata file is a JSON-formatted file that includes a metadata section and a node offsets section. The Metadata parameter contains a JSON object with parameters, some of which are strictly informational and some of which are used by Eradication.exe. However, the informational ones may still be important to understand the provenance and meaning of the data.

In a second section, the NodeOffsets parameter contains a list of hex-encoded 16-byte values used to find the data for each given node (the NodeID).They are not 16-byte offsets, but instead, two 8-byte hex-encoded character strings. This encoding includes the source NodeID. You can map the binary data to its corresponding source NodeID by using the NodeOffset information.

The binary file contains the climate data in a sequential stream. In other words, it presents all the data for the first node, then all the data for the second node, all the way through to the last node.

To use the climate files, you must set Climate_Model to either “CLIMATE_BY_DATA” or “CLIMATE_BY_KOPPEN”, as appropriate, in the configuration file. There are also additional parameters in the configuration file you can use to scale or otherwise modify the data included in the climate files.

Climate by data

Climate by data files contain real data gathered from weather stations in the region to be simulated. This includes rainfall, temperature, relative humidity, and so on. At this time, the EMOD executable (Eradication.exe) reads land temperature data, but does not use the data in any calculations. IDM clones the air temperature and uses that as the land temperature in the climate data files. If you are going to be constructing your own climate files, we advise you to do the same.

Metadata file

The following parameters in the metadata section are informational:

Parameter

Data type

Description

DateCreated

string

The day the file was created.

Author

string

The author of the file.

OriginalDataYears

string

The years from which the original data was derived.

StartDayOfYear

string

The day of the year representing the first day in the climate file.

DataProvenance

string

The source of the data.

The following parameters in the metadata section are used by Eradication.exe:

Parameter

Data type

Description

IdReference

string

A unique, user-selected string that indicates the method used for generating NodeID values in the input file. For more information, see Input files.

NodeCount

integer

The number of nodes to expect in this file.

DatavalueCount

integer

The number of data values per node. The number must be the same across every node in the binary file.

UpdateResolution

enum

The time resolution of the climate file. Available values are:

  • CLIMATE_UPDATE_YEAR

  • CLIMATE_UPDATE_MONTH

  • CLIMATE_UPDATE_WEEK

  • CLIMATE_UPDATE_DAY

  • CLIMATE_UPDATE_HOUR

An example of climate by data metadata is as follows:

{
    "Metadata": {
        "DateCreated": "Sun Sep 25 19:02:09 2011",
        "Tool": "createclimateheader.py",
        "Author": "authorName",
        "IdReference": "Gridded world grump2.5arcmin",
        "NodeCount": 1,
        "DatavalueCount": 3650,
        "UpdateResolution": "CLIMATE_UPDATE_DAY",
        "OriginalDataYears": "1990-1993",
        "StartDayOfYear": "January 1",
        "DataProvenance": "47 consecutive months of data were used to generate one average year of data that is repeated for 10 years"
    },
    "NodeOffsets": "144B07A400000000"
}
Binary file

The binary file is a stream of 4-byte floating point values that contain the data value at the data count position for a given node, running from 1 to the maximum data count value.

The binary format is as follows:

_images/climate-by-data-binary.jpg
Climate by Koppen

The Koppen classification system is one of the most widely used climate classification systems. The Koppen classification system makes the assumption that native vegetation is the best expression of climate.

Metadata file

The following parameters in the metadata section are informational:

Parameter

Data type

Description

DateCreated

string

The day the file was created.

Author

string

The author of the file.

DataProvenance

string

The source of the data.

Tool

string

The script used to create the file.

The following parameters in the metadata section are used by Eradication.exe:

Parameter

Data type

Description

IdReference

string

A unique, user-selected string that indicates the method used for generating NodeID values in the input file. For more information, see Input files.

NodeCount

integer

The number of nodes to expect in this file.

An example of climate by Koppen metadata is as follows:

{
    "Metadata": {
        "DateCreated": "Sun Sep 25 19:08:52 2011",
        "Tool": "createclimateheader.py",
        "Author": "authorName",
        "IdReference": "Gridded world grump2.5arcmin",
        "NodeCount": 2,
        "DataProvenance": "Köppen-Geiger Classification System from http://koeppen-geiger.vu-wien.ac.at/"
    },
    "NodeOffsets": "157D075200000000157E07520000000"
}
Binary file

The binary file parameters use the naming convention below to store the data.

Parameter

Data type

Description

KoppenIndexX

integer, 4 bytes

The Koppen Index value, with X running from 1 to the maximum number of nodes.

The binary format is as follows:

_images/climate-by-koppen-binary.jpg
Load-balancing files

If you are running a large simulation with multiple nodes, you may want to use a load balancing file to distribute the computing load among multiple cores. This can be especially helpful if the nodes vary considerably in size and, therefore, processing time. If no load balancing file is submitted, the Eradication.exe allocates simulation nodes to cores according to a checkerboard pattern.

For each simulation, the load balancing file contains information about the relative level of processing time required for each geographical node. The Eradication.exe can then allocate nodes to processors in such a way that the total processing required is evenly distributed across all processors.

To use a load balancing file, you must set the configuration parameter Load_Balance_Filename to the path to the load balancing file, relative to the input file directory. Load-balancing files can be JSON (JavaScript Object Notation) files, which are preferred, or binary files.

JSON file

A JSON load-balancing file contains the Load_Balance_Scheme_Nodes_On_Core_Matrix parameter, assigned an array of arrays, each of which represents a core and lists the NodeID of each node to be processed on that core. If any node contained in the demographics file is not listed in the load-balancing file, it will be processed on the first core.

For example, the load-balancing file shown below distributes the processing for 100 nodes across 4 cores, assigning more nodes to particular cores when those nodes require less processing time.

{
    "Load_Balance_Scheme_Nodes_On_Core_Matrix": [
        [1, 2, 3, 4, 5, 11, 12, 13, 14, 15, 21, 22, 23, 24, 25, 31, 32, 33, 34, 35, 45],
        [6, 7, 8, 9, 10, 16, 17, 18, 19, 20, 26, 27, 28, 29, 30, 36, 37, 38, 39, 40, 46, 47, 48, 49, 50, 56, 57, 58, 59, 60, 67, 68, 69, 70],
        [61, 62, 63, 64, 65, 66, 71, 72, 73, 74, 75, 76, 77, 81, 82, 83, 84, 85, 86, 87, 91, 92, 93, 94, 95, 96, 97],
        [41, 42, 43, 44, 51, 52, 53, 54, 55, 78, 79, 80, 88, 89, 90, 98, 99, 100]
    ]
}
Binary file

A binary load-balancing file starts with an initial unsigned 4-byte integer that indicates the number of nodes. Following that value is a series of 4-byte unsigned integers representing the NodeID values. The number of values will be equal to the previously read number of nodes. Following that, another series of 4-byte floating point values, with each value representing the relative processing time required for each geographic node. Again, the number of values will be equal to the previously read number of nodes. The series of values are set up such that the \(i^{th}\) entry in the series is equal to the cumulative proportion of the processing load for all the previous nodes 0 through \(i\).

The cumulative nature of each node’s value does not mean each node is assigned that amount for its processing load. Rather, it is a way to make the internal calculations more efficient. For example, if you had ten nodes and each node was assigned 10% of the load, the values assigned from node0 to node9 would be the following: 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 and 1.0.

The following diagram shows the format for the binary load-balancing file data:

_images/loadbalancingBFF.jpg
Serialized population file

To model a population with endemic disease, you cannot use a common modeling technique in which you introduce a disease outbreak to a naive population and analyze the immediate aftermath. Doing so would create a population missing the levels of natural immunity built up in the population due to the history of exposure to the pathogen. Instead, you must run the simulation for a period of time until the disease dynamics reach an equilibrium (aside from stochastic noise). This is known as steady state. The simulation output prior to that point is disregarded. This is a modeling concept known as simulation burn-in, borrowed from the electronics industry where the first items produced by a manufacturing process are discarded before the process is applied.

However, the time necessary to run simulations until this point can be significant, especially for large populations. Indeed, for endemic disease present at low absolute prevalence, you should simulate a larger population size that allows a small number of infected individuals to be represented.

EMOD avoids the need to run the burn-in period again and again with each simulation by using serialization to save the population state after it reaches equilibrium. Then, when you want to begin a subsequent simulation investigating the outcome of a particular set of interventions, you can begin the simulation at that point rather than needing to re-run the burn-in period. You can serialize the population at multiple time steps during a simulation.

The serialized population files created are placed in the output directory and use the naming convention state-<timestep>.dtk. They are binary files that contain state information about every agent in a simulation: their health status, age, property values, and more. These files can be consumed by subsequent simulations to decrease run time.

Note

If you used repeating interventions during the burn-in period, those interventions will not continue based on the information in the serialized population file. Check your campaign file for repeating interventions and reconfigure them as needed for the period after burn-in.

Note

Not every type of intervention can be serialized. Please see Individual-level interventions and Node-level interventions for information on which interventions can be serialized.

See Simulation setup parameters for more information on configuration. See Serializing populations for more information on utilizing serialized populations in your simulations.

File format

Serialized population files are saved in JSON format. The file structure reflects the hierarchical structure of the objects in Eradication.exe. Each file starts with a header and then contains information about the simulation and the nodes. The following figure gives a rough overview of the JSON file structure (note that only vector or malaria simulations will have larval habitats, vector populations, or vector life cycles).

_images/serialized_file_format.png
Internal state

The structure of a serialized population file reflects the relations of objects in a simulation. On the highest level, a serialized population file contains a list with nodes, each node is the root of a tree. At the configured timestep the simulation calls Node::serialize() which then calls the serialized method of other members of this object. The Node class, for instance, contains a list with individuals, each individual has certain attributes and a list of infections. Each infection has certain attributes and might contain again a list of objects, etc. This continues until there are no more objects.

All objects that are instantiated from classes that inherit from ISerializable and implement the ISerializable interface can write itself to a buffer. It depends on the chosen writer how this buffer is written and where it is written. Here we assume that a JSON writer is used and that the writer adds variable names and formatting characters so that a valid JSON file is generated. A similar mechanism is at play when a simulation is loaded again from a file. The names of variables and their values are used to re-instantiate the same objects. Mind that EMOD is written in a compiled language, so the description of data and objects (classes and variable types) in the source code must match the data types in the serialized population file, e.g. if a variable is of type integer changing its value to a float won’t have the anticipated effect.

Multi-core

If the simulation is run on several cores, each core saves one file at the configured timestep(s). To continue the simulation one EMOD file must be defined per node.

File Name

The format for single core is: state-timestep.dtk e.g. state-00100.dtk

The format for multi-core is: state-timestep-core.dtk e.g. state-00100-001.dtk

Running a simulation

The EMOD executable (Eradication.exe) consumes the input files, configuration file, and, optionally, campaign file to run simulations that model disease dynamics and campaign efficacy. The simulation type controls the transmission mechanism of the disease. Each agent, whether human or vector, is simulated and follows a set of rules that govern their health and behavior.

You have a few different options for running simulations. The option you choose will depend upon whether you want to run one or more simulations, to run simulations locally or on a remote cluster (for large simulations or multiple simulations), or to run simulations for debugging the source code. This topic briefly describes the different options you have for running simulations. Because the model is stochastic, you must run simulations multiple times to produce scientifically valid results.

Run a single simulation

It is simplest to run a single simulation using the EMOD command-line options. This will run a single simulation and put the output files in a local directory.

You must either download the latest version of Eradication.exe from GitHub or clone the EMOD source from GitHub and build Eradication.exe yourself. This gives you access to the latest features and parameters for EMOD. To learn EMOD, you can also download the EMOD scenarios, which contains all input files needed to run local simulations that model a variety of disease scenarios.

Run multiple simulations

Because the EMOD model is stochastic, simulations must be run multiple times to return scientifically valid results. Therefore, you have the following options to run multiple simulations at a time, either locally or remotely on a high-performance computing (HPC) cluster. Generally, only small simulations should be run locally.

Many of these options are scripting languages that you can also use to modify the files consumed by EMOD, simplifying your workflow when running many simulations.

Run simulations for debugging

If you are helping advance the EMOD model by contributing to source code, there are other options for running simulations that provide debugging support. These options for running simulations are not recommended if you are not modifying the source code.

You can run a simulation locally from Visual Studio using the built-in debugger. You will be able to put in breakpoints and step through the code while inspecting the values of different state variables throughout the simulation.

You can use regression_test.py in the GitHub Regression directory to run multiple simulations on a cluster, including running the suite of regression tests run by the IDM testing team. For more information, see Regression testing.

Directory structure

Although there are many ways you can structure the files needed to run a simulation, we recommend the following to keep your files organized and simplify the file paths set in the configuration file or passed as arguments to Eradication.exe.

  • Place the configuration and campaign files needed for a simulation in the same directory. This is also known as the working directory.

    However, if you are using overlay files, you may want the default configuration or campaign file in a separate directory so they can be used with different overlay files for other simulations.

  • Place all demographics and climate files for a given region in the same directory.

  • Place output for a simulation in a subdirectory of the directory containing configuration and campaign files.

It is not important where you install Eradication.exe or the Eradication binary for Linux.

Run a simulation using the command line

Using command-line options is the simplest way to run an EMOD simulation. This topic describes the commands available for running simulations.

The EMOD executable (Eradication.exe) and Eradication binary for Linux also provide commands that provide information about the version of EMOD that is installed, such as available parameters and simulation types. For more information, see Generate a list of all available parameters (a schema). The examples below show the Windows Eradication.exe, but the options are the same for the Eradication binary for Linux on CentOS.

Command options

The following command-line options are available for running EMOD simulations. Paths can be absolute or relative to the directory you are in when running the commands, unless otherwise specified.

Simulation options

Long form

Short form

Description

--version

-v

Get version information.

--schema-path

Path to write the schema (the complete list of all parameters and associated information). If not specified, the schema will be written to stdout.

--config

-C

Path to the configuration file. If not specified, EMOD will look for a file named config.json in the current directory.

--input-path

-I

Path to the directory containing other input files (except the campaign file, which must be in the current directory). If not specified, EMOD will look for files in the current directory. The specific demographics, climate, etc. files to use must be listed in the configuration file.

--output-path

-O

Path to the directory where output files will be written. If not specified, EMOD will create an “output” directory and overwrite any previous output in that directory.

--dll-path

-D

Path to the EMODule root directory. For more information, see Custom reporters.

--python-script-path

-P

The path to Python scripts to use for pre- or post-processing.

The following options are for monitoring the progress of simulations running on an high-performance computing (HPC) cluster. They are optional for any simulation, but they must be used together. To monitor progress, listen for User Datagram Protocol (UDP) messages on the specified host and port.

HPC cluster options

Long form

Description

--monitor_host

The IP address of the commissioning/monitoring host. Set to “none” for no IP address.

--monitor_port

The port of the commissioning/monitoring host. Set to “0” for no port.

--sim_id

The unique ID for this simulation. This ID is needed for self-identification to the UDP host. Set to “none” for no simulation ID.

--progress

Send updates on the progress of the simulation to the HPC job scheduler.
Usage

  1. Open a Command Prompt window and navigate to the working directory, which contains the configuration and campaign files.

  2. Enter a command like the one illustrated below, substituting the appropriate paths and file names for your simulation:

    ../Eradication.exe -C config.json -I C:\EMOD\InputFiles -O Sim1Output

    If you do not specify anything when invoking Eradication.exe, it will not execute with all defaults, but will instead tell you how to invoke the --help command.

  3. EMOD will display logging information, including an errors that occur, while running the simulation. See Error and logging files for more information.

  4. Output files will be placed in the directory specified. For more information on how to evaluate and analyze the output, see Output files (reports).

Run a simulation using mpiexec

The application mpiexec is used to run multi-node simulations in parallel. Eradication.exe is “single threaded”, so it uses only one core for processing. If you run a simulation with multiple geographic nodes using mpiexec instead of invoking Eradication.exe directly, multiple copies of Eradication.exe will be running, with one copy per core processing data for a single node at a time. Message Passing Interface (MPI) communicates between the cores when handling the migration of individuals from one node to another.

Although mpiexec can be used to run a simulation in parallel on your local computer, it is more often used to run complex simulations in parallel on an HPC cluster or several linked computers. Mpiexec is part of the Microsoft HPC Pack 2019 SDK (64-bit) installed as a prerequisite for building Eradication.exe from the EMOD source code. See EMOD source code installation for more information.

Note

If you get an error that the mpiexec command cannot be found, you must add the path to mpiexec to the PATH environment variable. For example, open Control Panel and add the path C:\Program Files\Microsoft HPC Pack 2012\Bin to PATH.

Usage

  1. Take note of the number of cores on your computer or cluster.

    If running locally, we recommend running mpiexec with one fewer cores than are available, so one core is reserved for the operating system. The simulation can be run on all available cores and will complete faster, but the desktop will not be responsive.

  2. Open a Command Prompt window and navigate to the directory that contains the configuration and campaign files for the simulation.

  3. Invoke Eradication.exe using mpiexec as follows, replacing the number of cores, paths, and command options as necessary for your environment. See Run a simulation using the command line for more information about the command options available for use with Eradication.exe.

    mpiexec -n 3 ..\Eradication.exe --config config.json --input-path ..\InputDirectory\Garki --output-path OutputGarki

Mpiexec will start multiple copies of Eradication.exe as specified by -n. Those instances will communicate with each other via MPI. If all cores are on a single computational node or host, they will use internal networking to carry the MPI packets.

You can also link together several computers with MPI using the mpiexec -host option. For example, if you were using six cores on two computers, you could run three copies of Eradication.exe on the first computer, and three more could be run on the second computer. Again, this assumes that each computer has at least three cores.

For more information about mpiexec, see MSDN.

Run a simulation using Python

If you used Python to create or modify JSON files as shown in Example scripts to modify a configuration file, it may be convenient to invoke Eradication.exe to run a simulation from a Python script. One way of doing this is shown below using the subprocess package.

import subprocess

# specify paths
binary_path = "binDirectory\Eradication.exe"
input_path = "inputDirectory\Namawala\

# commission job
subprocess.call( [binary_path, "-C", "config.json", "--input", input_path] )

See Run a simulation using the command line for more information about the command options available for use with Eradication.exe.

Run a simulation using MATLAB

If you used MATLAB to create or modify JSON files as shown in Example scripts to modify a configuration file, it may be convenient to invoke Eradication.exe to run a simulation from a MATLAB script. One way of doing this is shown below using the dos command.

exe_name = fullfile( binDirectory, 'Eradication.exe' );
exe_args = [ '-C config.json -I ', fullfile( inputDirectory, 'Namawala' ) ];
[status,result] = dos( ['cd ', WorkDirectory, ' && ', exe_name, ' ', exe_args ], '-echo' );

See Run a simulation using the command line for more information about the command options available for use with Eradication.exe.

Speed up EMOD simulations

While small simulations can be run quickly on a local computer, the time and memory needed to complete a simulation can grow significantly when simulations become larger and more complex. For example, a single geographical node simulation might use 3 GB with one core in order to run successfully in one minute or less, while another simulation job may require 32 GB in a dual-core system in order to complete in approximately the same amount of time.

As your simulations grow, you will likely want to run simulations on an HPC cluster or take other steps to improve performance and reduce processing time. This topic describes many of the steps you can take to speed up EMOD simulations.

Parallel processing

The EMOD executable (Eradication.exe) is “single threaded”, meaning that for processing, it will use only one core. However, you can use the Message Passing Interface (MPI) to run multiple copies of Eradication.exe in parallel, either locally or on an HPC cluster. To run a simulation in parallel, you must invoke Eradication.exe with the mpiexec command. For more information, see Run a simulation using mpiexec.

Parameter settings

You can also set various parameters in the configuration file that will improve performance by scaling down the amount of data used or optimizing data processing. See the parameters listed in the sections below for guidance on making performance adjustments. See Configuration parameters for more information about each of the parameters.

Scaling and sampling
Simulation_Duration

Obviously, simulating a shorter timespan will take less processing time. However, the processing time is often driven by the number of infections or immune updates, so running a simulation after all infections have cleared may not increase processing time much. For more information, see Simulation setup parameters.

Individual_Sampling_Type

Instead of the default Individual_Sampling_Type setting of “TRACK_ALL”, you can speed up performance by sampling such that each individual object represents more than one person. For example, a simulation with a population of 1 million and sample rate of 0.1 would simulate 100,000 individuals, with each given a weight of 10. Sampling can be fixed at a particular rate or can adapt the rate based on certain criteria, such as age or immune state. However, you should be especially careful not to undersample simulations to the point where they are overly sensitive to rare stochastic events. For more information, see Sampling parameters.

Population_Scale_Type and x_Base_Population

Alternatively, you can simply reduce the total population of the simulation using Population_Scale_Type set to “FIXED_SCALING” and x_Base_Population set to less than one. For more information, see Scalars and multipliers parameters.

Processing
Num_Cores

For large, spatially distributed simulations, running the intra-node dynamics (for example, infection and immune dynamics) in parallel on multiple cores may be very advantageous. Ideally, the timing would be reduced inversely to the number of cores. However, there are costs to serializing individuals for migration over MPI, as well as considerations for balancing the CPU load on each core. These issues may be mitigated using a load balancing input file that is suitable to the geography being simulated. See Load-balancing files for more information.

Vector and malaria
Simulation_Type

“VECTOR_SIM” will run much faster than “MALARIA_SIM” if detailed intra-host dynamics are not “required.

Vector_Sampling_Type and Mosquito_Weight

The individual vector simulation (“TRACK_ALL_VECTORS”) will run more slowly than the cohort model (“VECTOR_COMPARTMENTS_NUMBER”). If the individual vector model must be used, you can improve performance by sampling some fraction of all vectors (“SAMPLE_IND_VECTORS”) where each mosquito has a weight configured by Mosquito_Weight, which is analogous to individual human sampling.

Infection_Updates_Per_Timestep

Using fewer intra-day updates to the malaria intra-host model will speed up simulation time. However, updates longer than three hours, such as eight per day, result in increasingly inaccurate dynamics.

Serializing populations

Some simulations can take a long time to run and the part you are really interested in analyzing isn’t until closer to the end. You’d like to save the state of the simulation just before the interesting stuff and then restart from that spot. This would allow you to iterate more quickly on different intervention strategies or just trying to understand what the simulation is doing better. EMOD supports this ability with a feature called “serialized populations.”

Serializing is the idea of saving the state of object so that there is enough information to recreate that object later. Deserializing is the reverse, where you create a new object but initialize it with the state that was saved. For more information, see serialization. This is analogous to the sleep feature in your computer: when you put it to sleep, it serializes data of the objects running the system; when you start it back up again, it recreates what was running from that saved state.

The serialized population feature in EMOD allows you to save the state of the people and restart from that saved state. This state includes the person’s health, infections, any interventions that they have, and more. This is especially useful when you need to create a population that has natural immunity to a pathogen (i.e. the pathogen is not novel and the population is not naive.) It is important to note that only events that have already occurred will be saved in the serialized population; for example, if the person has received only the first dose of a two dose vaccine when the simulation was serialized, the person will only have that one dose when the simulation is deserialized. The logic to give the person the second dose will still need to be added to the simulation.

Being able to serialize the population is a powerful feature and can save the researcher lots of time. However, it does have it is idiosyncrasies. The following information is intended to help the user understand how serialization works, how to write a file, how to read from a serialized file, and what parameters you can change when reading from a serialized file. For more information on the format of serialized files, see Serialized population file.

Example use cases

Serialization is useful for a variety of use-cases; we have outlined some common examples below.

Try out different interventions (Simple burn-in)

To create a population with an endemic disease, one option is to start with a naïve population and run simulation till disease dynamics reach an equilibrium. These simulation burn-in methods can take several years of simulation time until a steady state is reached. Saving the state to a file and continuing from this state reduces the time needed to model the effect of different interventions.

Burn-in is especially effective when exposure and individual immunity is age structured within the population. For example, if older individuals have higher antibody levels than younger individuals, due to prolonged exposure.

Ultimate Report

In case existing reports don’t have what you need, a serialized population file contains all the internal states of the people in the population. By saving serialized population files periodically and then extracting the data from these files, it is possible to gain very deep insights into the simulation. The extracted “report data” can then be processed with dtk_post_process or analyzers such that you only save the extracted data. The serialized population files can be very big so you could use this mechanism extract the data you want and then delete the large files.

Reconfigure (currently only larval habitats)

When a simulation is saved to file and then loaded again, the state is exactly the same as it was at the moment it was saved. There are options to reconfigure the simulation and read parameters from config.json instead of the serialized file. This is called “masking” and is controlled by the parameters Serialization_Mask_Node_Write and Serialization_Mask_Node_Read. These options are currently very limited and only work for larval habitats.

Create a synthetic population

Sometimes you want to create a large spatial scenario but you don’t want to wait to do burn-in just to get a population with reasonable immune systems. One way you could solve this problem is to run a smaller one-node scenario that has a prevalence similar to the overall area you are trying to model. You do a burn-in with this smaller scenario to generate a representative population and serialize that population at the end. You could then use the people in this representative scenario to generate a larger scenario and larger serialized file.

The state of all the individuals in a simulation is saved in a serialized population file. Python libraries exist to manipulate the file and can be used to create, remove, or change attributes of an individual.

Calibration Chaining

You can calibrate for a first period of time and then save the file. From that starting point, you can calibrate for the next period of time and so on.

Serialized population file

The information below describes how serialized files work. For more information on formatting serialized files, see Serialized population file.

Internal state

A serialized population file contains the internal state of the objects used to create the population in that realization. This internal state includes lots of implementation details that are not really meant for user consumption. With the right tools, you can open a file and look at its contents. The core of the information is in JSON format. For more detailed information, see Serialized population file.

These implementation details are frequently the things that cause the data in the file to change. When adding a new feature or fixing a bug, the developer might need to add new state to an object that needs to be maintained from timestep to timestep. This new state could be related to a new model or something that just makes the model run faster. Either way, this new information needs to be written to and read from the file. Hence, if you are updating from an older version to a newer one, you will likely need to regenerate your serialized files.

Population

The file contains the state of a human population and, for vector or malaria simulations, a vector population. All the nodes that belong to the simulation and the individuals that belong to a node are saved. Furthermore, each individual has an internal state and certain characteristics that belong to it, like infections, drugs or interventions. Thus, interventions that were distributed before the population was saved are still active. Node-level interventions or event coordinators are not saved because they are not part of the population: they can change they state of a population but are not part of it.

Similar to individuals, vectors and their habitats are saved.

Multi-core

Serializing a simulation also works on a multi-core system: one file per core is written. To continue the simulation all the created files must be defined in config.json and must use the same number of cores.

Upgrading to new executable

There is a strong dependency between the program code and the serialized file. Changing the EMOD executable or modifying source code (especially removing, renaming or adding variables) will require a rerun of the burn-in.

File sizes

Serialized population files can be quite large - sometimes on the order of gigabytes. This is mostly impacted by the size of your population (and for vector or malaria simulations, the size of the vector population). Since the file already makes use of compression, compressing a serialized file will provide minimal benefit.

The size of the file can become a memory issue when reading from the serialized file. Reading large files, for example over 5 gigabytes, requires a lot of memory to both read the file and create the objects from that file. The amount of RAM you have available for each of your simulations should factor into how large of a file you want to use. If this is a concern, one can set the parameter logLevel_Memory to DEBUG when running the simulation to serialize. This will tell you how much memory will be needed for the simulation after you read it back in. However, it does not include the memory needed to read the file. If you are allowed 8-GB of memory per core, your simulation is using 7-GB, and your file is 2-GB, you could be in danger of using more memory than you are allowed.

Creating and editing serialized populations
Saving

To create and save serialized population files, configure the following parameters and then run a simulation. The output serialized population file is a binary file that is saved with a name similar to state-[timestamp].dtk, where [timestamp] is number of timesteps in the data file (e.g. state-00100.dkt). Note that the size of the population impacts the size of the file, so working with smaller populations will create smaller serialized files.

Config parameters

The following configuration parameters must be set to create serialized files:

Parameter

Data type

Default

Description

Serialized_Population_Writing_Type

enum

NONE

The type of serialization to perform. NONE for no serialization; TIME to use the definition from Serialization_Times; and TIMESTEP to use definition from Serialization_Time_Steps.

Serialization_Times

array of floats

[]

The list of times at which to save the serialized state to file. 0 indicates the initial state before simulation, ‘n’ indicates the time to serialize in terms of start time and step size, rounded up to the nearest timestep. Time is in term of days.

Serialization_Time_Steps

array of integers

[]

The list of timesteps after which to save the serialized state to file. 0 indicates the initial state before simulation, n indicates after the nth timestep.

Serialization_Mask_Node_Write

integer

0

A bitmask that defines what is NOT written to the file. O implies write everything to the file, 16 implies do NOT write larval habitats to the file.

Serialization_Precision

enum

REDUCED

REDUCED is used to reduce the size of the serialized file. FULL give more floating point precision but creates larger files. FULL precision is needed if you want the continuing simulation to be exactly the same as if you didn’t start from a serialized file.
Example JSON

The following example will save the population on day 50 and day 100.

{
    "Serialized_Population_Reading_Type": "NONE",
    "Serialized_Population_Writing_Type": "TIME",
    "Serialization_Times": [
        50,
        100
    ],
    "Serialization_Mask_Node_Write": 0,
    "Serialization_Precision": "REDUCED"
}
Starting from

You include and use serialized population files in a simulation by configuring the following parameters. If you do not specify an accurate path and filename, EMOD generates an error.

Config parameters

The following configuration parameters must be set to create serialized files:

Parameter

Data type

Default

Description

Serialized_Population_Reading_Type

enum

NONE

Set to READ to enable reading from a serialized population file, set to NONE otherwise.

Serialized_Population_Path

string

empty string

The root path for the serialized population files.

Serialized_Population_Filenames

array of strings

[]

An array of filenames with serialized population data. The number of filenames must match the number of cores used for the simulation. The path to the files is defined in Serialized_Population_path.

Serialization_Mask_Node_Read

integer

0

This is a bitmask to control what data is loaded. 0 implies loading all of the data in the serialized file. 16 implies that we do not load the larval habitat data from the serialized file and use the parameters in the configuration file instead.

Enable_Random_Generator_From_Serialized_Population

boolean

0

Determines if the random number generator should be extracted from a serialized population file. Enabling this (set to 1) starts a simulation from this file with the exact same random number stream and location in that stream as when the file was serialized.
Example JSON

In this example a population is loaded from file ./my_files/state-00050.dtk. Everything saved in the file is read (“Serialization_Mask_Node_Read”: 0).

{
    "Serialized_Population_Writing_Type": "NONE",
    "Serialized_Population_Reading_Type": "READ",
    "Serialized_Population_Path": ".",
    "Serialized_Population_Filenames": [
        "state-00050.dtk"
    ],
    "Serialization_Mask_Node_Read": 0
}
Using larval habitats in the configuration file when reading from a serialized file

Sometimes when you are using a serialized file, you want to change the larval habitat for the vectors. To do this, you first need to set the Serialization_Mask_Node_Read parameter to 16. If there is habitat data in the file, it will not be used. The habitat you have configured in the configuration file will be used instead. WARNING: You MUST still have the same habitat types. You can add new ones but you you cannot remove them. The eggs and larva that are in the file need a definition to go to. However, they will use the new parameter values (for example, for Max_Larval_Capacity).

If the serialized file was created without the habitat (Serialization_Mask_Node_Write = 16), then the application will automatically generate habitat from the configuration parameters independent of setting Serialization_Mask_Node_Read = 16.

Manipulating

IDM provides a Python package EMOD-Py with installation instructions and documentation to manipulate a serialized population file.

The following provides a list of a few scripts that are available in EMOD-Py. Please see the package for others and more details.

Remove all genomes - When using Full Parasite Genetics, this script will remove all of the genomes from the existing infections. This can be helpful when you want to replace existing genomes with new ones.

Remove all infections - Sometimes you want people to have developed immunity but want to control who is infected when the simulation starts. This script allows you to remove all the infections from the humans (and vectors) so that you can determine who gets infected and when.

Remove all interventions - Like removing infections, you might just want a certain demographic of people and their immunities but you want to determine what interventions they have.

Add/remove an individual property - Sometimes you need to add a new IP to the population in order to implement a new intervention strategy. If so, it is better to use a script and add the new IP to the population. This will ensure things work like you expect.

Modify vector habitat - You might have situation where there is a change in the environment like a period of drought or extra long rainy season. If so, you might want the ability to change the habitat to reflect this environmental change.

Changing parameters

When you are reading from a serialized file, you have a contention between what is in the serialized file and what is in the input files. Currently, there is not a good way to know what parameters you can change and which ones you cannot. This section is intended to provide some high level guidance.

Changing serialized configuration parameters

Not every parameter can be changed after the creation of serialized files. The following overview is intended as high-level guidance for what changes can and cannot be made.

Simulation parameters
Cannot be changed

  • Simulation_Type The type of simulation determines the parameters and the types of objects in the file.

  • Enable_Demographics_Builtin

  • Default_Geography_Initial_Node_Population

  • Default_Geography_Torus_Size

  • Node_Grid_Size This is not serialized; the value is loaded from the configuration file every time a simulation is created.

  • Enable_Interventions

  • Campaign_Filename

  • Load_Balance_Filename You must use the same number of cores that you used when you created the serialized file. The nodes will be on the those same cores.

  • Malaria_Model

Can be changed

  • Simulation_Duration This controls the number of days the simulation will run from the day it starts (when the executable begins executing).

  • Start_Time It can be useful to set this parameter to the day that the file was serialized plus one timestep. For example, if you serialized the file on day 100 and your timestep was 5, setting Start_Time = 105 will make the appearance that the simulation started right where it left off. It is also fine to have it start at 0.

  • Custom_Individual_Events You can easily add new events but must be careful when removing them. If an individual has a serialized intervention that emits the event that you delete, you should get an error de-serializing the file because the event is unknown.

  • Malaria_Drug_Params These parameters are not serialized so they can be changed. However, there are a few caveats:

    # If you have handed out drugs to people and the people have the drugs in them when you serialize, then you must have drugs in Malaria_Drug_Params with the same name. When the people are de-serialized, the drug intervention will look for the drug parameters with the same name as when the person was serialized. # A person who had drugs when serialized will still be in that state when de-serialized. The previous values created the person’s current state; changing the parameters will not reverse the previous effects. # Given the person’s state when serialized, the new parameters could cause weird effects depending on how the parameters are changed. For example, if you shorten a delay significantly, the drug could suddenly have no remaining effect.

  • Serialized_Population_Reading_Type

  • Serialized_Population_Writing_Type

  • Serialized_Population_Path

  • Serialized_Population_Filenames

  • Enable_Random_Generator_From_Serialized_Population

Individual parameters
Can be changed

  • Enable_Aging This parameters is not serialized and can be changed. However, if it was enabled and you disable it, people will be frozen at whatever age they were during serialization. Conversely, people will start aging when you enable it. This could mean everyone is the same age.

  • Infection_Updates_Per_Timestep This parameter is not serialized and can be changed. However, it could impact some interventions like drugs.

  • Enable_Superinfection

  • Max_Individual_Infections

  • Age_Dependent_Biting_Risk_Type

  • Newborn_Biting_Risk_Multiplier

Infection parameters
Can be changed

  • Incubation_Period_Distribution This parameter is not serialized and can be changed, including the extra parameters used in defining the distribution.

  • Infectious_Period_Distribution This parameter is not serialized and can be changed, including the extra parameters used in defining the distribution. (Note this parameter is not used in malaria simulations.)

  • Base_Infectivity This parameter is not serialized and can be changed, including the extra parameters used in defining the distribution. (Note this parameter is not used in malaria simulations.)

  • Malaria_Strain_Model This parameter is not serialized and can be changed but it will only impact new infections.

Changing serialized campaign parameters

Campaigns are used to distribute interventions to the people in the scenario. When it comes to serialization, only the interventions that have already been distributed to people are serialized. For example, if you are distributing drugs to people when they exhibit clinical symptoms, then people who exhibited symptoms before the file was serialized should have their drugs. However, to get drugs to the people exhibiting symptoms after reading from the serialized file, you will need to add the campaign events that do that distribution.

The following overview is intended as high-level guidance for what changes can and cannot be made.

Campaign events & event coordinators

See Events and event coordinators for more information on events and event coordinators.

Events

Campaign events are not serialized.

Event coordinators

Event coordinators are not serialized. Any coordinator that was periodically distributing interventions will need to be added to the new campaign.

Interventions
Node-level interventions

Node-level interventions are not serialized. Any node-level vector control will need to be redistributed. Any NodelLevelHealthTriggeredIV that is responding to events will also need to be redistributed.

Individual-level interventions

To see which campaign classes can be serialized see Individual-level interventions.

Some may be serialized. If the intervention has been distributed to the person, then it will be serialized. For example, if the person received a bednet before the file was serialized, then the serialized person will still have that bednet with however much its performance has decayed. However, if the person is supposed to receive drugs if they become clinical and the person has not been clinical yet, then you need to include that drug logic in your new campaign.

Changing serialized demographics parameters

Not every parameter can be changed after the creation of serialized files. The following overview is intended as high-level guidance for what changes can and cannot be made.

Population-level parameters
Cannot be changed

  • Age_Initialization_Distribution_Type

  • Birth_Rate_Boxcar_Forcing

  • Birth_Rate_Dependence

  • Birth_Rate_Time_Dependence

  • Birth_Rate_Sinusoidal

  • Death_Rate_Dependence

  • Enable_Birth

  • Enable_Demographics_Birth

  • Enable_Demographics_Risk

  • Enable_Initial_Prevalence

  • Enable_Natural_Mortality

  • Enable_Vital_Dynamics

  • Population_Scale_Type

  • x_Base_Population

  • x_Birth

  • x_Other_Mortality

Can be changed

  • Enable_Disease_Mortality

NodeAttributes
Cannot be changed

  • InitialPopulation

  • InitialVectorsPerSpecies

  • LarvalHabitatMultiplier This is read but not used. Both the LarvalHabitatMultiplier and the x_Temporary_Larval_Habitat are applied when a habitat is created at the beginning of a simulation. When the habitat is serialized, it is stored with the results of these multipliers. If you are reading from a serialized file and Serialization_Mask_Node_Read = 0, then you ignore both LarvalHabitatMultiplier and x_Temporary_Larval_Habitat settings and just use what was stored in the serialized file. If Serialization_Mask_Node_Read = 16, then we create new habitats and ignore what is in the serialized file. We do use the x_Temporary_Larval_Habitat setting to adjust the habitat, but it is a known issue that we don’t also use LarvalHabitatMultiplier.

Can be changed

  • Altitude

  • BirthRate

  • Latitude

  • Longitude

  • NodePropertyValues

  • Airport

  • Region

  • Seaport

IndividualAttributes
Cannot be changed

  • AgeDistributionFlag, AgeDistribution1, AgeDistribution2

  • SusceptibilityDistributionFlag, SusceptibilityDistribution1, SusceptibilityDistribution2

  • PrevalenceDistributionFlag, PrevalenceDistribution1, PrevalenceDistribution2

Can be changed (with dependencies)

  • FertilityDistribution: If Birth_Rate_Dependence was serialized with the value INDIVIDUAL_PREGNANCIES_BY_AGE_AND_YEAR, then you can change this distribution.

  • MigrationHeterogeneityDistributionFlag, MigrationHeterogeneityDistribution1, MigrationHeterogeneityDistribution2: These values can be can be changed if Enable_Migration_Heterogeneity is set to 1 in config.json and Enable_Demographics_Birth was serialized true/on. Existing individuals will get new migration modifiers. For newborns to get it Enable_Demographics_Birth needed to be serialized with a value of 1.

  • MortalityDistributionFlag, MortalityDistribution1, MortalityDistribution2: These can be changed if Enable_Natural_Mortality was serialized as true/on. Which distribution is used depends on the serialized value of Death_Rate_Dependence.

  • RiskDistributionFlag, RiskDistribution1, RiskDistribution2: These can be changed if Enable_Demographics_Risk & Enable_Demographics_Birth were serialized as true/on. However, it will only impact newborns. If you had serialized Enable_Demographics_Risk and not Enable_Demographics_Birth, these parameters will do nothing.

IndividualProperties
Cannot be changed

  • Remove value from existing key You cannot remove an existing value from a key if the population that has been serialized has this value.

Can be changed

  • Add new value to existing key

  • Change Initial_Distribution You can change the Initial_Distribution if you just want to change the likelihood that a newborn will have a specific IndividualProperties value. It will not impact the how the IndividualProperties values are distributed in the population that is serialized.

Special cases

  • Add/Remove IP/Key Some may be changed, but it is not recommended. It is best practices to not add or remove IndividualProperties values unless you also update the serialized file. However, if you remove a value and do not ever reference it in the simulation that is running from the serialized file, the simulation may run without errors. If you add values and don’t add them to the people in the serialized file, then you should not reference it until all the people that came from the serialized file have died.

Changing serialized climate parameters

Climate data can be changed as it is not serialized. However, if the serialized file was created with weather data files, then it is best practices to save the file such that when the simulation is restarted the time of the year remains the same. For example, if you saved the file at the end of the dry season and start the simulation at the beginning of the dry season, the pressure on the vectors will be twice as long as normal and could impact results.

Note that currently only vector and malaria simulations utilize climate files.

There are couple ways to do this:

# Always save your serialized files on 365 day intervals. This way you know that the first day of the simulation that is reading from the file will start on January 1st. # Use the configuration parameter Start_Time to have the scenario start the day after the simulation was serialized. This can allow you to start right where you left off.

Editable climate parameters

  • Climate_Model

  • Climate_Update_Resolution

  • Enable_Climate_Stochasticity

  • Enable_Rainfall_Stochasticity

  • Air_Temperature_Variance

  • Land_Temperature_Variance

  • Relative_Humidity_Variance

Climate_Model = CLIMATE_CONSTANT

  • Base_Air_Temperature

  • Base_Land_Temperature

  • Base_Rainfall

  • Base_Relative_Humidity

Climate_Model = CLIMATE_BY_DATA

  • Air_Temperature_Filename

  • Land_Temperature_Filename

  • Rainfall_Filename

  • Relative_Humidity_Filename

  • Air_Temperature_Offset

  • Land_Temperature_Offset

  • Rainfall_Scale_Factor

  • Relative_Humidity_Scale_Factor

Climate_Model = CLIMATE_KOPPEN

  • Koppen_Filename

Changing serialized migration parameters

Not every parameter can be changed after the creation of serialized files. The following overview is intended as high-level guidance for what changes can and cannot be made.

Migration_Model

  • ON-to-OFF: If you turn it off, there will be no human migration even if the serialized file had trips planned or people were waiting to return.

  • OFF-to-ON:

    • If the file was serialized with migration off, and you set the Migration_Model to FIXED_RATE_MIGRATION and set Migration_Pattern to SINGLE_ROUND_TRIPS/RANDOM_WALK_DIFFUSION, then the migration will work as expected.

    • If the file was serialized with migration off, and you set Migration_Pattern = WAYPOINTS_HOME, then the serialized people will be fixed at one waypoint. Newborns will get the value of Roundtrip_Waypoints, but not the serialized people.

Migration_Pattern

This cannot be serialized. Changing the value will change behavior but not in a guaranteed way. For example, if you change it from SINGLE_ROUND_TRIPS to WAYPOINTS_HOME, Roundtrip_Waypoints is always 1 for the people that were serialized. Newborns should get the new value.

Note that while the type of Migration_Pattern cannot be changed, other migration parameters can be changed under specific circumstances.

Migration_Pattern = SINGLE_ROUND_TRIPS

The following parameters can be changed as long as Migration_Pattern was serialized as SINGLE_ROUND_TRIPS. Changing this parameter will not impact those people who have determined their return trip before the file was serialized. After de-serializing, new decisions to return will use these parameters.

  • Air_Migration_Roundtrip_Probability

  • Family_Migration_Roundtrip_Probability

  • Local_Migration_Roundtrip_Probability

  • Regional_Migration_Roundtrip_Probability

  • Sea_Migration_Roundtrip_Probability

  • Air_Migration_Roundtrip_Duration

  • Family_Migration_Roundtrip_Duration

  • Local_Migration_Roundtrip_Duration

  • Regional_Migration_Roundtrip_Duration

  • Sea_Migration_Roundtrip_Duration

Enable_X_Migration

This includes the parameters Enable_Local_Migration, Enable_Region_Migration, Enable_Air_Migration, Enable_Sea_Migration, and Enable_Family_Migration.

  • ON-to-OFF: If Migration_Model = FIXED_RATE_MIGRATION and you turn these off, existing plans for migration - both out and return - will still occur. However, no new migration plans will be made.

  • OFF-to-ON: People should start migrating as designed.

Roundtrip_Waypoints

This can be changed under certain circumstances. If you serialized with Migration_Pattern set to WAYPOINTS_HOME, you can change this but it will NOT change the number of waypoints for the people that were serialized. All the people being born after de-serializing will receive the new number of waypoints.

Vector migration

Vector migration can be changed when reading from a serialized file. Vectors migrate differently then humans: each timestep a vector considers migrating; if they decide to migrate, they then go to the new node. They do not save state. Hence, if you turn it on or off, vectors will act like the new situation was always this way. NOTE however that Only female adult vectors migrate.

The following parameters can be changed:

  • Enable_Vector_Migration

  • Enable_Vector_Migration_Local

  • Enable_Vector_Migration_Regional

  • Vector_Migration_Filename_Local

  • Vector_Migration_Filename_Regional

  • Vector_Migration_Modifier_Equation

  • Vector_Migration_Food_Modifier

  • Vector_Migration_Habitat_Modifier

  • Vector_Migration_Stay_Put_Modifier

  • x_Vector_Migration_Local

  • x_Vector_Migration_Regional

Changing reports in serialized simulations

Reports can be changed as they are not serialized. If a report has a channel that involves accumulating data over a period of time, it will start fresh with the new realization from the de-serialized data.

The following parameters can be changed:

  • Enable_Default_Reporting

  • Enable_Demographics_Reporting

  • Enable_Property_Output

  • Enable_Spatial_Output

  • Report_Event_Recorder

  • Enable_Vector_Species_Report

The following report parameters can be changed; their new values will only impact the reports in new realizations from the deserialized data:

  • Report_Detection_Threshold_Blood_Smear_Parasites

  • Report_Detection_Threshold_Blood_Smear_Gametocytes

  • Report_Detection_Threshold_PCR_Parasites

  • Report_Detection_Threshold_PCR_Gametocytes

  • Report_Detection_Threshold_PfHRP2

  • Report_Detection_Threshold_True_Parasite_Density

  • Report_Detection_Threshold_Fever

  • Report_Parasite_Smear_Sensitivity

  • Report_Gametocyte_Smear_Sensitivity

Output files (reports)

After the simulation finishes, a reporter extracts simulation data, aggregates it, and outputs it to a file (known as an output report). Most of the reports are also JSON files, the most important of which is InsetChart.json. The InsetChart.json file provides simulation-wide averages of disease prevalence at each time step.

After running a simulation, the simulation data is extracted, aggregated, and saved as an output report to the output directory in the working directory. Depending on your configuration, one or more output reports will be created, each of which summarize different data from the simulation. Output reports can be in JSON, CSV, or binary file formats. EMOD also creates logging or error output files.

The EMOD functionality that produces an output report is known as a reporter. EMOD provides several built-in reporters for outputting data from simulations. By default, EMOD will always generate the report InsetChart.json, which contains the simulation-wide average disease prevalence by time step. If none of the provided reports generates the output report that you require, you can create a custom reporter.

_images/output.png

If you want to visualize the data output from an EMOD simulation, you must use graphing software to plot the output reports. In addition to output reports, EMOD will generate error and logging files to help troubleshoot any issues you may encounter.

Using output reports

By default, the output report InsetChart.json is always produced, which contains per- time step values accumulated over the simulation in a variety of reporting channels, such as new infections, prevalence, and recovered. EMOD provides several other built-in reports that you can produce if you enable them in the configuration file with the Output settings parameters. Reports are generally in JSON or CSV format. If none of the built-in output reports provide the data you need, you can use a custom reporter that plugs in to the Eradication.exe as an EMODule dynamic link library (DLL). For more information, see Custom reporters.

In order to interpret the output of EMOD simulations, you will find it useful to parse the output reports into an analyzable structure. For example, you can use a Python or MATLAB script to create graphs and charts for analysis.

Convert output to CSV format

Most output reports, including the primary InsetChart report, are in JSON format. If you are using R for data analysis, you may prefer a CSV report. You can easily convert the output format using Python post-processing using the icjjson2csv.py script provided in the EMOD GitHub repository. Provide the path to this script using the -P argument when you run Eradication.exe at the command line. See Run a simulation using the command line for more information.

Use Python to plot data

The example below uses the Python package JSON to parse the file and the Python package matplotlib.pyplot to plot the output. This is a very simple example and not likely the most robust or elegant. Be sure to set the actual path to your working directory.

import os
import json
import matplotlib.pyplot as plt

# open and parse InsetChart.json
ic_json = json.loads( open( os.path.join( WorkingDirectoryLocation, "output", "InsetChart.json" ) ).read() )
ic_json_allchannels = ic_json["Channels"]
ic_json_birthdata = ic_json["Channels"]["Births"]

# plot "Births" channel by time step
plt.plot( ic_json_birthdata[  "Data"  ], 'b-' )
plt.title( "Births" )
plt.show()
Use MATLAB to plot data

The example below uses the MATLAB toolbox JSONlab to parse an InsetChart.json file and plot one channel. This script uses JSONLab to parse the file into a usable form in MATLAB. This is a very simple example and not likely the most robust or elegant. Be sure to set the actual paths to JSONlab and your working directory.

% this sample uses JSONLab toolbox
addpath('PATH TO/jsonlab');

% open and parse InsetChart.json
ic_json = loadjson( fullfile( 'WorkingDirectoryLocation', 'output', 'InsetChart.json' ));
ic_json_allchannels = ic_json.Channels;
ic_json_birthinfo = ic_json_allchannels.Births;
ic_json_birthdata = ic_json_birthinfo.Data;
M = num2cell(ic_json_birthdata);

% plot "Births" channel by time step
plot(cell2mat(M));
title( 'Births' );
Custom reporters

Reporters extract simulation data, aggregate it, and output it to a file known as an output report. You can process the report data using tools, such as Python or MATLAB, to create graphs and charts for analysis. EMOD provides built-in reporters that are part of the EMOD executable (Eradication.exe) and can be enabled or disabled by setting parameters in the configuration file. See Output files (reports) for a list of the reports that are available using built-in reporters.

In addition, you can use custom reporters that extract data from the simulation and aren’t part of the Eradication.exe. A custom reporter is an EMODule that you plug in to EMOD. Custom reporters are not supported for CentOS. There are several reporters in the GitHub reporters directory that you can use. You may also want to build your own custom reporter to create a new output report.

_images/Reporters.png

The Eradication.exe must load the reporter when running a simulation to use it. If it is loaded, the output report will be automatically generated at the end of the simulation. There are three ways to specify their location so Eradication.exe can load them. Eradication.exe attempts to load the file using the following methods, in the order listed:

  1. Define the location of the dynamic link library (DLL) in a JSON-formatted file (emodules_map.json).

  2. Point to the location using the --dll-path command-line option when invoking Eradication.exe.

  3. Place the DLL in the working directory.

JSON file

First, Eradication.exe looks for an emodules_map.json file in the working directory. This map file lists specific reporter DLLs and their exact location. This is useful if you want to store all custom reporters in the same directory, but only want certain reporters to be used with each simulation.

The emodules_map.json file has three sections: diseases, interventions and reporters. Each section is a JSON array and contains the path to each EMODule in the array. Although reporter EMODules are the only type currently supported, empty disease and intervention sections must be included. You must use either a double backslash (\\) or a single forward slash (/) to represent a single backslash for the path. For example:

{
    "disease_plugins": [
         ],
    "interventions": [
         ],
    "reporter_plugins": [
        "C:\\src\\EMOD\\x64\\Release\\emodules\\reporter_plugins\\libreportpluginbasic.dll"
    ]
}
Command-line option

If a map file is not provided, you can include the --dll-path option when you invoke Eradication.exe. It must point to a directory with a reporter_plugins subdirectory that contains the reporters. The name of the directory does not matter, but the reporter must be in a subdirectory named reporter_plugins. Note that this method will load all reporters in the root directory specified.

If you build custom reporters as described here, the DLLs will be saved to the following directories, depending on the build configuration:

  • <EMOD source directory path>x64Releasereporter_plugins

  • <EMOD source directory path>x64Debugreporter_plugins

Therefore, if you do not move your reports, you will set --dll-path to one of the following paths:

  • <EMOD source directory path>x64Release

  • <EMOD source directory path>x64Debug

For more information on EMOD command-line options, see Run a simulation using the command line.

Working directory

Finally, if neither the the map file or the command-line option are provided, Eradication.exe looks for the reporter plug-in in the current working directory.

Error and logging files

When you run a simulation, EMOD will output basic error and logging information to help track the progress and help debug any issues that may occur. If you run the simulation on an HPC cluster, the cluster will generate additional logging and error files. See Troubleshooting EMOD simulations if you need help resolving an error.

Status

A status.txt file will be saved to the working directory that provides one output line per time step and includes the total run time of the simulation. A simulation with 50 time steps will look something like this:

Beginning Simulation...
1 of 50 steps complete.
2 of 50 steps complete.
3 of 50 steps complete.
4 of 50 steps complete.
5 of 50 steps complete.
6 of 50 steps complete.
7 of 50 steps complete.
8 of 50 steps complete.
9 of 50 steps complete.
10 of 50 steps complete.
11 of 50 steps complete.
12 of 50 steps complete.
13 of 50 steps complete.
14 of 50 steps complete.
15 of 50 steps complete.
16 of 50 steps complete.
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18 of 50 steps complete.
19 of 50 steps complete.
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21 of 50 steps complete.
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30 of 50 steps complete.
31 of 50 steps complete.
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47 of 50 steps complete.
48 of 50 steps complete.
49 of 50 steps complete.
50 of 50 steps complete.
Done - 0:00:02
Standard output

When you run a simulation on an HPC cluster, it will generate a standard output logging file (StdOut.txt) in the working directory that captures all standard output messages. When you run a simulation locally, the Command Prompt window will display the same information. If you want to save this information to a text file instead, you can append > stdout.txt to the command used to run the local EMOD simulation to redirect the console output to the file specified.

The file contains information about a particular simulation, such as the EMOD version used and the files in use, as well as other initialization information, including the default logging level and the logging levels set for particular modules. The file follows that information with log output using the following format: <timestep><HPC rank><log level><module><message>.

By default, the logging level is set to “INFO”. If you want to change the logging level, see Set log levels.

For example:

..\..\..\Eradication.exe --config config.json --input-path ..\..\..\Demographics_Files
Intellectual Ventures(R)/EMOD Disease Transmission Kernel 2.18.16.0
Built on Jul  5 2018 15:32:59 by SYSTEM from master (e98fbf4) checked in on 2018-06-12 11:32:40 -0700
Supports sim_types: GENERIC, VECTOR, MALARIA, AIRBORNE, TBHIV, STI, HIV, PY.
Using config file: config.json
Using input path: ..\..\..\Demographics_Files
Using output path: output
Using dll path:
Python not initialized because --python-script-path (-P) not set.
Initializing environment...
Log-levels:
        Default -> INFO
        Eradication -> INFO
00:00:00 [0] [I] [Eradication] Loaded Configuration...
00:00:00 [0] [I] [Eradication] 56 parameters found.
00:00:00 [0] [I] [Eradication] Initializing Controller...
00:00:00 [0] [I] [Controller] DefaultController::execute_internal()...
00:00:00 [0] [I] [Simulation] Using PSEUDO_DES random number generator.
00:00:00 [0] [I] [DllLoader] ReadEmodulesJson: no file, returning.
00:00:00 [0] [I] [DllLoader] dllPath not passed in, getting from EnvPtr
00:00:00 [0] [I] [DllLoader] Trying to copy from string to wstring.
00:00:00 [0] [I] [DllLoader] DLL ws root path:
00:00:00 [0] [W] [Simulation] 00:00:00 [0] [W] [Simulation] Failed to load reporter emodules for SimType: GENERIC_SIM from path: reporter_plugins
Failed to load reporter emodules for SimType: GENERIC_SIM from path: reporter_plugins
00:00:00 [0] [I] [Simulation] Found 0 Custom Report DLL's to consider loading, load_all_reports=1
00:00:00 [0] [I] [Controller] DefaultController::execute_internal() populate simulation...
00:00:00 [0] [I] [Simulation] Campaign file name identified as: campaign.json
00:00:00 [0] [I] [Climate] Initialize
00:00:00 [0] [I] [LoadBalanceScheme] Using Checkerboard Load Balance Scheme.
00:00:00 [0] [I] [Simulation] Looking for campaign file campaign.json
00:00:00 [0] [I] [Simulation] Found campaign file successfully.
00:00:00 [0] [I] [DllLoader] ReadEmodulesJson: no file, returning.
00:00:00 [0] [I] [DllLoader] dllPath not passed in, getting from EnvPtr
00:00:00 [0] [I] [DllLoader] Trying to copy from string to wstring.
00:00:00 [0] [I] [DllLoader] DLL ws root path:
00:00:00 [0] [W] [Simulation] 00:00:00 [0] [W] [Simulation] Failed to load intervention emodules for SimType: GENERIC_SIM from path: interventions
Failed to load intervention emodules for SimType: GENERIC_SIM from path: interventions
00:00:01 [0] [I] [JsonConfigurable] Using the default value ( "Number_Repetitions" : 1 ) for unspecified parameter.
00:00:01 [0] [I] [JsonConfigurable] Using the default value ( "Timesteps_Between_Repetitions" : -1 ) for unspecified parameter.
00:00:01 [0] [I] [JsonConfigurable] Using the default value ( "Incubation_Period_Override" : -1 ) for unspecified parameter.
00:00:01 [0] [I] [Simulation] populateFromDemographics() created 1 nodes
00:00:01 [0] [I] [Simulation] populateFromDemographics() generated 1 nodes.
00:00:01 [0] [I] [Simulation] Rank 0 contributes 1 nodes...
00:00:01 [0] [I] [Simulation] Merging node rank maps...
00:00:01 [0] [I] [Simulation] Merged rank 0 map now has 1 nodes.
00:00:01 [0] [I] [Simulation] Rank map contents not displayed until NodeRankMap::ToString() (re)implemented.
00:00:01 [0] [I] [Simulation] Initialized 'InsetChart.json' reporter
00:00:01 [0] [I] [Simulation] Initialized 'BinnedReport.json' reporter
00:00:01 [0] [I] [Simulation] Initialized 'DemographicsSummary.json' reporter
00:00:01 [0] [I] [Simulation] Update(): Time: 1.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:01 [0] [I] [SimulationEventContext] Time for campaign event. Calling Dispatch...
00:00:01 [0] [I] [SimulationEventContext] 1 node(s) visited.
00:00:01 [0] [I] [JsonConfigurable] Using the default value ( "Incubation_Period_Override" : -1 ) for unspecified parameter.
00:00:01 [0] [I] [StandardEventCoordinator] UpdateNodes() gave out 4 'OutbreakIndividual' interventions at node 1
00:00:01 [0] [I] [Simulation] Update(): Time: 2.0 Rank: 0 StatPop: 10000 Infected: 4
00:00:01 [0] [I] [Simulation] Update(): Time: 3.0 Rank: 0 StatPop: 10000 Infected: 13
00:00:01 [0] [I] [Simulation] Update(): Time: 4.0 Rank: 0 StatPop: 10000 Infected: 65
00:00:01 [0] [I] [Simulation] Update(): Time: 5.0 Rank: 0 StatPop: 10000 Infected: 283
00:00:01 [0] [I] [Simulation] Update(): Time: 6.0 Rank: 0 StatPop: 10000 Infected: 1149
00:00:01 [0] [I] [Simulation] Update(): Time: 7.0 Rank: 0 StatPop: 10000 Infected: 3777
00:00:01 [0] [I] [Simulation] Update(): Time: 8.0 Rank: 0 StatPop: 10000 Infected: 7268
00:00:01 [0] [I] [Simulation] Update(): Time: 9.0 Rank: 0 StatPop: 10000 Infected: 7065
00:00:01 [0] [I] [Simulation] Update(): Time: 10.0 Rank: 0 StatPop: 10000 Infected: 5578
00:00:01 [0] [I] [Simulation] Update(): Time: 11.0 Rank: 0 StatPop: 10000 Infected: 4377
00:00:01 [0] [I] [Simulation] Update(): Time: 12.0 Rank: 0 StatPop: 10000 Infected: 3392
00:00:01 [0] [I] [Simulation] Update(): Time: 13.0 Rank: 0 StatPop: 10000 Infected: 2640
00:00:01 [0] [I] [Simulation] Update(): Time: 14.0 Rank: 0 StatPop: 10000 Infected: 2054
00:00:01 [0] [I] [Simulation] Update(): Time: 15.0 Rank: 0 StatPop: 10000 Infected: 1624
00:00:01 [0] [I] [Simulation] Update(): Time: 16.0 Rank: 0 StatPop: 10000 Infected: 1247
00:00:01 [0] [I] [Simulation] Update(): Time: 17.0 Rank: 0 StatPop: 10000 Infected: 975
00:00:01 [0] [I] [Simulation] Update(): Time: 18.0 Rank: 0 StatPop: 10000 Infected: 742
00:00:01 [0] [I] [Simulation] Update(): Time: 19.0 Rank: 0 StatPop: 10000 Infected: 605
00:00:01 [0] [I] [Simulation] Update(): Time: 20.0 Rank: 0 StatPop: 10000 Infected: 469
00:00:01 [0] [I] [Simulation] Update(): Time: 21.0 Rank: 0 StatPop: 10000 Infected: 355
00:00:01 [0] [I] [Simulation] Update(): Time: 22.0 Rank: 0 StatPop: 10000 Infected: 267
00:00:01 [0] [I] [Simulation] Update(): Time: 23.0 Rank: 0 StatPop: 10000 Infected: 206
00:00:01 [0] [I] [Simulation] Update(): Time: 24.0 Rank: 0 StatPop: 10000 Infected: 164
00:00:01 [0] [I] [Simulation] Update(): Time: 25.0 Rank: 0 StatPop: 10000 Infected: 122
00:00:01 [0] [I] [Simulation] Update(): Time: 26.0 Rank: 0 StatPop: 10000 Infected: 89
00:00:01 [0] [I] [Simulation] Update(): Time: 27.0 Rank: 0 StatPop: 10000 Infected: 73
00:00:01 [0] [I] [Simulation] Update(): Time: 28.0 Rank: 0 StatPop: 10000 Infected: 57
00:00:01 [0] [I] [Simulation] Update(): Time: 29.0 Rank: 0 StatPop: 10000 Infected: 46
00:00:01 [0] [I] [Simulation] Update(): Time: 30.0 Rank: 0 StatPop: 10000 Infected: 32
00:00:01 [0] [I] [Simulation] Update(): Time: 31.0 Rank: 0 StatPop: 10000 Infected: 22
00:00:01 [0] [I] [Simulation] Update(): Time: 32.0 Rank: 0 StatPop: 10000 Infected: 17
00:00:01 [0] [I] [Simulation] Update(): Time: 33.0 Rank: 0 StatPop: 10000 Infected: 16
00:00:01 [0] [I] [Simulation] Update(): Time: 34.0 Rank: 0 StatPop: 10000 Infected: 15
00:00:01 [0] [I] [Simulation] Update(): Time: 35.0 Rank: 0 StatPop: 10000 Infected: 10
00:00:01 [0] [I] [Simulation] Update(): Time: 36.0 Rank: 0 StatPop: 10000 Infected: 6
00:00:01 [0] [I] [Simulation] Update(): Time: 37.0 Rank: 0 StatPop: 10000 Infected: 4
00:00:01 [0] [I] [Simulation] Update(): Time: 38.0 Rank: 0 StatPop: 10000 Infected: 3
00:00:01 [0] [I] [Simulation] Update(): Time: 39.0 Rank: 0 StatPop: 10000 Infected: 3
00:00:01 [0] [I] [Simulation] Update(): Time: 40.0 Rank: 0 StatPop: 10000 Infected: 2
00:00:01 [0] [I] [Simulation] Update(): Time: 41.0 Rank: 0 StatPop: 10000 Infected: 1
00:00:01 [0] [I] [Simulation] Update(): Time: 42.0 Rank: 0 StatPop: 10000 Infected: 1
00:00:01 [0] [I] [Simulation] Update(): Time: 43.0 Rank: 0 StatPop: 10000 Infected: 1
00:00:01 [0] [I] [Simulation] Update(): Time: 44.0 Rank: 0 StatPop: 10000 Infected: 1
00:00:01 [0] [I] [Simulation] Update(): Time: 45.0 Rank: 0 StatPop: 10000 Infected: 1
00:00:01 [0] [I] [Simulation] Update(): Time: 46.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:01 [0] [I] [Simulation] Update(): Time: 47.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:01 [0] [I] [Simulation] Update(): Time: 48.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:01 [0] [I] [Simulation] Update(): Time: 49.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:01 [0] [I] [Simulation] Update(): Time: 50.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:01 [0] [I] [Simulation] Update(): Time: 51.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:01 [0] [I] [Simulation] Update(): Time: 52.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:01 [0] [I] [Simulation] Update(): Time: 53.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 54.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 55.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 56.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 57.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 58.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 59.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 60.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 61.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 62.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 63.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 64.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 65.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 66.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 67.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 68.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 69.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 70.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 71.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 72.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 73.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 74.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 75.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 76.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 77.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 78.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 79.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 80.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 81.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 82.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 83.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 84.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 85.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 86.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 87.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 88.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 89.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 90.0 Rank: 0 StatPop: 10000 Infected: 0
00:00:02 [0] [I] [Simulation] Finalizing 'InsetChart.json' reporter.
00:00:02 [0] [I] [Simulation] Finalized  'InsetChart.json' reporter.
00:00:02 [0] [I] [Simulation] Finalizing 'BinnedReport.json' reporter.
00:00:02 [0] [I] [Simulation] Finalized  'BinnedReport.json' reporter.
00:00:02 [0] [I] [Simulation] Finalizing 'DemographicsSummary.json' reporter.
00:00:02 [0] [I] [Simulation] Finalized  'DemographicsSummary.json' reporter.
00:00:02 [0] [I] [Controller] Exiting DefaultController::execute_internal
00:00:02 [0] [I] [Eradication] Controller executed successfully.
Standard error

When you run a simulation on an HPC cluster, it will also generate a standard error logging file (StdErr.txt) in the working directory that captures all standard error messages.

BinnedReport

The binned output report (BinnedReport.json) is a JSON-formatted file where the channel data has been sorted into age bins. It is very similar to an inset chart, however, with the binned report all channels are broken down into sub-channels (bins) based on age. For example, instead of having a single prevalence channel, you might have prevalence in the “0-3 years old bin” and the “4-6 years old bin, and so forth.

To generate the binned report, set the Enable_Demographics_Reporting configuration parameter to 1. The demographics summary output report will also be generated.

The file contains a header and a channels section.

Channels

The channels section contains the following parameters.

Parameter

Data type

Description

<Channel_Title>

string

The title of the particular channel.

Units

string

The units used for this channel.

Data

array

A list of the channel data at each time step.

Malaria binned reports have the following channel data:

Parameter

Description

Blood Smear Gametocyte Positive

The number of individuals who are detectable with the BLOOD_SMEAR_GAMETOCYTES version of MalariaDiagnostic intervention. The detectability of the diagnostic is controlled by parameters Report_Gametocyte_Smear_Sensitivity and Report_Detection_Threshold_Blood_Smear_Gametocytes.

Blood Smear Parasite Positive

The number of individuals who are detectable with the BLOOD_SMEAR_PARASITES version of MalariaDiagnostic intervention. The detectability of the diagnostic is controlled by parameters Report_Parasites_Smear_Sensitivity and Report_Detection_Threshold_Blood_Smear_Parasites.

Fever Positive

The number of individuals currently with a fever. Detectable fever is determined by the parameter Report_Detection_Threshold_Fever. which is the level of temperature above normal (Celsius) for an individual to be considered to have a fever.

Infected

The number of individuals who are infected on that day in that age bin.

Mean Parasitemia

The geometric mean number of parasites per microliter of blood. [Exponential of (Sum of log(BLOOD_SMEAR_PARASITES diagnostic ) for all individuals divided by the number of individuals detected using BLOOD_SMEAR_PARASITES) diagnostic - geometric mean].

New Clinical Cases

The number of new clinical cases on that day. This channel is controlled by the Clinical_Fever_Threshold_Low and Clinical_Fever_Threshold_High  parameters. The amount that an individual’s fever is above normal must be greater than both of these values to be considered clinical.

New Infections

The number of individuals who got infected on that day. This is not the number of new infections on that day. An individual could receive multiple infections in a single day depending on the value of the Malaria_Model parameter in your configuration setup.

New Severe Cases

The number of new severe cases of malaria on that day. An individual is considered to be a severe case if the combined probability of anemia, parasite density, and fever is greater than a uniform random number. This combined probability is the combination of sigmoid using the following parameters: Anemia_Severe_Threshold and Anemia_Severe_Inverse_Width, Parasite_Severe_Threshold and Parasite_Severe_Inverse_Width , and Fever_Severe_Threshold and Fever_Severe_Inverse_Width.

PCR Gametocytes Positive

The number of individuals who are detectable with the PCR_GAMETOCYTES version of MalariaDiagnostic. The detectability of the diagnostic is controlled by parameter Report_Detection_Threshold_PCR_Gametocytes.

PCR Parasites Positive

The number of individuals who are detectable with the PCR_PARASITES version of MalariaDiagnostic. The detectability of the diagnostic is controlled by parameter Report_Detection_Threshold_PCR_Parasites.

PfHRP2 Positive

The number of individuals who are detectable with the PF_HRP2 version of MalariaDiagnostic. The detectability of the diagnostic is controlled by parameter Report_Detection_Threshold_PCR_PfHRP2.

Population

The total number of individuals in this age range on this day.

Sum MSP Variant Fractions

This the sum of each individual’s fraction of MSP variants that an individual has had. The parameter Falciparum_MSP_Variants defines the total number of possible variants. This channel indicates the average fraction that an individual has seen of this total number. The greater the fraction the more that the population has developed antibodies to the parasite.

Sum Non-Specific Variant Fractions

This the sum of each individual’s fraction of Non-Specific variants that an individual has had. The parameter Falciparum_Nonspecific_Types defines the total number of possible variants. This channel indicates the average fraction that an individual has seen of this total number. The greater the fraction the more that the population has developed antibodies to the parasite.

Sum of Squared MSP Variant Fractions

This is the sum of the squares of the values used in calculating Sum MSP Variant Fractions.

Sum of Squared Non-Specific Variant Fractions

This is the sum of the squares of the values used in calculating Sum Non-Specific Variant Fractions.

Sum of Squared PfEMP1 Variant Fractions

This is the sum of the squares of the values used in calculating Sum PfEMP1 Variant Fractions.

Sum PfEMP1 Variant Fractions

This the sum of each individual’s fraction of variants of the PfEMP1 var genes that an individual has had. The parameter Falciparum_PfEMP1_Variants defines the total number of possible variants. This channel indicates the average fraction that an individual has seen of this total number. The greater the fraction the more that the population has developed antibodies to the parasite.

True Positive

The number of individuals who are detectable with the TRUE_PARASITE_DENSITY version of MalariaDiagnostic. The detectability of the diagnostic is controlled by parameter Report_Detection_Threshold_True_Parasite_Density.
Example

The following is a sample of a BinnedReport.json file.

{
    "Header": {
        "DateTime": "Wed Oct 01 14:49:22 2014",
        "DTK_Version": "4284 trunk 2014/09/23 11:50:52",
        "Report_Version": "2.1",
        "Timesteps": 150,
        "Subchannel_Metadata": {
            "AxisLabels": [
                ["Age"]
            ],
            "NumBinsPerAxis": [
                [21]
            ],
            "ValuesPerAxis": [
                [
                    [1825, 3650, 5475, 7300, 9125, 10950, 12775, 14600, 16425, 18250, 20075, 21900, 23725, 25550, 27375, 29200, 31025, 32850, 34675, 36500, 999999]
                ]
            ],
            "MeaningPerAxis": [
                [
                    ["<5", "5-9", "10-14", "15-19", "20-24", "25-29", "30-34", "35-39", "40-44", "45-49", "50-54", "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90-94", "95-99", ">100"]
                ]
            ]
        },
        "Channels": 4
    },
    "Channels": {
        "Disease Deaths": {
            "Units": "",
            "Data": [
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            ]
        },
        "New Infections": {
            "Units": "",
            "Data": [
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                [398, 398, 400, 399, 399, 399, 399, 399, 399, 400, 401, 402, 402, 402, 403, 402, 402, 403, 403, 403, 403, 403, 403, 404, 404, 406, 407, 407, 407, 407, 407, 407, 407, 407, 407, 406, 406, 406, 406, 406, 406, 405, 403, 403, 403, 405, 404, 404, 403, 403, 403, 404, 403, 404, 404, 404, 403, 403, 404, 404, 404, 404, 405, 405, 405, 405, 405, 406, 407, 406, 405, 405, 405, 405, 405, 404, 405, 406, 406, 406, 407, 407, 407, 408, 408, 409, 409, 411, 411, 411, 411, 411, 411, 410, 410, 410, 409, 409, 409, 408, 409, 409, 410, 410, 411, 411, 412, 412, 413, 413, 414, 413, 413, 413, 413, 413, 413, 412, 412, 412, 411, 410, 410, 410, 410, 408, 409, 409, 411, 411, 410, 410, 410, 410, 412, 412, 411, 411, 411, 411, 411, 411, 411, 411, 411, 411, 412, 412, 412, 412],
                [349, 349, 349, 350, 350, 351, 351, 351, 350, 350, 350, 351, 351, 350, 350, 351, 351, 351, 351, 351, 350, 350, 350, 350, 350, 349, 349, 349, 349, 349, 349, 349, 349, 349, 349, 350, 350, 350, 349, 349, 349, 350, 351, 351, 351, 350, 351, 351, 352, 352, 352, 351, 352, 352, 352, 351, 352, 353, 352, 352, 352, 352, 352, 352, 352, 352, 353, 353, 353, 354, 355, 355, 354, 354, 354, 355, 355, 355, 355, 355, 355, 355, 355, 355, 354, 354, 353, 353, 353, 353, 353, 353, 353, 354, 354, 354, 355, 355, 355, 356, 356, 356, 356, 356, 356, 356, 355, 355, 354, 354, 354, 355, 355, 355, 355, 354, 354, 355, 355, 355, 356, 357, 357, 357, 357, 359, 359, 359, 359, 358, 359, 359, 359, 360, 360, 361, 362, 362, 363, 363, 363, 361, 361, 360, 359, 359, 359, 359, 359, 359],
                [281, 281, 281, 281, 281, 281, 280, 280, 281, 281, 281, 281, 281, 282, 282, 282, 282, 282, 282, 282, 283, 283, 282, 282, 282, 283, 283, 283, 283, 283, 283, 283, 283, 283, 283, 282, 281, 281, 282, 282, 281, 281, 282, 282, 282, 283, 283, 283, 283, 283, 283, 284, 284, 284, 284, 285, 285, 285, 286, 286, 286, 286, 286, 286, 286, 286, 286, 286, 286, 286, 286, 286, 287, 287, 287, 287, 286, 286, 286, 286, 285, 285, 285, 285, 285, 285, 286, 286, 286, 286, 286, 287, 287, 287, 287, 287, 287, 287, 287, 287, 286, 286, 286, 285, 285, 285, 285, 285, 286, 284, 284, 284, 284, 283, 282, 283, 283, 283, 283, 283, 283, 282, 282, 282, 282, 282, 282, 282, 281, 282, 282, 282, 282, 282, 282, 281, 281, 281, 281, 281, 281, 283, 283, 283, 284, 283, 283, 282, 282, 282],
                [218, 218, 218, 218, 218, 218, 219, 219, 218, 218, 218, 218, 218, 218, 218, 218, 218, 218, 218, 218, 218, 218, 219, 219, 219, 219, 219, 219, 219, 219, 218, 216, 216, 215, 215, 215, 215, 214, 214, 214, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 214, 214, 214, 214, 214, 214, 214, 214, 213, 213, 213, 213, 213, 213, 213, 213, 213, 213, 213, 213, 213, 213, 213, 213, 214, 214, 214, 214, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 215, 216, 216, 216, 217, 217, 216, 218, 218, 218, 218, 219, 220, 220, 220, 220, 220, 220, 220, 221, 221, 221, 221, 221, 220, 220, 221, 221, 220, 220, 220, 220, 220, 221, 221, 221, 221, 221, 221, 221, 221, 222, 222, 223, 223, 224, 224, 224],
                [188, 188, 188, 188, 188, 188, 188, 188, 189, 189, 188, 188, 188, 188, 188, 187, 187, 187, 187, 186, 186, 184, 182, 182, 182, 182, 182, 182, 181, 181, 182, 184, 184, 185, 185, 186, 187, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 189, 189, 189, 188, 188, 188, 188, 187, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 188, 187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 186, 186, 186, 187, 186, 186, 186, 186, 186, 185, 184, 184, 184, 184, 184, 184, 184, 183, 183, 184, 184, 184, 184, 184, 184, 184, 184, 185, 185, 185, 185, 185, 185, 185, 185, 184, 184, 184, 184, 184, 184, 184, 184, 184, 184, 185, 185, 185, 185, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186],
                [158, 158, 158, 158, 158, 158, 158, 158, 158, 158, 159, 159, 159, 159, 159, 160, 160, 160, 160, 161, 161, 163, 165, 165, 165, 165, 165, 165, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 165, 164, 164, 165, 165, 165, 165, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 167, 167, 167, 167, 167, 167, 167, 167, 167, 167, 168, 168, 168, 168, 169, 169, 169, 169, 169, 170, 171, 171, 171, 171, 171, 171, 171, 170, 170, 170, 170, 168, 168, 168, 168, 168, 168, 168, 167, 166, 166, 165, 165, 165, 165, 166, 166, 166, 166, 166, 166, 166, 166, 166, 166, 165, 165, 165, 165, 165, 164, 164, 164, 164, 164, 164, 164, 164, 164, 164, 164, 164, 163, 163, 163, 163, 163, 163, 163],
                [115, 115, 115, 114, 114, 114, 114, 114, 114, 114, 114, 114, 113, 113, 113, 113, 112, 112, 112, 111, 111, 111, 111, 111, 111, 111, 110, 110, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 110, 110, 110, 110, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 109, 110, 110, 110, 110, 110, 110, 110, 110, 110, 110, 110, 110, 109, 109, 109, 109, 109, 109, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 108, 107, 107, 107, 107, 107, 107, 107, 107, 107, 107, 106, 106, 106, 106, 108, 108, 108, 108, 110, 109, 109, 109, 109, 109, 109, 110, 111, 111, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 112, 113, 113, 113, 113, 113, 114, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 113, 114, 114, 114, 114, 114, 114, 114],
                [95, 95, 95, 96, 96, 96, 96, 96, 96, 96, 96, 96, 97, 97, 97, 97, 98, 98, 98, 99, 99, 99, 99, 99, 99, 99, 100, 100, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 103, 103, 103, 103, 103, 103, 104, 104, 104, 104, 104, 104, 103, 103, 103, 103, 103, 103, 103, 104, 104, 104, 103, 103, 103, 103, 103, 103, 103, 104, 104, 104, 104, 104, 104, 104, 104, 104, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105, 105],
                [78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 77, 77, 77, 77, 77, 77, 77, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 75, 75, 75, 75, 75, 75, 75, 75, 75, 76, 76, 75, 75, 75, 75, 75, 75, 75, 75, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 76, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 74, 75, 75, 75, 75, 75, 75, 75, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74],
                [72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 71, 71, 71, 71, 71, 71, 71, 72, 72, 72, 72, 72, 72, 72, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 72, 72, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 74, 74, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74],
                [49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 50, 50, 51, 51, 51, 52, 52, 52, 52, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50],
                [39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 43, 43, 43, 43, 43, 43, 43, 43, 43, 42, 42, 42, 42, 42],
                [30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 28, 29, 29, 29, 29, 29],
                [119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 119, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121, 121]
            ]
        }
    }
}

The following is a sample of a BinnedReport.json file for malaria.

{
    "Header": {
        "DateTime": "Mon Dec 21 11:33:27 2020",
        "DTK_Version": " Malaria-Ongoing (bb265ad) Dec 21 2020 11:15:31",
        "Report_Version": "2.1",
        "Timesteps": 730,
        "Subchannel_Metadata": {
            "AxisLabels": [
                [
                    "Age"
                ]
            ],
            "NumBinsPerAxis": [
                [
                    21
                ]
            ],
            "ValuesPerAxis": [
                [
                    [
                        1825,
                        3650,
                        5475
                    ]
                ]
            ],
            "MeaningPerAxis": [
                [
                    [
                        "<5",
                        "55-59",
                        "95-99"
                    ]
                ]
            ]
        },
        "Channels": 19
    },
    "Channels": {
        "Blood Smear Gametocyte Positive": {
            "Units": "",
            "Data": [
                [
                    380,
                    300,
                    1670
                ]
            ]
        },
        "Blood Smear Parasite Positive": {
            "Units": "",
            "Data": [
                [
                    90,
                    80,
                    90
                ]
            ]
        },
        "Fever Positive": {
            "Units": "",
            "Data": [
                [
                    1700,
                    1760,
                    1660
                ]
            ]
        },
        "Infected": {
            "Units": "",
            "Data": [
                [
                    1790,
                    1090,
                    1723
                ]
            ]
        }
    }
}
DemographicsSummary

The demographic summary output report (DemographicsSummary.json) is a JSON-formatted file with the demographic channel output results of the simulation, consisting of simulation-wide averages by time step. The format is identical to the inset chart output report, except the channels reflect demographic categories, such as gender ratio.

To generate the demographics summary report, set the Enable_Demographics_Reporting configuration parameter to 1. The BinnedReport will also be generated.

The file contains a header and a channels section.

Channels

The channels section contains the following parameters.

Parameter

Data type

Description

Average Age

integer

The average age of the Statistical Population at each time step. This takes the age of each agent and multiplies it by the agent’s Monte Carlo Weight, adds them together, then divides that sum by the Statistical Population.

Gender Ratio (fraction male)

integer

The fraction of the statistical population that is male at each time step. This takes the Monte Carlo weight of each male, adds them together, then divides by the statistical population.

New Births

integer

The statistical number of children born during each time step. This is the sum of the Monte Carlo weight of each newborn.

New Natural Deaths

integer

The statistical number of people that died from natural causes (i.e. not disease) during each time step. This is the sum of the Monte Carlo weights of each person that died.

Population Age X-Y

integer

The statistical population of the people whose age is greater than or equal to X and strictly less than Y+1. For example, if X=10 and Y=14, then if 10 <= age < 15, the person will be counted in that channel. This channel is the sum of the Monte Carlo weight of each person that qualifies for that channel. The set of channels is starts at 0-5 (i.e.<5) and increases every 5 years until the last bin is those people over 100.

Possible Mothers

integer

The total number of females in the population whose age is greater than 14 years and less than 45 years.

Pseudo-Population

integer

The number of actual human agents in the simulation on that day. This number times the Monte Carlo Weight (which is controlled by the configuration parameter Individual_Sampling_Type) should be the same value as in the Statistical Population channel.

Statistical Population

integer

The total number of individuals in the simulation on that day. The sum of the Population Age X-Y channels at each time step should sum to this channel at the corresponding time step.
Example

The following is a sample of a DemographicsSummary.json file.

{
    "Header": {
        "DateTime": "Mon Mar 16 07:45:10 2015",
        "DTK_Version": "4777 v2.0-HIV 2015/02/26 10:51:25",
        "Report_Type": "InsetChart",
        "Report_Version": "3.2",
        "Start_Time": 0,
        "Simulation_Timestep": 1,
        "Timesteps": 5,
        "Channels": 28
    },
    "Channels": {
        "Average Age": {
            "Units": "",
            "Data": [
                8592.415039063,
                8593.427734375,
                8594.439453125,
                8595.41796875,
                8596.412109375
            ]
        },
        "Gender Ratio (fraction male)": {
            "Units": "",
            "Data": [
                0.5350999832153,
                0.5350999832153,
                0.5350999832153,
                0.5350999832153,
                0.5350999832153
            ]
        },
        "New Births": {
            "Units": "",
            "Data": [
                0,
                0,
                0,
                0,
                0
            ]
        },
        "New Natural Deaths": {
            "Units": "",
            "Data": [
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Population Age 10-14": {
            "Units": "",
            "Data": [
                1203,
                1204,
                1202,
                1203,
                1203
            ]
        },
        "Population Age 15-19": {
            "Units": "",
            "Data": [
                1056,
                1057,
                1059,
                1059,
                1059
            ]
        },
        "Population Age 20-24": {
            "Units": "",
            "Data": [
                810,
                810,
                810,
                809,
                809
            ]
        },
        "Population Age 25-29": {
            "Units": "",
            "Data": [
                732,
                732,
                732,
                732,
                732
            ]
        },
        "Population Age 30-34": {
            "Units": "",
            "Data": [
                540,
                539,
                539,
                539,
                539
            ]
        },
        "Population Age 35-39": {
            "Units": "",
            "Data": [
                410,
                411,
                411,
                411,
                411
            ]
        },
        "Population Age 40-44": {
            "Units": "",
            "Data": [
                351,
                351,
                351,
                352,
                352
            ]
        },
        "Population Age 45-49": {
            "Units": "",
            "Data": [
                294,
                294,
                294,
                294,
                294
            ]
        },
        "Population Age 5-9": {
            "Units": "",
            "Data": [
                1599,
                1597,
                1598,
                1597,
                1599
            ]
        },
        "Population Age 50-54": {
            "Units": "",
            "Data": [
                201,
                201,
                201,
                201,
                201
            ]
        },
        "Population Age 55-59": {
            "Units": "",
            "Data": [
                194,
                194,
                194,
                193,
                193
            ]
        },
        "Population Age 60-64": {
            "Units": "",
            "Data": [
                163,
                163,
                163,
                164,
                164
            ]
        },
        "Population Age 65-69": {
            "Units": "",
            "Data": [
                111,
                111,
                111,
                111,
                111
            ]
        },
        "Population Age 70-74": {
            "Units": "",
            "Data": [
                104,
                104,
                104,
                103,
                103
            ]
        },
        "Population Age 75-79": {
            "Units": "",
            "Data": [
                86,
                86,
                86,
                87,
                87
            ]
        },
        "Population Age 80-84": {
            "Units": "",
            "Data": [
                55,
                55,
                55,
                55,
                55
            ]
        },
        "Population Age 85-89": {
            "Units": "",
            "Data": [
                61,
                61,
                61,
                61,
                61
            ]
        },
        "Population Age 90-94": {
            "Units": "",
            "Data": [
                49,
                49,
                49,
                49,
                49
            ]
        },
        "Population Age 95-99": {
            "Units": "",
            "Data": [
                30,
                30,
                30,
                30,
                30
            ]
        },
        "Population Age <5": {
            "Units": "",
            "Data": [
                1829,
                1829,
                1828,
                1828,
                1826
            ]
        },
        "Population Age >100": {
            "Units": "",
            "Data": [
                122,
                122,
                122,
                122,
                122
            ]
        },
        "Possible Mothers": {
            "Units": "",
            "Data": [
                1912,
                1912,
                1912,
                1913,
                1913
            ]
        },
        "Pseudo-Population": {
            "Units": "",
            "Data": [
                10000,
                10000,
                10000,
                10000,
                10000
            ]
        },
        "Statistical Population": {
            "Units": "",
            "Data": [
                10000,
                10000,
                10000,
                10000,
                10000
            ]
        }
    }
}
InsetChart

The inset chart (InsetChart.json) is an output report that is automatically generated with every simulation. It contains simulation-wide averages, one per time step, for a wide number of data channels. The channels are fully specified by the simulation type and cannot be altered without making changes to the EMOD source code. Python or other tools can be used to create charts out of the information contained in the file (see the example charts provided at the end of this page.)

Configuration

To generate this report, make sure the Enable_Default_Reporting parameter is set to 1 in the config.json file.

Output file data
Headers

When running malaria simulations, the header section will contain the following parameters.

Parameter

Data type

Description

DateTime

string

The time stamp indicating when the report was generated.

DTK_Version

string

The version of EMOD used.

Report_Type

string

The type of output report.

Report_Version

string

The format version of the report.

Start_Time

integer

The time in days when the simulation begins.

Simulation_Timestep

integer

The number of days in each time step.

Timesteps

integer

The number of time steps in the simulation.

Channels

integer

The number of channels in the simulation.
Channels

When running malaria simulations, the following channels are included in the InsetChart.json file. Note that the diagnostic channels are calculated once each time step and the same results of the diagnostic are used in all reports.

Note that you cannot generate a separate InsetChart by node; to do so, use ReportMalariaFiltered.

Channel

Description

30-day Av Infection Duration

A running average of the duration of each infection that cleared in the last 30 days (both naturally and due to drugs). Use Inset_Chart_Reporting_Include_30Day_Avg_Infection_Duration to control the presence of this channel. The default is to include the channel.

Adult Vectors

The average number of adult vectors per node.

Air Temperature

The average air temperature, in Celsius, on that day per node.

Avg Num Infections

The average number of infections, per person (infected people only) on that day. These are all the infections the individual has and may not be detectable by diagnostics. Note that this may not equal the number of infected people as people may have multiple infections.

Avg Num Vector Infs

The average number of infections per infected/infectious vector. This number implies that the infections are different in either time or genetics of the parasite. This channel is included when Malaria_Model is set to MALARIA_MECHANISTIC_MODEL_WITH_PARASITE_GENETICS.

Births

The live births cumulative up to that day.

Blood Smear Gametocyte Prevalence

The fraction of the population that is detectable with the BLOOD_SMEAR_GAMETOCYTES version of MalariaDiagnostic. The detectability of the diagnostic is controlled by the parameters Report_Gametocyte_Smear_Sensitivity and Report_Detection_Threshold_Blood_Smear_Gametocytes.

Blood Smear Parasite Prevalence

The fraction of the population that is detectable with the BLOOD_SMEAR_PARASITES version of MalariaDiagnostic. The detectability of the diagnostic is controlled by parameters Report_Parasite_Smear_Sensitivity and Report_Detection_Threshold_Blood_Smear_Parasites.

Campaign Cost

The cost of campaigns cumulative up to that day (set by the Cost_To_Consumer parameter in each intervention).

Daily Bites per Human

The average number of mosquito bites received per individual on that day.

Daily EIR

The entomological inoculation rate (EIR), or number of infected mosquito bites each individual receives each day, on average.

Disease Deaths

The number of individuals whose death is attributed to malaria, cumulative up to that day.

Fever Prevalence

The fraction of the population currently with a fever. Detectable fever is determined by the parameter Report_Detection_Threshold_Fever, the level of temperature above normal (Celsius) for an individual to be considered to have a fever.

Has_<Intervention>

The fraction of the population that has the given intervention. There is one channel for each entry in the parameter Inset_Chart_Has_Interventions. The parameter contains a list of intervention names that you can define in the campaign for the intervention using the parameter Intervention_Name. This only pertains to those interventions that persist in the individual from one time step to the next. You can use this channel as a simple debugging tool to see if and when interventions are distributed. If Inset_Chart_Has_Interventions is an empty list, there are no channels. The parameter does not validate the entries.

HasIP_<Key>:<Value>

The fraction of the population that has this Value for the individual property Key. There will be one channel for each value of each individual property key listed in the configuration parameter Inset_Chart_Has_IP. If Inset_Chart_Has_IP is an empty list, there will be no channels added to the report. They keys provided in the parameter must be defined in the demographics.

Human Infectious Reservoir

The expected number of infections from human to vector, including human immunity and within-host interventions but not vector control interventions, if each human were bitten by one vector that day.  The average infectiousness per individual in the simulation.

Infected

The fraction of the population currently infected.

Infected and Infectious Vectors

The fraction of adult female vectors that are either infected or infectious. These vectors could have either oocyts, sporozoites or both. This channel is included when Malaria_Model is set to MALARIA_MECHANISTIC_MODEL_WITH_PARASITE_GENETICS.

Infectious Vectors

The fraction of vectors in the simulation that are currently infected and infectious.

Land Temperature

The average land temperature per node, in Celsius, on that day.

Log Prevalence

The logarithm of the population prevalence. Log10 of the infected channel.

Mean Parasitemia

The geometric mean number of parasites per microliter of blood. [Exponential of (Sum of log(BLOOD_SMEAR_PARASITES diagnostic) for all individuals divided by the number of individuals detected using (BLOOD_SMEAR_PARASITES) diagnostic – geometric mean].

New Clinical Cases

The number of new clinical cases on that day. This channel is controlled by the Clinical_Fever_Threshold_Low and Clinical_Fever_Threshold_High parameters. The amount that an individual’s fever is above normal must be greater than both of these values to be considered clinical.

New Infections

The number of individuals who got infected on that day. Because an individual could receive multiple infections in a single day, this is not the number of total new infections on that day. The Malaria_Model parameter controls the number of new infections possible per person per day.

New Vector Infections

The number of new infections occurring in vectors on that day. Since vectors only feed once per day, it is also the number of vectors that received an infection that day. This could be an infected or infectious vector taking on more gametocytes. This channel is included when Malaria_Model is set to MALARIA_MECHANISTIC_MODEL_WITH_PARASITE_GENETICS.

New Severe Cases

The number of new severe cases of malaria on that day. An individual is considered to be a severe case if the combined probability of anemia, parasite density, and fever is greater than a uniform random number. This combined probability is the combination of sigmoid using the following parameters: Anemia_Severe_Threshold and Anemia_Severe_Inverse_Width, Parasite_Severe_Threshold and Parasite_Severe_Inverse_Width, and Fever_Severe_Threshold and Fever_Severe_Inverse_Width. For more information see Malaria symptoms and diagnostics.

Newly Symptomatic

50% of new infections are randomly selected to be ‘symptomatic’.

PCR Gametocyte Prevalence

The fraction of the population that is detectable with the PCR_GAMETOCYTES version of MalariaDiagnostic. The detectability of the diagnostic is controlled by parameter Report_Detection_Threshold_PCR_Gametocytes.

PCR Parasite Prevalence

The fraction of the population that is detectable with the PCR_PARASITES version of MalariaDiagnostic. The detectability of the diagnostic is controlled by parameter Report_Detection_Threshold_PCR_Parasites.

PfHRP2 Prevalence

The fraction of the population that is detectable with the PF_HRP2 version of MalariaDiagnostic. The detectability of the diagnostic is controlled by parameter Report_Detection_Threshold_PCR_PfHRP2.

Rainfall

The average number in millimeters of rainfall per node on that day.

Relative Humidity

The average relative humidity per node on that day.

Statistical Population

The total number of individuals in the simulation on that day.

Symptomatic Population

Not connected in Malaria simulations.

True_Prevalence

The fraction of the population that is detectable with the TRUE_PARASITE_DENSITY version of MalariaDiagnostic. The detectability of the diagnostic is controlled by the parameter Report_Detection_Threshold_True_Parasite_Density.

Variant Fraction-PfEMP1 Major

The average of the fraction of variants of the PfEMP1 var genes that an individual has had. The parameter Falciparum_PfEMP1_Variants defines the total number of possible variants. This channel indicates the average fraction that an individual has seen of this total number. The greater the fraction the more that the population has developed antibodies to the parasite.
Example

The following is an example of a malaria-specific InsetChart.json:

{
    "Header": {
        "DateTime": "Thu Jun 10 07:01:07 2021",
        "DTK_Version": "0 unknown-branch (unknown) Jun  9 2021 11:19:20",
        "Report_Type": "InsetChart",
        "Report_Version": "3.2",
        "Start_Time": 0,
        "Simulation_Timestep": 1,
        "Timesteps": 2850,
        "Channels": 31
    },
    "Channels": {
        "30-day Avg Infection Duration": {
            "Units": "Days",
            "Data": [3729.955566406, 3697.680664063, 3650.832519531, 3629.385742188, 3646.3359375]
        },
        "Adult Vectors": {
            "Units": "Average Count/Node",
            "Data": [1027.041625977, 1027.176391602, 1027.213378906, 1026.593139648, 1026.565429688]
        },
        "Air Temperature": {
            "Units": "degrees Celsius",
            "Data": [22, 22, 22, 22, 22]
        },
        "Avg Num Infections": {
            "Units": "Count",
            "Data": [2.812913894653, 2.819536447525, 2.817204236984, 2.809917449951, 2.80363035202]
        },
        "Births": {
            "Units": "Count",
            "Data": [2515, 2515, 2517, 2520, 2521]
        },
        "Blood Smear Gametocyte Prevalence": {
            "Units": "Fraction Detected Infected",
            "Data": [0.1729622334242, 0.1707532554865, 0.1703867465258, 0.1688397824764, 0.1727031767368]
        },
        "Blood Smear Parasite Prevalence": {
            "Units": "Fraction Detected Infected",
            "Data": [0.2233267128468, 0.2217804342508, 0.223646402359, 0.2238674014807, 0.2206272035837]
        },
        "Campaign Cost": {
            "Units": "USD",
            "Data": [5486.75, 5488.75, 5490.75, 5494.75, 5498.75]
        },
        "Daily Bites per Human": {
            "Units": "Bites/Day/Human",
            "Data": [113.6352996826, 113.5904541016, 113.3630905151, 113.5420913696, 113.4094543457]
        },
        "Daily EIR": {
            "Units": "Infectious Bites/Day",
            "Data": [0.3297989964485, 0.3540976345539, 0.3334806859493, 0.3672928214073, 0.3440812528133]
        },
        "Disease Deaths": {
            "Units": "Count",
            "Data": [0, 0, 0, 0, 0]
        },
        "Fever Prevalence": {
            "Units": "Fraction Detected Infected",
            "Data": [0.008614976890385, 0.008614976890385, 0.008839778602123, 0.005966851022094, 0.005962897557765]
        },
        "Human Infectious Reservoir": {
            "Units": "Infectivity/Person",
            "Data": [0.005706539843231, 0.005738727282733, 0.005730483215302, 0.005793820600957, 0.005701572168618]
        },
        "Infected": {
            "Units": "Infected Fraction",
            "Data": [0.2666224837303, 0.2668433785439, 0.2669613361359, 0.2671823203564, 0.2672261595726]
        },
        "Infectious Vectors": {
            "Units": "Infectious Fraction",
            "Data": [0.00372875877656, 0.003818936413154, 0.003874097252265, 0.003857779549435, 0.003872038796544]
        },
        "Land Temperature": {
            "Units": "degrees Celsius",
            "Data": [26, 26, 26, 26, 26]
        },
        "Log Prevalence": {
            "Units": "Log Prevalence",
            "Data": [-0.5741032361984, -0.5737435817719, -0.5735516548157, -0.5731922984123, -0.5731210112572]
        },
        "Mean Parasitemia": {
            "Units": "Geo. mean parasites/microliter",
            "Data": [409.4178161621, 426.9133300781, 387.6334228516, 402.7696838379, 379.7192382813]
        },
        "New Clinical Cases": {
            "Units": "Count",
            "Data": [9, 13, 9, 7, 5]
        },
        "New Infections": {
            "Units": "Count",
            "Data": [32, 31, 25, 17, 21]
        },
        "New Severe Cases": {
            "Units": "Count",
            "Data": [ 0, 0, 0, 3, 1]
        },
        "Newly Symptomatic": {
            "Units": "Count",
            "Data": [19, 15, 16, 5, 11]
        },
        "PCR Gametocyte Prevalence": {
            "Units": "% Detected Infected",
            "Data": [0.2491716295481, 0.2476253658533, 0.2495027631521, 0.2466298341751, 0.2495582997799]
        },
        "PCR Parasite Prevalence": {
            "Units": "% Detected Infected",
            "Data": [0.2540313601494, 0.2529268860817, 0.2519336938858, 0.2514917254448, 0.2526501715183]
        },
        "PfHRP2 Prevalence": {
            "Units": "% Detected Infected",
            "Data": [0.248729839921, 0.2491716295481, 0.2481767982244, 0.2497237622738, 0.25]
        },
        "Rainfall": {
            "Units": "mm/day",
            "Data": [9.999994277954, 9.999994277954, 9.999994277954, 9.999994277954, 9.999994277954]
        },
        "Relative Humidity": {
            "Units": "",
            "Data": [0.75, 0.75, 0.75, 0.75, 0.75]
        },
        "Statistical Population": {
            "Units": "Population",
            "Data": [4527, 4527, 4525, 4525, 4528]
        },
        "Symptomatic Population": {
            "Units": "Count",
            "Data": [0, 0, 0, 0, 0]
        },
        "True Prevalence": {
            "Units": "% Detected Infected",
            "Data": [0.2622045576572, 0.2622045576572, 0.2618784606457, 0.2616574466228, 0.2617049515247]
        },
        "Variant Fraction-PfEMP1 Major": {
            "Units": "Fraction",
            "Data": [0.1735506355762, 0.1736092269421, 0.1737322509289, 0.1737831532955, 0.173709705472]
        }
    }
}
_images/malaria-inset-chart.png
PropertyReport

The property output report (PropertyReport.json) is a JSON-formatted file with the channel output results of the simulation, defined by the groups set up using IndividualProperties in the demographics file. See NodeProperties and IndividualProperties for more information. The report contains the count of individuals with each possible IP key-value combination. The < channel-title > tells you the statistic and property that are being counted. For example, it allows you to compare disease deaths for people in the high risk group versus the low risk group.

To generate the property report, set the Enable_Property_Output configuration parameter to 1.

The file contains a header and a channels section.

Channels

The channels section contains the following parameters.

Parameter

Data type

Description

<Channel_Title>

string

The title of the particular property channel. The channel titles use the following conventions: for a single property, <channel type>:<property>:<value>, and for multiple properties, <channel type>:<property 1>:<value>,<property 2>:<value>. For example, Infected:Accessibility:Easy or New Infections:Accessibility:Difficult,Risk:High.

Units

string

The units used for this channel.

Data

array

A list of the channel data at each time step.
Channel_Title

The following information is available for the <Channel_Title> parameters. Note that these channels will have separate sections for each IndividualProperites key:value pair.

Channel

Description

Disease Deaths

The number of people who died from an infection that day.

Infected

The number of people who have at least one infection on that day. These infections could be so new that they are not detectable via a diagnostic.

New Infections

The number of people with new infections that day.

Statistical Population

The total number of people in the simulation that day.
Example

The following is a sample of a PropertyReport.json file.

{
    "Header": {
        "DateTime": "Mon Feb 15 21:49:24 2016",
        "DTK_Version": "5538 trunk 2015/08/07 14:40:43",
        "Report_Type": "InsetChart",
        "Report_Version": "3.2",
        "Start_Time": 0,
        "Simulation_Timestep": 1,
        "Timesteps": 10,
        "Channels": 8
    },
    "Channels": {
        "Disease Deaths:Accessibility:Easy": {
            "Units": "",
            "Data": [
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Disease Deaths:Accessibility:Hard": {
            "Units": "",
            "Data": [
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Infected:Accessibility:Easy": {
            "Units": "",
            "Data": [
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Infected:Accessibility:Hard": {
            "Units": "",
            "Data": [
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0
            ]
        },
        "New Infections:Accessibility:Easy": {
            "Units": "",
            "Data": [
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0
            ]
        },
        "New Infections:Accessibility:Hard": {
            "Units": "",
            "Data": [
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Statistical Population:Accessibility:Easy": {
            "Units": "",
            "Data": [
                6946,
                6946,
                6946,
                6946,
                6946,
                6946,
                6946,
                6946,
                6946,
                6946
            ]
        },
        "Statistical Population:Accessibility:Hard": {
            "Units": "",
            "Data": [
                3054,
                3054,
                3054,
                3054,
                3054,
                3054,
                3054,
                3054,
                3054,
                3054
            ]
        }
    }
}
ReportEventCounter

The event counter report (ReportEventCounter.json) is a JSON-formatted file that keeps track of how many of each event types occurs during a time step. The report produced is similar to the InsetChart.json channel report, where there is one channel for each event defined in the configuration file (config.json). This report only counts events; a similar report, ReportEventRecorder, will provide information about the person at the time of the event.

Configuration

The following parameters need to be configured to generate the report:

Parameter name

Data type

Min

Max

Default

Description

Filename_Suffix

string

NA

NA

(empty string)

Augments the filename of the report. If multiple reports are being generated, this allows you to distinguish among the multiple reports.

Start_Day

float

0

3.40282e+38

0

The day of the simulation to start collecting data.

End_Day

float

0

3.40282e+38

3.40282e+38

The day of the simulation to stop collecting data.

Node_IDs_Of_Interest

array of integers

0

2.14748e+09

[]

Data will be collected for the nodes in this list. Empty list implies all nodes.

Min_Age_Years

float

0

9.3228e+35

0

Minimum age in years of people to collect data on.

Max_Age_Years

float

0

9.3228e+35

9.3228e+35

Maximum age in years of people to collect data on.

Must_Have_IP_Key_Value

string

NA

NA

(empty string)

A Key:Value pair that the individual must have in order to be included. Empty string means to not include IPs in the selection criteria.

Must_Have_Intervention

string

NA

NA

(empty string)

The name of the intervention that the person must have in order to be included. Empty string means to not include interventions in the selection criteria.

Event_Trigger_List

list of strings

NA

NA

NA

The list of event triggers for the events included in the report. There will be a channel in the report for each event.

 
{
    "Reports": [{
        "class": "ReportEventCounter",
        "Filename_Suffix": "Node1",
        "Start_Day": 365,
        "End_Day": 465,
        "Node_IDs_Of_Interest": [ 1 ],
        "Min_Age_Years": 5,
        "Max_Age_Years": 10,
        "Must_Have_IP_Key_Value": "Risk:HIGH",
        "Must_Have_Intervention": "SimpleVaccine",
        "Event_Trigger_List": [
            "NewInfectionEvent",
            "NewClinicalCase"
        ]
    }],
    "Use_Defaults": 1
}
Channels

The channels section contains the following parameters:

Parameter

Data type

Description

<Event Names>

string

The name of the event.

Data

array

An array of event counts where each entry is the number of events that occurred during the timestep.

Units

string

Empty string, but it is the ‘event count’.
Example

The following is an example of ReportEventCounter.json.

{
    "Header": {
        "Channels": 4,
        "DTK_Version": "4148 Malaria-Ongoing (bb265ad) May 16 2019 09:39:35",
        "DateTime": "Tue Mar 12 15:16:08 2019",
        "Report_Type": "InsetChart",
        "Report_Version": "3.2",
        "Simulation_Timestep": 1,
        "Start_Time": 0,
        "Timesteps": 3
    },
    "Channels": {
        "Births": {
            "Data": [
                0,
                1,
                1
            ],
            "Units": ""
        },
        "EveryUpdate": {
            "Data": [
                1000,
                1001,
                1002
            ],
            "Units": ""
        },
        "NewInfectionEvent": {
            "Data": [
                110,
                0,
                2
            ],
            "Units": ""
        },
        "NonDiseaseDeaths": {
            "Data": [
                0,
                0,
                0
            ],
            "Units": ""
        }
    }
}
ReportEventRecorder

The health events and interventions report (ReportEventRecorder.csv) provides information on each individual’s demographics and health status at the time of an event. Additionally, it is possible to see the value of specific IndividualProperties, as assigned in the demographics file (see NodeProperties and IndividualProperties for more information).

This report is highly customizable; see the Configuration section, below, for details and instructions. Disease-specific information and examples are provided at the end of page.

Configuration

To generate this report, the following parameters must be configured in the config.json file (applies to all simulation types):

Parameter name

Data type

Min

Max

Default

Description

Report_Event_Recorder

boolean

0

1

0

Set to 1 to generate the report.

Report_Event_Recorder_Events

array of strings

NA

NA

[]

The list of events to include or exclude in the output report, based on how Report_Event_Recorder_Ignore_Events_In_List is set. See Event list for a list of all possible built-in events. Custom_Individual_Events may also be included. Warning: If the list is empty and Report_Event_Recorder_Ignore_Events_In_List is set to 0, no events will be returned.

Report_Event_Recorder_Ignore_Events_In_List

boolean

0

1

0

If set to false (0), only the events listed in Report_Event_Recorder_Events will be included in the output report. If set to true (1), only the events listed in Report_Event_Recorder_Events will be excluded, and all other events will be included. To return all events from the simulation, set this value to 1 and leave the the Report_Event_Recorder_Events array empty.

Report_Event_Recorder_Individual_Properties

array of strings

NA

NA

[]

An array of optional individual property (IP) keys to be added to the report. One column will be added for each IP Key listed, indicating the individual’s value for that IP Key at the time of the event. See Individual and node properties for details on setting individual properties.

Report_Event_Recorder_Start_Day

float

0

3.40282e+38

0

The day of the simulation to start collecting data.

Report_Event_Recorder_End_Day

float

0

3.40282e+38

3.40282e+38

The day of the simulation to stop collecting data.

Report_Event_Recorder_Node_IDs_Of_Interest

array of integers

0

2.14748e+09

[]

Data will be collected for the nodes in this list. Leave the array empty (default value) to collect data on all nodes.

Report_Event_Recorder_PropertyChange_IP_Key_Of_Interest

string

NA

NA

""

If the string is not empty, then the recorder will add the PropertyChange event to the list of events that the report is listening to. However, it will only record the events where the property changed the value of the given key.

Report_Event_Recorder_Min_Age_Years

float

0

9.3228e+35

0

Minimum age in years of people to collect data on.

Report_Event_Recorder_Max_Age_Years

float

0

9.3228e+35

9.3228e+35

Maximum age in years of people to collect data on.

Report_Event_Recorder_Must_Have_IP_Key_Value

string

NA

NA

""

An individual property (IP) Key:Value pair that an individual must have in order to be included in the report. Leave the string empty (default value) to not include IPs in the selection criteria. See Individual and node properties for more information.

Report_Event_Recorder_Must_Have_Intervention

string

NA

NA

""

The name of the intervention that the individual must have in order to be included in the report. Leave the string empty (default value) to not include interventions in the selection criteria. See Individual-level interventions for more information.

 
{
    "Report_Event_Recorder": 1,
    "Report_Event_Recorder_Events": [],
    "Report_Event_Recorder_Ignore_Events_In_List": 1,
    "Report_Event_Recorder_Individual_Properties": ["Risk"],
    "Report_Event_Recorder_Start_Day": 1,
    "Report_Event_Recorder_End_Day": 300,
    "Report_Event_Recorder_Node_IDs_Of_Interest": [ 1, 2, 3 ],
    "Report_Event_Recorder_PropertyChange_IP_Key_Of_Interest": "",
    "Report_Event_Recorder_Min_Age_Years": 20,
    "Report_Event_Recorder_Max_Age_Years": 60,
    "Report_Event_Recorder_Must_Have_IP_Key_Value": "Risk:HIGH",
    "Report_Event_Recorder_Must_Have_Intervention": "",
}
Output file data

The report contains the following data channels for all simulation types.

Data channel

Data type

Description

Time

float

The time of the event, in days.

Node_ID

integer

The identification number of the node.

Event_Name

string

The event being logged. If Report_Event_Recorder_Ignore_Events_In_List is set to 0, then the event name will be one of the ones listed under Report_Event_Recorder_Events. Otherwise, it will be the name of any other event that occurs and is not listed under Report_Event_Recorder_Events.

Individual_ID

integer

The individual’s unique identifying number

Age

integer

The age of the individual in units of days. Divide by 365 to obtain age in years.

Gender

character

The gender of the individual: “M” for male, or “F” for female.

Infected

boolean

Describes whether the individual is infected or not; 0 when not infected, 1 for infected.

Infectiousness

float

A value ranging from 0 to 1 that indicates how infectious an individual is, with 0 = not infectious and 1 = very infectious. HIV and malaria simulation types have specific definitions listed below.

<IP Key>

string

An additional column will be added to the report for each IP Key listed in Report_Event_Recorder_Individual_Properties. The values shown in each column will be the value for the indicated key, for that individual, at the time of the event.

Additional data channels used in malaria simulations:

Data channel

Data type

Description

RelativeBitingRate

float

A number indicating the likelihood of an individual being bitten by mosquitoes. This can include any biting rates set by the user and/or a value based on the age or size of the individual.

TrueParasiteDensity

float

The number of infected red blood cells per microliter of blood.

TrueGametocyteDensity

float

The true number of gametocytes per microliter of blood.

HasClinicalSymptoms

T/F

T implies that the person’s fever has been above Clinical_Fever_Threshold_Low for at least Min_Days_Between_Clinical_Incidents since the person was a NewClinicalCase (their fever first peaked above Clinical_Fever_Threshold_High), F implies that the person is not considered to have clinical symptoms.
Example

The following is an example of a ReportEventRecorder.csv report from a malaria simulation:

Time

Node_ID

Event_Name

Individual_ID

Age

Gender

Infected

Infectiousness

Relative BitingRate

TrueParasite Density

True Gametocyte Density

HasClinicalSymptoms

30

7

EveryUpdate

15

4797.43

M

1

0

1

19791.2

0

T

35

7

EveryUpdate

1037

642.683

F

1

0.227641

1

644.143

42.5087

F

35

7

EveryUpdate

1058

12432.7

F

1

0.036678

1

970.459

7.63285

F

35

7

EveryUpdate

1065

14232.2

F

1

0.006245

1

110913

7.63259

T

35

7

EveryUpdate

1093

19206.8

F

1

0.035739

1

1020.28

7.63263

F

35

7

EveryUpdate

1114

17144.3

M

1

0.006236

1

73854.4

7.63208

T

35

7

EveryUpdate

1135

5739.73

F

1

0.007743

1

106963

9.4479

T

35

7

EveryUpdate

1149

6064.07

F

1

0.044953

1

1138.48

9.00229

F

35

7

EveryUpdate

1163

21692.3

M

1

0.006216

1

118332

7.63267

T

35

7

EveryUpdate

1170

14238.3

F

1

0.006216

1

118332

7.6325

T

35

7

EveryUpdate

6924

7388.82

F

1

0.03641

1

1248.47

7.63267

F

35

7

EveryUpdate

6938

20377.8

F

1

0.006216

1

118331

7.63272

T

35

7

EveryUpdate

6952

29412.4

M

1

0.006216

1

118333

7.63291

T

35

7

EveryUpdate

6959

32766.3

M

1

0.037062

1

952.578

7.63273

F

35

7

EveryUpdate

6966

1245.33

F

1

0.025033

1

64225.2

30.0704

T

35

7

EveryUpdate

6994

13428.8

F

1

0.006236

1

113029

7.63279

T

40

7

EveryUpdate

15

4807.43

M

1

1

1

14.8706

940.014

F
ReportHumanMigrationTracking

The human migration tracking report (ReportHumanMigrationTracking.csv) is a CSV-formatted report that provides details about human travel during simulations. The finished report will provide one line for each surviving individual that migrates during the simulation.

Configuration

There are no special parameters that need to be configured to generate the report. However, the simulation must have migration enabled (see the migration parameters in Configuration parameters).

Report structure and channel descriptions

The file contains the following data channels:

Data channel

Data type

Description

Time

integer

The day that an individual leaves their current node.

IndividualID

integer

The ID of the migrating individual.

AgeYears

integer

The age (in years) of the migrating individual.

Gender

string

The gender of the individual. Possible values are M or F.

IsAdult

enum

Possible values are T or F; T (true) if individual’s age is greater than the configuration parameter Minimum_Adult_Age_Years.

Home_NodeID

string

The external ID of the node the individual considers to be their ‘home’ node.

From_NodeID

string

The external ID of the node the individual is moving from.

To_NodeID

string

the external ID of the node the individual is moving to.

MigrationType

enum

The type of migration that is occurring. When the event is SimulationEnd, the values are either ‘home’ or ‘away,’ indicating if the individual was in their home node or not when the simulation ended. Otherwise, possible values are air, local, sea, regional, local, family, or intervention.

Event

enum

Possible values are Emigrating, NonDiseaseDeaths, DiseaseDeaths, or SimulationEnd. Emigrating means the individual is moving, SimulationEnd means the simulation has finished.
Example

The following is an example of ReportHumanMigrationTracking.csv.

Time

IndividualID

AgeYears

Gender

IsAdult

Home_NodeID

From_NodeID

To_NodeID

MigrationType

Event

2

46

94.6986

F

T

1

1

4

local

Emigrating

4

108

35.7116

M

T

2

2

5

local

Emigrating

9

108

35.7253

M

T

2

5

2

regional

Emigrating

12

46

94.726

F

T

1

4

9

regional

Emigrating

13

46

94.7287

F

T

1

9

6

local

Emigrating

17

108

35.7472

M

T

2

2

5

local

Emigrating

22

46

94.7534

F

T

1

6

7

regional

Emigrating

23

108

35.7637

M

T

2

5

2

regional

Emigrating

25

108

35.7691

M

T

2

2

1

local

Emigrating

28

46

94.7698

F

T

1

7

4

local

Emigrating

35

46

94.789

F

T

1

4

7

regional

Emigrating

37

46

94.7945

F

T

1

7

4

local

Emigrating

44

46

94.8137

F

T

1

4

8

regional

Emigrating

100

1

38.9821

M

T

1

1

1

home

SimulationEnd

100

46

94.9643

F

T

1

8

8

regional

SimulationEnd

100

108

35.9719

M

T

2

1

1

local

SimulationEnd
ReportNodeDemographics

The node demographics report (ReportNodeDemographics.csv) is a CSV-formatted report that provides population information stratified by node. For each time step, the report will collect data on each node and age bin.

Configuration

To generate this report, the following parameters must be configured in the custom_reports.json file:

Parameter name

Data type

Min

Max

Default

Description

Age_Bins

array of floats

-3.04E+38

3.04E+38

[ ]

The age bins (in years, in ascending order) to aggregate within and report. An empty array does not stratify by age.

IP_Key_To_Collect

string

NA

NA

(empty string)

The name of the IndividualProperties (IP) key by which to stratify the report. An empty string means the report is not stratified by IP.

Stratify_By_Gender

boolean

NA

NA

1

Set to true (1) to stratify by gender; a value of 0 will not stratify by gender.

 
{
    "Reports": [
        {
            "class": "ReportNodeDemographics",
            "Age_Bins": [
                10,
                20,
                30,
                40,
                50,
                60,
                70,
                80,
                90,
                100,
                125
            ],
            "IP_Key_To_Collect": "",
            "Stratify_by_Gender": 1
        }
    ],
    "Use_Defaults": 1
}
Output file data

The report will contain the following output data, divided between stratification columns and data columns.

Stratification columns

Parameter

Data type

Description

Time

float

The day of the simulation that the data was collected.

NodeID

integer

The external ID of the node for the data in the row in the report.

Gender

enum

Possible values are M or F; the gender of the individuals in the row in the report. This column only appears if Stratify_By_Gender = 1.

AgeYears

float

The max age in years of the bin for the individuals in the row in the report. If Age_Bins is empty, this column does not appear.

IndividualProp

string

The value of the IP for the individuals in the row in the report. If IP_Key_To_Collect is an empty string, then this column does not appear.
Data columns

Parameter

Data type

Description

NumIndividuals

integer

The number of individuals that meet the stratification values.

NumInfected

integer

The number of infected individuals that meet the stratification values.

NodeProp = <Node Property Keys>

string

For each possible Node Property, there is one column where the data in the column is the value of that particular property. If there are no Node Properties, then there are no columns.
Example

The following is an example of ReportNodeDemographics.csv.

Time

NodeID

Gender

AgeYears

IndividualProp=Risk

NumIndividuals

NumInfected

0

2

M

40

High

1455

0

0

2

M

40

Low

627

0

0

2

M

125

High

47

0

0

2

M

125

Low

24

0

0

2

F

40

High

1390

0

0

2

F

40

Low

609

0

0

2

F

125

High

43

0

0

2

F

125

Low

22

0

0

1

M

40

High

459

0

0

1

M

40

Low

1630

0

0

1

M

125

High

18

0

0

1

M

125

Low

47

0

0

1

F

40

High

439

0

0

1

F

40

Low

1597

0

0

1

F

125

High

17

0

0

1

F

125

Low

67

0

1

2

M

40

High

1455

0

1

2

M

40

Low

627

0

1

2

M

125

High

47

0

1

2

M

125

Low

24

0

1

2

F

40

High

1390

0

1

2

F

40

Low

609

0

1

2

F

125

High

43

0

1

2

F

125

Low

22

0

1

1

M

40

High

459

0

1

1

M

40

Low

1630

26

1

1

M

125

High

18

0

1

1

M

125

Low

47

0

1

1

F

40

High

439

1

1

1

F

40

Low

1597

17

1

1

F

125

High

17

0

1

1

F

125

Low

67

0
SpatialReport

The spatial output report breaks the channel data down per node, rather than across the entire simulation. It is a set of binary files, consisting of one file per channel. For each value set in the Spatial_Output_Channels configuration parameter array, a binary file with the name convention SpatialReport_<channel>.bin is generated. In addition, Enable_Spatial_Output must be set to 1.

The binary format of the file consists of a stream of 4-byte integers followed by a stream of 4-byte floating point values. The first value is a 4-byte integer representing the number of nodes in the file and the second is a 4-byte integer that contains the number of time steps in the file. Following these two values is a stream of 4-byte integers that contain the node ID values in the order they will appear in the rest of the file. Following the node IDs is an array of 4-byte floating point values that represent the output values at the first time step for each node. The next array contains the values at the second time step, and so on.

The following diagram shows the format for data in the spatial output report file:

_images/spatialOutputBFF.jpg
Configuration

The following is an example of a spatial output channel configuration (config.json), and the following table defines the spatial output channels you can add to this report.

{
    "Enable_Spatial_Output": 1,
    "Spatial_Output_Channels": [
        "Population",
        "New_Infections",
        "Prevalence",
        "Births"
    ]
}
Output file data

Channel

Description

Adult_Vectors

The total number of adult female vectors in the node at each time step. Includes vectors from all species and all infectious sates (not infected, infected, and infectious).

Air_Temperature

The air temperature of that day for that node in Celcius.

Births

The live births cumulative up to that day for that node.

Blood_Smear_Gametocyte_Prevalence

The fraction of the population that is detectable with the BLOOD_SMEAR_GAMETOCYTES version of MalariaDiagnostic. The detectability of the diagnostic is controlled by the parameters Report_Gametocyte_Smear_Sensitivity and Report_Detection_Threshold_Blood_Smear_Gametocytes.

Blood_Smear_Parasite_Prevalence

The fraction of the population that is detectable with the BLOOD_SMEAR_PARASITES version of MalariaDiagnostic. The detectability of the diagnostic is controlled by the parameters Report_Parasite_Smear_Sensitivity and Report_Detection_Threshold_Blood_Smear_Parasites.

Campaign_Cost

The cumulative cost of the interventions that have been distributed to that node. This is based on the Cost_To_Consumer parameter in the interventions used.

Daily_Bites_Per_Human

The average number of mosquito bites received per individual on that day.

Daily_EIR

The number of infectious bites per person in the node made that day/reporting period as determined by the vector model. It includes the infectious bites of all species for both indoor and outdoor bites.

Disease_Deaths

The number of people that died in the node that day. The population for the node at the beginning of the next time step will be the population at this time step minus the number of deaths plus new births.

Fever_Prevalence

This fraction is the number of people whose fever is above the Report_Detection_Threshold_Fever threshold divided by the number of people in the node.

Human_Infectious_Reservoir

The sum of the infectiousness of each person in the node; a person’s infectiousness is the probability of infecting a mosquito during a successful blood meal.

Infected

The fraction of the population currently infected.

Infectious Vectors

The fraction of vectors in the simulation that are currently infected and infectious.

Land_Temperature

The temperature of the land/ground that day in that node in degrees Celsius.

Mean_Parasitemia

The geometric mean number of parasites per microliter of blood.

New_Clinical_Cases

The number of new clinical cases for a given day.

New_Infections

The number of people who received a new infection in that node on that time step.

New_Reported_Infections

The number of people reported to have received a new infection in that node on that time step. This number should be about 50% of the value in New_Infections.

New_Severe_Cases

The number of new severe cases of malaria on that day in the node. An individual is considered to be a severe case if the combined probability of anemia, parasite density, and fever is greater than a uniform random number. This combined probability is the combination of sigmoid using the following parameters: Anemia_Severe_Threshold and Anemia_Severe_Inverse_Width, Parasite_Severe_Threshold and Parasite_Severe_Inverse_Width, and Fever_Severe_Threshold and Fever_Severe_Inverse_Width.

PCR_Gametocyte_Prevalence

The fraction of the population that is detectable with the PCR_GAMETOCYTES version of MalariaDiagnostic. The detectability of the diagnostic is controlled by the parameter Report_Detection_Threshold_PCR_Gametocytes.

PCR_Parasite_Prevalence

The fraction of the population that is detectable with the PCR_PARASITES version of MalariaDiagnostic. The detectability of the diagnostic is controlled by the parameter Report_Detection_Threshold_PCR_Parasites.

PfHRP2_Prevalence

The fraction of the population that is detectable with the PF_HRP2 version of MalariaDiagnostic. The detectability of the diagnostic is controlled by the parameter Report_Detection_Threshold_PCR_PfHRP2.

Population

The total number of individuals in the node on that day.

Prevalence

The fraction of the population currently infected in that node on that day.

Rainfall

The number millimeters of rainfall in that node on that day.

Relative_Humidity

The amount of water vapor present in air expressed as a percentage of the amount needed for saturation at the same temperature for the node on that day.

True_Prevalence

The fraction of the population that is detectable with the TRUE_PARASITE_DENSITY version of MalariaDiagnostic. The detectability of the diagnostic is controlled by the parameter Report_Detection_Threshold_True_Parasite_Density.
ReportVectorGenetics

The vector genetics report is a CSV-formatted report that collects information on how many vectors of each genome/allele combination exist at each time, node, and vector state. Information can only be collected on one species per report.

Configuration

To generate this report, the following parameters must be configured in the custom_reports.json file:

Parameter

Data type

Min

Max

Default

Description

Filename_Suffix

string

NA

NA

(empty string)

Augments the filename of the report. If multiple reports are being generated, this allows you to distinguish among the multiple reports.

Start_Day

float

0

3.40282e+38

0

The day of the simulation to start collecting data.

End_Day

float

0

3.40282e+38

3.40282e+38

The day of the simulation to stop collecting data.

Node_IDs_Of_Interest

array of integers

0

2.14748e+09

[]

Data will be collected for the nodes in this list. Empty list implies all nodes.

Allele_Combinations_For_Stratification

array of strings

NA

NA

[]

If stratifying by allele, this will be the list of alleles to stratify by. Depends on Stratify_By = ALLELE.

Alleles_For_Stratification

array of strings

NA

NA

[]

The list of alleles for which to collect frequency counts. If the list is empty, the report uses a list of all possible alleles. Depends on Stratify_By = ALLELE_FREQ

Combine_Similar_Genomes

boolean

NA

NA

[]

If set to true (1), genomes are combined for each locus (ignoring gender) if the set of alleles of the two genomes are the same. Note that ‘1-0’ is considered to be the same as ‘0-1’. Depends on Stratify_By = GENOME or SPECIFIC_GENOME.

Gender

enum

NA

NA

VECTOR_FEMALE

The gender to include in the file; possible values are VECTOR_FEMALE, VECTOR_MALE, and VECTOR_BOTH_GENDERS.

Include_Vector_State_Columns

boolean

NA

NA

1

If set to true (1), the columns for each vector state (Eggs, Larvae, etc) will be included.

Include_Death_By_State_Columns

boolean

NA

NA

0

Adds columns for the number of vectors that died in this state during this time step as well as the average age at death. It adds two columns for each of the following states: ADULT, INFECTED, INFECTIOUS, and MALE.

Species

string

NA

NA

(if not specified, the first species found will be used)

The species to include information on; the name must exist in Vector_Species_Params in the config.json file. The name will be added to the report name.

Specific_Genome_Combinations_For_Stratification

array of strings

NA

NA

NA

If stratifying by SPECIFIC_GENOME, this is the list of genomes to stratify by. ‘*’ will list all entries at that location, and ‘?’ will combine all entries at that location. Depends on Stratify_By = SPECIFIC_GENOME.

Stratify_By

enum

NA

NA

GENOME

Determines how the report will be stratified; possible values are GENOME, SPECIFIC_GENOME, and ALLELE.

The following is an example of an input file for this report:

{
    "Reports": [{
            "class": "ReportVectorGenetics",
            "Species": "arabiensis2",
            "Gender": "VECTOR_FEMALE",
            "Stratify_By": "GENOME",
            "Start_Day": 365,
            "End_Day": 465,
            "Filename_Suffix": "Node1",
            "Node_IDs_Of_Interest": [ 1 ]
        },
        {
            "class": "ReportVectorGenetics",
            "Species": "arabiensis2",
            "Gender": "VECTOR_BOTH_GENDERS",
            "Stratify_By": "ALLELE_FREQ",
            "Alleles_For_Stratification" : [ "a0", "a1", "b0", "b1" ],
            "Filename_Suffix": "Test67"
        },
        {
            "class": "ReportVectorGenetics",
            "Species": "arabiensis2",
            "Gender": "VECTOR_FEMALE",
            "Stratify_By": "ALLELE",
            "Allele_Combinations_For_Stratification": [
                [ "a0", "b0" ],
                [ "a1", "b1" ]
            ],
            "Include_Vector_State_Columns": 0
        },
        {
            "class": "ReportVectorGenetics",
            "Species": "arabiensis2",
            "Stratify_By": "SPECIFIC_GENOME",
            "Gender": "VECTOR_FEMALE",
            "Specific_Genome_Combinations_For_Stratification": [
                {
                    "Allele_Combination": [
                        [  "X",  "X" ],
                        [ "a0",  "*" ],
                        [ "b1", "b0" ]
                    ]
                },
                {
                    "Allele_Combination": [
                        [  "X",  "X" ],
                        [ "a1", "a0" ],
                        [ "b0",  "*" ]
                    ]
                },
                {
                    "Allele_Combination": [
                        [  "X",  "X" ],
                        [ "a1",  "*" ],
                        [ "b1", "b0" ]
                    ]
                },
                {
                    "Allele_Combination": [
                        [  "X",  "X" ],
                        [ "a1", "a0" ],
                        [ "b1",  "*" ]
                    ]
                }
            ],
            "Combine_Similar_Genomes": 1,
            "Include_Vector_State_Columns": 1
        }
    ],
    "Use_Defaults": 1
}
Output file data

The output report will contain the following information.

Stratification columns

Parameter

Data type

Description

Time

integer

The day of the simulation that the data was collected.

NodeID

integer

The External ID of the node that the data is being collected for.

Alleles

string

If Stratify_By = ALLELE, then the column will be all of the possible alleles plus the values in Allele_Combinations_For_Stratification.

Genome

string

If Stratify_By = GENOME or SPECIFIC_GENOME, then this is the ‘Genome’ column and for each time and NodeID, the column will contain the possible combinations of genomes.
Data columns

Parameter

Data type

Description

VectorPopulation

integer

If Gender = VECTOR_BOTH_GENDERS or VECTOR_FEMALE, then this column will contain the number of female vectors that are in the states STATE_INFECTIOUS, STATE_INFECTED, or STATE_ADULT.

STATE_INFECTIOUS

integer

If Gender is VECTOR_BOTH_GENDERS or VECTOR_FEMALE, then this column will contain the number of female vectors that are in this state.

STATE_INFECTED

integer

If Gender is VECTOR_BOTH_GENDERS or VECTOR_FEMALE, then this column will contain the number of female vectors that are in this state.

STATE_ADULT

integer

If Gender is VECTOR_BOTH_GENDERS or VECTOR_FEMALE, then this column will contain the number of female vectors that are in this state.

STATE_MALE

integer

If Gender is VECTOR_BOTH_GENDERS or VECTOR_MALE, then this column will contain the number of mature male vectors.

STATE_IMMATURE

integer

This column contains the number of vectors (male and female) in the ‘immature’ state.

STATE_LARVA

integer

This column contains the number of larva (male and female).

STATE_EGG

integer

This column contains the number of eggs (male and female).

VectorPopulationNumDied

integer

If Include_Death_By_State_Columns is true and Gender = VECTOR_BOTH_GENDERS or VECTOR_FEMALE, then this column will contain the number of female vectors that died and were in states STATE_INFECTIOUS, STATE_INFECTED, or STATE_ADULT.

InfectiousNumDied

integer

If Include_Death_By_State_Columns is true, Include_Vector_State_Columns is true, and Gender = VECTOR_BOTH_GENDERS or VECTOR_FEMALE, then this column will contain the number of infectious, mature, female vectors that died during this time step.

InfectedNumDied

integer

If Include_Death_By_State_Columns is true, Include_Vector_State_Columns is true, and Gender = VECTOR_BOTH_GENDERS or VECTOR_FEMALE, then this column will contain the number of infected, mature, female vectors that died during this time step.

AdultsNumDied

integer

If Include_Death_By_State_Columns is true, Include_Vector_State_Columns is true, and Gender = VECTOR_BOTH_GENDERS or VECTOR_FEMALE, then this column will contain the number of mature female vectors that are neither infected or infectious that died during this time step.

MaleNumDied

integer

If Include_Death_By_State_Columns is true, Include_Vector_State_Columns is true, and Gender = VECTOR_BOTH_GENDERS or VECTOR_MALE, then this column will contain the number of mature male vectors that died during this time step.

VectorPopulationAvgAgeAtDeath

float

If Include_Death_By_State_Columns is true and Gender = VECTOR_BOTH_GENDERS or VECTOR_FEMALE, then this column will contain the average age (in days) of the the infectious, mature, female vectors that died during this time step.

InfectiousAvgAgeAtDeath

float

If Include_Death_By_State_Columns is true, Include_Vector_State_Columns is true, and Gender = VECTOR_BOTH_GENDERS or VECTOR_FEMALE, then this column will contain the average age (in days) of the the infectious, mature, female vectors that died during this time step.

InfectedAvgAgeAtDeath

float

If Include_Death_By_State_Columns is true, Include_Vector_State_Columns is true, and Gender = VECTOR_BOTH_GENDERS or VECTOR_FEMALE, then this column will contain the average age (in days) of the the infected, mature, female vectors that died during this time step.

AdultsAvgAgeAtDeath

float

If Include_Death_By_State_Columns is true, Include_Vector_State_Columns is true, and Gender = VECTOR_BOTH_GENDERS or VECTOR_FEMALE, then this column will contain the average age (in days) of the the mature, female vectors that are neither infected or infectious that died during this time step.

MaleAvgAgeAtDeath

float

If Include_Death_By_State_Columns is true, Include_Vector_State_Columns is true, and Gender = VECTOR_BOTH_GENDERS or VECTOR_MALE, then this column will contain the average age (in days) of the the mature male vectors that died during this time step.
Example

The following are examples of ReportVectorGenetics.csv files. The Death By State columns are not included in the examples.

This table is stratified by genome and female vectors.

Time

NodeID

Genome

VectorPopulation

STATE_INFECTIOUS

STATE_INFECTED

STATE_ADULT

STATE_IMMATURE

STATE_LARVA

STATE_EGG

0

1

X-a0-b0:X-a0-b0

293

0

0

293

0

0

8903

0

1

X-a0-b0:X-a1-b0

181

0

0

181

0

0

5797

0

1

X-a0-b0:X-a0-b1

657

0

0

657

0

0

21948

0

1

X-a0-b0:X-a1-b1

454

0

0

454

0

0

14096

0

1

X-a1-b0:X-a0-b0

194

0

0

194

0

0

6360

0

1

X-a1-b0:X-a1-b0

124

0

0

124

0

0

3934

0

1

X-a1-b0:X-a0-b1

437

0

0

437

0

0

14967

0

1

X-a1-b0:X-a1-b1

285

0

0

285

0

0

9140

0

1

X-a0-b1:X-a0-b0

638

0

0

638

0

0

22200

0

1

X-a0-b1:X-a1-b0

439

0

0

439

0

0

15429

0

1

X-a0-b1:X-a0-b1

1515

0

0

1515

0

0

50901

0

1

X-a0-b1:X-a1-b1

1039

0

0

1039

0

0

33525

0

1

X-a1-b1:X-a0-b0

435

0

0

435

0

0

15209

0

1

X-a1-b1:X-a1-b0

293

0

0

293

0

0

9808

0

1

X-a1-b1:X-a0-b1

1032

0

0

1032

0

0

34564

0

1

X-a1-b1:X-a1-b1

695

0

0

695

0

0

23311

1

1

X-a0-b0:X-a0-b0

261

0

0

261

0

5877

9167

1

1

X-a0-b0:X-a1-b0

157

0

0

157

0

3830

6470

1

1

X-a0-b0:X-a0-b1

564

0

0

564

0

14393

21121

1

1

X-a0-b0:X-a1-b1

397

0

0

397

0

9134

13920

1

1

X-a1-b0:X-a0-b0

167

0

0

167

0

4143

5533

1

1

X-a1-b0:X-a1-b0

104

0

0

104

0

2536

3779

1

1

X-a1-b0:X-a0-b1

377

0

0

377

0

9836

13234

1

1

X-a1-b0:X-a1-b1

243

0

0

243

0

6002

9229

1

1

X-a0-b1:X-a0-b0

564

0

0

564

0

14428

19047

1

1

X-a0-b1:X-a1-b0

381

0

0

381

0

10047

13371

1

1

X-a0-b1:X-a0-b1

1322

0

0

1322

0

33210

47190

1

1

X-a0-b1:X-a1-b1

898

0

0

898

0

21974

31616

1

1

X-a1-b1:X-a0-b0

381

0

0

381

0

9938

12977

1

1

X-a1-b1:X-a1-b0

257

0

0

257

0

6393

9156

1

1

X-a1-b1:X-a0-b1

886

0

0

886

0

22766

30973

1

1

X-a1-b1:X-a1-b1

604

0

0

604

0

15236

20404

This table is stratified by genome and male vectors.

Time

NodeID

Genome

STATE_MALE

STATE_IMMATURE

STATE_LARVA

STATE_EGG

728

1

X-a0-b0:Y-a1-b0

471

234

2889

76019

728

1

X-a0-b0:Y-a0-b1

472

238

2876

77305

728

1

X-a0-b0:Y-a1-b1

6139

3607

44504

1200234

728

1

X-a1-b0:Y-a1-b1

20783

12670

166170

4672298

728

1

X-a0-b1:Y-a1-b1

20879

12732

166591

4748180

728

1

Other

73196

46195

639267

18714288

729

1

X-a0-b0:Y-a1-b0

460

224

2818

76957

729

1

X-a0-b0:Y-a0-b1

461

238

2798

79896

729

1

X-a0-b0:Y-a1-b1

6076

3578

44258

1205125

729

1

X-a1-b0:Y-a1-b1

20824

12630

165392

4658639

729

1

X-a0-b1:Y-a1-b1

20801

12677

166219

4709209

729

1

Other

73268

46247

640706

18831238

This table is stratified by allele and female vectors, with vector state columns included.

Time

NodeID

Alleles

VectorPopulation

604

1

Ad

36238

604

1

Am

60886

604

1

Aw

63143

604

1

Bd

9419

604

1

Bm

13313

604

1

Bw

96119

604

1

Ce

15029

604

1

Cw

96861

604

1

De

12088

604

1

Dw

97095

604

1

X

97469

604

1

Y

0

605

1

Ad

36232

605

1

Am

60897

605

1

Aw

62762

605

1

Bd

9503

605

1

Bm

13334

605

1

Bw

95893

605

1

Ce

14964

605

1

Cw

96645

605

1

De

12253

605

1

Dw

96877

605

1

X

97250

605

1

Y

0
ReportVectorMigration

The vector migration report (ReportVectorMigration.csv) provides detailed information on when and where vectors are migrating. As there will be one line for each migrating vector, it is beneficial to use the parameters Start_Day and End_Day to limit the size of the output file. Note that only female vectors migrate.

See Vector migration files for more information on how to create vector migration files.

Configuration

To generate the report, configure the following parameters in the custom_reports.json file:

Parameter

Data type

Min

Max

Default

Description

End_Day

float

0

3.40E+38

3.40E+38

The day to stop collecting data for the report.

Start_Day

float

0

3.40E+38

0

The day to start collecting data for the report.

 
{
    "Reports": [
        {
            "Start_Day": 372,
            "End_Day": 912,
            "class": "ReportVectorMigration"
        }
    ],
    "Use_Defaults": 1
}
Report structure and data channel descriptions

The file contains the following data channels:

Data channel

Data type

Description

Time

integer

The day that the vector migrated.

ID

integer

The ID of the vector or cohort. Note that when using the cohort model, a cohort may need to split such that some of the cohort migrates to the node and some do not, creating new cohort IDs. This may make it difficult to follow cohorts by ID.

FromNodeID

integer

The ID of the node that the vector was migrating from.

ToNodeID

integer

The ID of the node that the vector traveled to.

MigrationType

string

The method of migration used by the vector: local or regional.

Species

string

The name of the species of vector.

Age

integer

The number of days the vector has been alive.

Population

integer

The number of vectors that are migrating. If Vector_Sampling_Type is set to TRACK_ALL_VECTORS or SAMPLE_INDIVIDUAL_VECTORS this will be set to 1. If it is set to VECTOR_COMPARTMENTS_NUMBER or VECTOR_COMPARTMENTS_PERCENT then the number will be greater than one, and indicates that ‘X’ number of vectors are involved in that line of the report, with ‘X’ vectors of that age moving from node to node.
Example

The following is an example of ReportVectorMigration.csv:

Time

ID

FromNodeID

ToNodeID

Migration type

Species

Age

Population

365

7203794

25

24

local

Arabiensis

0

3

365

7203820

25

20

local

Arabiensis

0

7

365

7203846

25

19

local

Arabiensis

0

6

365

7203872

25

24

local

Arabiensis

1

7

365

7203898

25

20

local

Arabiensis

1

4

365

7203924

25

19

local

Arabiensis

1

8

365

7203950

25

24

local

Arabiensis

2

3

365

7203976

25

20

local

Arabiensis

2

7
ReportVectorStats

The vector statistics report (ReportVectorStats.csv) is a CSV-formatted report that provides detailed life-cycle data on the vectors in the simulation. The report is stratified by time, node ID, and (optionally) species.

Configuration

To generate this report, the following parameters must be configured in the custom_reports.json file:

Parameter

Data type

Min

Max

Default

Description

Species_List

array of strings

NA

NA

[ ]

The species for which to include information. If the list is empty or absent, then data for all species will be collected.

Stratify_By_Species

boolean

NA

NA

0

If set to true (1), then data will be stratified by species for each node.

Include_Wolbachia_Columns

boolean

NA

NA

0

If set to true (1), columns will be added for each type of Wolbachia. Summation of columns should be equal to VectorPopulation.

Include_Gestation_Columns

boolean

NA

NA

0

If set to true (1), columns will be added for feeding and gestation.

Include_Death_By_State_Columns

boolean

NA

NA

0

Adds columns for the number of vectors that died in this state during this time step as well as the average age at death. It adds two columns for each of the following states: ADULT, INFECTED, INFECTIOUS, and MALE.

 
{
    "Reports": [
        {
            "Species_List": [
                "arabiensis",
                "funestus"
            ],
            "Stratify_By_Species": 1,
            "Include_Wolbachia_Columns": 0,
            "Include_Gestation_Columns": 1,
            "class": "ReportVectorStats"
        }
    ],
    "Use_Defaults": 1
}
Output file data

The output report will contain the following information.

Stratification columns

Parameter

Data type

Description

Time

integer

The day of the simulation that the data was collected.

NodeID

integer

The External ID of the node that the data is being collected for.

Species

string

If Stratify_By_Species = 1, then the species column will contain the name of the species for the given row.
Data columns

Parameter

Data type

Description

Population

integer

The number of people in the node.

VectorPopulation

integer

The number of adult female vectors that are in the STATE_INFECTIOUS, STATE_INFECTED, and STATE_ADULT.

STATE_INFECTIOUS

integer

The number of adult female vectors that are infectious.

STATE_INFECTED

integer

The number of adult female vectors that are infected.

STATE_ADULT

integer

The number of adult female vectors.

STATE_MALE

integer

The number of adult male vectors.

STATE_IMMATURE

integer

The number of immature vectors, includes male and female.

STATE_LARVA

integer

The number of larva, includes male and female.

STATE_EGG

integer

The number of eggs, includes male and female.

NumLookingToFeed

integer

If Include_Gestation_Columns = 1, then this column contains the number of adult female vectors that were looking to feed during this time step.

NumFedCount

integer

If Include_Gestation_Columns = 1, then this column contains the number of adult female vectors that had a blood meal during this time step.

NumGestatingBegin

integer

If Include_Gestation_Columns = 1, then this column contains the number of adult female vectors that were gestating and did not die due to non-feeding mortality.

NumGestatingEnd

integer

If Include_Gestation_Columns = 1, then this column contains the number of adult female vectors that were gestating at the end of the time step.

NumAttemptFeedIndoor

integer

If Include_Gestation_Columns = 1, then this column contains the number of adult female vectors that attempt to feed indoors. This group of vectors can still die indoors before they feed on a human.

NumAttemptFeedOutdoor

integer

If Include_Gestation_Columns = 1, then this column contains the number of adult female vectors that attempt to feed outdoors. This group of vectors can still die outdoors before they feed on a human.

NumAttemptButNotFeed

integer

If Include_Gestation_Columns = 1, then this column contains the number of adult female vectors that were attempting to feed indoors or outdoors but did not die or feed on a human. They will look to feed during the next day.

NewEggsCount

integer

The number of eggs that were laid this day.

IndoorBitesCount

float

The number of bites made on humans indoors. This includes vectors that live for another day as well as those that die trying to get away.

IndoorBitesCountInfectious

float

The number of bites made by infectious vectors on humans indoors. This includes vectors that live for another day as well as those that die trying to get away.

OutdoorBitesCount

float

The number of bites made on humans outdoors. This includes vectors that live for another day as well as those that die trying to get away.

OutdoorBitesCountInfectious

float

The number of bites made by infectious vectors on humans outdoors. This includes vectors that live for another day as well as those that die trying to get away.

UnmatedAdults

float

The number of adult females that have not mated. This can be an important statistic when the male vector population is suppressed. When the male population is low, females moving from immature to adult might not find a male to mate with for several days. The female continues to feed but she does not produce fertile eggs.

NewAdults

integer

The number of female vectors that matured to adulthood this day.

DiedBeforeFeeding

integer

The number of vectors that died this time step due to local mortality, sugar feeding, outdoor area killing, etc.

DiedDuringFeedingIndoor

integer

This is the number of vectors that died indoors while attempting to feed (e.g. due to bed nets).

NumDiedInfectious

integer

If Include_Death_By_State_Columns = 1, then this column contains the number of infectious, mature, female vectors that died during this time step.

NumDiedInfected

integer

If Include_Death_By_State_Columns = 1, then this column contains the number of infected, mature, female vectors that died during this time step.

NumDiedAdults

integer

If Include_Death_By_State_Columns = 1, then this column contains the number of mature female vectors that are neither infected or infectious that died during this time step.

NumDiedMale

integer

If Include_Death_By_State_Columns = 1, then this column contains the number of mature male vectors that died during this time step.

AvgAgeAtDeathInfectious

float

If Include_Death_By_State_Columns = 1, then this column contains the average age (in days) of the the infectious, mature, female vectors that died during this time step.

AvgAgeAtDeathInfected

float

If Include_Death_By_State_Columns = 1, then this column contains the average age (in days) of the the infected, mature, female vectors that died during this time step.

AvgAgeAtDeathAdults

float

If Include_Death_By_State_Columns = 1, then this column contains the average age (in days) of the the mature, female vectors that are neither infected or infectious that died during this time step.

AvgAgeAtDeathMale

float

If Include_Death_By_State_Columns = 1, then this column contains the average age (in days) of the the mature male vectors that died during this time step.

NumGestatingOnDay_0

integer

If Include_Gestation_Columns = 1, then this column contains the number of vectors that are gestating but with 0 more days before attempting to feed.

NumGestatingOnDay_1

integer

If Include_Gestation_Columns = 1, then this column contains the number of vectors that are gestating but with 1 more days before attempting to feed.

NumGestatingOnDay_2

integer

If Include_Gestation_Columns = 1, then this column contains the number of vectors that are gestating but with 2 more days before attempting to feed.

NumGestatingOnDay_3

integer

If Include_Gestation_Columns = 1, then this column contains the number of vectors that are gestating but with 3 more days before attempting to feed.

NumGestatingOnDay_4

integer

If Include_Gestation_Columns = 1, then this column contains the number of vectors that are gestating but with 4 more days before attempting to feed.

NumGestatingOnDay_5

integer

If Include_Gestation_Columns = 1, then this column contains the number of vectors that are gestating but with 5 more days before attempting to feed.

NumGestatingOnDay_6

integer

If Include_Gestation_Columns = 1, then this column contains the number of vectors that are gestating but with 6 more days before attempting to feed.

NumGestatingOnDay_7

integer

If Include_Gestation_Columns = 1, then this column contains the number of vectors that are gestating but with 7 more days before attempting to feed.

VECTOR_WOLBACHIA_FREE

integer

If Include_Wolbachia_Columns = 1, then this column contains the number of adult female vectors that are Wolbachia free.

VECTOR_WOLBACHIA_A

integer

If Include_Wolbachia_Columns = 1, then this column contains the number of adult female vectors that have Wolbachia A.

VECTOR_WOLBACHIA_B

integer

If Include_Wolbachia_Columns = 1, then this column contains the number of adult female vectors that have Wolbachia B.

VECTOR_WOLBACHIA_AB

integer

If Include_Wolbachia_Columns = 1, then this column contains the number of adult female vectors that have Wolbachia AB.

MigrationFromCountLocal

integer

This is the number of adult female vectors that made a local migration trip away from this node.

MigrationFromCountRegiona

integer

This is the number of adult female vectors that made a regional migration trip away from this node.

XXX_AvailableHabitat

integer

If Stratify_By_Species = 0, then this column title does not have the species name in it. If Stratify_By_Species = 1, then there is a column for each species. This column contains the number of larva that the habitat could add (e.g. number of spots open); equal to current capacity - current larval count.

XXX_EggCrowdingCorrection

float

If Stratify_By_Species = 0, then this column title does not have the species name in it. If Stratify_By_Species = 1, then there is a column for each species. This column contains the probability that eggs die due to overcrowding.
Example

The following are examples of ReportVectorStats.csv files. The examples do not include the Death By State columns.

This example includes Wolbachia columns:

STATE_ADULT

STATE_MALE

STATE_IMMATURE

STATE_LARVA

STATE_EGG

NewEggsCount

IndoorBitesCount

IndoorBitesCount-Infectious

OutdoorBitesCount

OutdoorBitesCount-Infectious

NewAdults

UnmatedAdults

DiedBeforeFeeding

DiedDuringFeedingIndoor

DiedDuringFeedingOutdoor

VECTOR_WOLBACHIA_FREE

VECTOR_WOLBACHIA_A

VECTOR_WOLBACHIA_B

VECTOR_WOLBACHIA_AB

MigrationFromCountLocal

MigrationFromCountRegional

arabiensis_AvailableHabitat

funestus_AvailableHabitat

gambiae_AvailableHabitat

arabiensis_EggCrowdingCorrection

funestus_EggCrowdingCorrection

gambiae_EggCrowdingCorrection

NumInfectousBitesGiven

NumInfectousBitesReceived

InfectiousBitesGivenMinusReceived

16220

36576

17593

147016

1541720

770860

8537

202

2426

75

1469

0

2788

844

245

0

137427

0

0

0

0

8991.45

4123.95

10026.9 0.00706801

0.0259187

0.00710855

277

136

141

0

14635

34183

17098

152982

958640

479320

4756

479

1366

128

1047

0

2501

517

121

15087

0

0

0

0

0

3873.64

3814.74

4504.05

0.0133491

0.0316351

0.0135903

607

295

312

13247

32339

12849

150692

1264080

632040

5812

296

1559

92

1339

0

2287

643

159

66443

0

0

0

0

0

3662.45

2833.68

4034.73

0.00908111

0.0593457

0.00962815

388

219

169

12066

30731

14408

140976

1452320

726160

6700

270

1803

76

1564

0

2100

637

171

0

130007

0

0

0

0

8211.71

3889.12

9207.12

0.00671468

0.0242195

0.00670622

346

178

168

12590

30509

10898

145816

794440

397220

4239

455

1166

129

2672

0

1969

466

134

15105

0

0

0

0

0

2797.77

3512.56

3192.74

0.0129099

0.0288083

0.0135152

584

274

310

11243

28771

10887

138512

954720

477360

5676

312

1556

128

1134

0

1928

610

153

59130

0

0

0

0

6894.61

3493.81

7625.89

0.00798359

0.049113

0.00857158

440

227

213

0

8933

26075

14491

138662

1112920

556460

5247

329

1429

92

0

0

1884

604

131

0

136952

0

0

0

0

14562.2

4190.82

16253.6

0.0172538

0.0315782

0.0171569

421

240

181

9541

25515

9959

159705

705280

352640

2854

347

746

106

1965

0

1575

286

84

12354

0

0

0

0

0

1660.21

3321.22

2031.23

0.0308546

0.035052

0.031173

453

221

232

8604

24184

14841

143138

946320

473160

5224

418

1312

111

1070

0

1549

552

130

4545

0

0

0

0

0

5737.64

3498.29

6278.16

0.00562629

0.0425362

0.00611594

529

250

279

7951

22936

16030

140226

860880

430440

4290

310

1058

86

1048

0

1454

435

117

0

137234

0

0

0

0

3113.21

3527.66

3467.57

0.014121

0.0311236

0.0146823

396

208

188

8216

22242

15596

132199

463280

231640

2584

344

685

109

1552

0

1391

299

68

15421

0

0

0

0

0

1562.56

3622.39

1707.29

0.00863056

0.0325911

0.00884764

453

214

239

6553

20374

17428

122419

854320

427160

4512

421

1178

111

248

0

1482

490

140

63235

0

0

0

0

0

3282.25

3217.14

3811.39

0.00827453

0.0702558

0.00766011

532

263

269

8015

21173

12487

118951

683640

341820

3003

376

741

94

2646

0

1204

346

76

0

136025

0

0

0

0

2047.61

3347.56

2169.57

0.00896889

0.0306045

0.00994518

470

233

237

7913

20868

8126

115273

420600

210300

3489

299

927

70

1585

0

1298

357

82

14010

0

0

0

0

0

1894.64

2482.64

2145.04

0.00736232

0.0335831

0.0070938

369

172

197

7560

20286

8209

107152

734040

367020

4018

384

1079

109

1393

0

1290

449

102

59413

0

0

0

0

0

2474.93

3355.48

2673.81

0.0110462

0.046009

0.0109935

493

241

252

7525

19644

6876

103579

476880

238440

2978

375

665

90

1292

0

1279

323

61

0

125487

0

0

0

0

1868.86

3167.59

2158.96

0.00808167

0.0314773

0.00832445

465

218

247

This table includes gestation columns:

Time

NodeID

Population

VectorPopulation

STATE_INFECTIOUS

STATE_INFECTED

STATE_ADULT

STATE_MALE

STATE_IMMATURE

STATE_LARVA

STATE_EGG

NumLookingToFeed

NumFedCount

NumGestatingBegin

NumGestatingEnd

NumAttemptFeedIndoor

NumAttemptFeedOutdoor

NumAttemptButNotFeed

NewEggsCount

IndoorBitesCount

IndoorBitesCount-Infectious

OutdoorBitesCount

OutdoorBitesCount-Infectious

Unmated Adults

NewAdults

DiedBeforeFeeding

DiedDuringFeedingIndoor

DiedDuringFeedingOutdoor

NumGestatingOnDay_0

NumGestatingOnDay_1

NumGestatingOnDay_2

NumGestatingOnDay_3

NumGestatingOnDay_4

NumGestatingOnDay_5

NumGestatingOnDay_6

NumGestatingOnDay_7

MigrationFromCountLocal

MigrationFromCountRegional

arabiensis_AvailableHabitat

arabiensis_EggCrowdingCorrection

350

1

1000

88760

2171

16352

70237

100715

124862

1699008

1647000

29944

16393

48447

64840

8098

8154

11915

823500

8

1

8

0

0

12005

10422

820

816

9823

11150

12887

14587

16393

0

0

0

0

0

0

0

351

1

1000

88451

2127

16377

69947

100900

124843

1699680

1641840

29839

16278

48381

64659

8059

8021

11911

820920

8

0

8

1

0

11881

10540

848

802

9772

11285

12856

14468

16278

0

0

0

0

0

0

0

352

1

1000

88312

2110

16283

69919

100936

125391

1697598

1619120

29630

16020

48388

64408

7970

7859

12004

809560

7

2

7

2

0

11900

10433

803

803

9901

11347

12790

14350

16020

0

0

0

0

0

0

0

353

1

1000

88477

2117

16291

70069

101167

125234

1698069

1655960

29957

16285

48062

64347

8014

8127

12029

827980

8

1

8

0

0

12101

10293

807

836

9964

11265

12681

14152

16285

0

0

0

0

0

0

0

354

1

1000

88617

2154

16232

70231

101057

125605

1697983

1669440

30280

16509

47938

64447

8045

8214

12183

834720

8

0

8

0

0

11987

10259

790

798

9889

11158

12502

14389

16509

0

0

0

0

0

0

0

355

1

1000

88480

2154

16217

70109

101222

125573

1698350

1649840

30097

16393

47995

64388

8120

8083

12069

824920

8

0

8

1

0

12023

10525

813

822

9715

11030

12662

14588

16393

0

0

0

0

0

0

0
VectorHabitatReport

The vector habitat report is a JSON-formatted file containing the habitat data for each vector species included in the simulation. It focuses on statistics relevant to mosquito developmental stages (e.g. eggs and larvae), such as egg capacity and larval crowding.

The output file is a binned JSON report named VectorHabitatReport.json.

Configuration

You do not need to configure any parameters to generate this report.

Output file data
Channels

The channels section contains the following parameters:

Parameter

Data type

Description

<Channel_Title>

string

The title of the particular channel. Possible channels are:

Parameter

Data type

Description

Artificial Larval Mortality

float

‘The probability of the larva in the habitat being killed due to interventions (i.e. Larvicides).’

Current Habitat Capacity

integer

the number of larva that the habitat can currently hold.

Egg Crowding Factor

float

The probability that eggs die due to overcrowding.

Local Larval Growth Modifier

float

The local density-dependent hatching modifier which depends on larval crowding

Local Larval Mortality

float

‘The local larval mortality rate due to larval competition in the habitat. Note that mortality is relative to a species baseline (1.0) and intermediate larval age (0.5).’

Rainfall Larval Mortality

float

The rate at which larvae are dying due to rainfall.

Total Larva

integer

The total number of larvae of that species in that habitat during that time step.

Units

string

The units used for this channel.

Data

array

A two-dimensional array of the channel data at each time step where the outer dimension is for each species:habitat and the inner dimension is for each time step.
Example 
{
    "Header": {
        "DateTime": "Fri May 17 15:33:03 2019",
        "DTK_Version": "4148 Malaria-Ongoing (bb265ad) May 16 2019 09:39:35",
        "Report_Version": "2.1",
        "Timesteps": 5,
        "Subchannel_Metadata": {
            "AxisLabels": [
                [
                    "Species:Habitat"
                ]
            ],
            "NumBinsPerAxis": [
                [
                    3
                ]
            ],
            "ValuesPerAxis": [
                [
                    [
                        0,
                        0,
                        0
                    ]
                ]
            ],
            "MeaningPerAxis": [
                [
                    [
                        "gambiae:TEMPORARY_RAINFALL",
                        "funestus:WATER_VEGETATION",
                        "arabiensis:TEMPORARY_RAINFALL"
                    ]
                ]
            ]
        },
        "Channels": 7
    },
    "Channels": {
        "Artificial Larval Mortality": {
            "Units": "",
            "Data": [
                [
                    0,
                    0,
                    0,
                    0,
                    0
                ],
                [
                    0,
                    0,
                    0,
                    0,
                    0
                ],
                [
                    0,
                    0,
                    0,
                    0,
                    0
                ]
            ]
        },
        "Current Habitat Capacity": {
            "Units": "",
            "Data": [
                [
                    402.5242614746,
                    469.3384094238,
                    482.3938293457,
                    730.068359375,
                    718.5045776367
                ],
                [
                    195.2786865234,
                    230.9520568848,
                    242.8811035156,
                    376.0362548828,
                    384.9170837402
                ],
                [
                    3622.71875,
                    4224.045898438,
                    4341.544433594,
                    6570.615234375,
                    6466.541503906
                ]
            ]
        },
        "Egg Crowding Factor": {
            "Units": "",
            "Data": [
                [
                    1,
                    0.1548170298338,
                    0.01222433708608,
                    0.03274614363909,
                    0.212905973196
                ],
                [
                    1,
                    0.1394847780466,
                    0.03747003525496,
                    0.0134865026921,
                    0.1550362557173
                ],
                [
                    1,
                    1,
                    1,
                    1,
                    1
                ]
            ]
        },
        "Local Larval Growth Modifier": {
            "Units": "",
            "Data": [
                [
                    1,
                    1,
                    1,
                    1,
                    1
                ],
                [
                    1,
                    1,
                    1,
                    1,
                    1
                ],
                [
                    1,
                    1,
                    1,
                    1,
                    1
                ]
            ]
        },
        "Local Larval Mortality": {
            "Units": "",
            "Data": [
                [
                    1,
                    1,
                    1,
                    1,
                    1
                ],
                [
                    1,
                    1,
                    1,
                    1,
                    1
                ],
                [
                    1,
                    1,
                    1,
                    1,
                    1
                ]
            ]
        },
        "Rainfall Larval Mortality": {
            "Units": "",
            "Data": [
                [
                    0,
                    0,
                    0,
                    0,
                    0
                ],
                [
                    0,
                    0,
                    0,
                    0,
                    0
                ],
                [
                    0,
                    0,
                    0,
                    0,
                    0
                ]
            ]
        },
        "Total Larva": {
            "Units": "",
            "Data": [
                [
                    0,
                    440,
                    430,
                    432,
                    721
                ],
                [
                    0,
                    171,
                    224,
                    221,
                    334
                ],
                [
                    0,
                    1600,
                    2840,
                    3978,
                    3987
                ]
            ]
        }
    }
}
VectorSpeciesReport

The vector species output report (VectorSpeciesReport.json) is a JSON-formatted file where the channel data has been sorted into bins. It is identical to the BinnedReport, with the exception that the bins are based on vector species, and provides the average number of adult vectors per node for each species. The vector species report is generated for all malaria or vector contain values in the Vector_Species_Params configuration parameter. For example, if Vector_Species_Params contained “funestus” and “gambiae”, you could be able to see the average number of female vectors per node for both A. funestus and A. gambiae.

To generate this report, you must set Enable_Vector_Species_Report to 1 in the config.json file.

Channels

The channels section contains the following parameters.

Parameter

Data type

Description

<Channel_Title>

string

The title of the particular channel.

Units

string

The units used for this channel.

Data

array

A list of the channel data at each timestep.

The data channels included are:

Data channel

Description

Adult Vectors Per Node

The average number of adult female vectors in each node on each day for each species.

Fraction Infectious Vectors

The fraction of adult female vectors that are infected and infectious.

Daily EIR

The entomological inoculation rate (EIR), or the number of infected mosquito bites each individual receives each day, on average.

Daily HBR

The average number of mosquito bites received per individual per day.

Fraction Vectors Died Before Feeding

The fraction of adult female vectors that die while attempting to feed indoors. This includes death before feeding (e.g. killed by a bednet), during feeding (e.g. squished = Human_Feeding_Mortality), and after feeding (e.g. landing on an IRS wall).

Fraction Vectors Died During Outdoor Feeding

The fraction of vectors that die while attempting to feed outdoors. The causes are typically due to individual-level outdoor interventions.
Example

The following is a sample of an VectorSpeciesReport.json file.

{
    "Header": {
        "DateTime": "Mon Dec 21 11:20:38 2020",
        "DTK_Version": "Malaria-Ongoing (bb265ad) Dec 21 2020 11:15:31",
        "Report_Version": "2.1",
        "Timesteps": 180,
        "Subchannel_Metadata": {
            "AxisLabels": [
                [
                    "Vector Species"
                ]
            ],
            "NumBinsPerAxis": [
                [
                    3
                ]
            ],
            "ValuesPerAxis": [
                [
                    [
                        0,
                        0,
                        0
                    ]
                ]
            ],
            "MeaningPerAxis": [
                [
                    [
                        "arabiensis",
                        "funestus",
                        "gambiae"
                    ]
                ]
            ]
        },
        "Channels": 7
    },
    "Channels": {
        "Adult Vectors Per Node": {
            "Units": "",
            "Data": [
                [
                    8795,
                    7720,
                    6735
                ]
            ]
        },
        "Daily EIR": {
            "Units": "",
            "Data": [
                [
                    0.00400000018999,
                    0.02800000086427,
                    0.835000038147
                ]
            ]
        },
        "Daily HBR": {
            "Units": "",
            "Data": [
                [
                    2.785000085831,
                    8.515999794006,
                    3.394000053406
                ]
            ]
        },
        "Percent Infectious Vectors": {
            "Units": "",
            "Data": [
                [
                    0.04697532951832,
                    0.18621134758,
                    0.2092223018408
                ]
            ]
        },
        "Percent Vectors Died Before Feeding": {
            "Units": "",
            "Data": [
                [
                    0.09339272230864,
                    0.1022477596998,
                    0.09913764148951
                ]
            ]
        },
        "Percent Vectors Died During Indoor Feeding": {
            "Units": "",
            "Data": [
                [
                    0.03303108736873,
                    0.0264107119292,
                    0.01262986380607
                ]
            ]
        },
        "Percent Vectors Died During Outdoor Feeding": {
            "Units": "",
            "Data": [
                [
                    0.01304347813129,
                    0.01427712664008,
                    0.03279281407595
                ]
            ]
        }
    }
}
MalariaImmunityReport

The malaria immunity report (MalariaImmunityReport.json) is a JSON-formatted file that provides statistics for several antibody types for specified age bins over a specified reporting duration. Specifically, the report tracks the average and standard deviation in the fraction of observed antibodies for merozoite surface protein (MSP), Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1), and non-specific (and less immunogenic) minor surface epitopes. The total amount possible is determined by the parameters Falciparum_MSP_Variants, Falciparum_PfEMP1_Variants, and Falciparum_Nonspecific_Types. The greater the fraction, the more antibodies the individual has against possible new infections. The smaller the fraction, the more naive the individual’s immune system is to malaria.

Configuration

To generate this report, the following parameters must be configured in the custom_reports.json file:

Parameter name

Data type

Min

Max

Default

Description

Filename_Suffix

string

NA

NA

(empty string)

Augments the filename of the report. If multiple reports are being generated, this allows you to distinguish among the multiple reports.

Start_Day

float

0

3.40282e+38

0

The day of the simulation to start collecting data.

End_Day

float

0

3.40282e+38

3.40282e+38

The day of the simulation to stop collecting data.

Node_IDs_Of_Interest

array of integers

0

2.14748e+09

[]

Data will be collected for the nodes in this list. Empty list implies all nodes.

Reporting_Interval

integer

1

1000000

1000000

Defines the cadence of the report by specifying how many time steps to collect data before writing to the file. This will limit system memory usage and is advised when large output files are expected.

Max_Number_Reports

integer

0

1000000

1

The maximum number of report output files that will be produced for a given campaign.

Pretty_Format

boolean

0

1

0

True (1) sets pretty JSON formatting. The default, false (0), saves space.

Age_Bins

array of integers

0

125

[ ]

The age bins (in years, in ascending order) to aggregate within and report. An empty array does not stratify by age.

 
{
    "Reports": [
        {
            "class": "MalariaImmunityReport",
            "Age_Bins": [
                10,
                20,
                30,
                40,
                50,
                60,
                70,
                80,
                90,
                100,
                125
            ],
            "Start_Day": 365,
            "End_Day": 465,
            "Filename_Suffix": "Node1",
            "Node_IDs_Of_Interest": [ 1 ],
            "Reporting_Interval": 10,
            "Max_Number_Reports": 1,
            "Pretty_Format": 1
        }
    ],
    "Use_Defaults": 1
}
Output file data

The report contains the following output data.

Parameter name

Data type

Description

Age Bins

array of integers

The array of age bins used in the report (in years).

MSP Mean by Age Bin

float

The average fraction of merozoite surface protein (MSP) antibodies over the total possible (Falciparum_MSP_Variants) an individual has for each age bin for each reporting interval.

Non-Specific Mean by Age Bin

float

The average fraction of less-immunogenic minor surface epitopes (non-specific) an individual has developed antibodies for over the total possible (Falciparum_Nonspecific_Types) for each age bin for each reporting interval.

PfEMP1 Mean by Age Bin

float

The average fraction of Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) antigens for which an individual has developed antibodies for over the total possible (Falciparum_PfEMP1_Variants) for each age bin for each reporting interval.

MSP StdDev by Age Bin

float

TThe standard deviation for the fraction of merozoite surface protein (MSP) seen by an individual for each age bin for each reporting interval.

Non-Specific StdDev by Age Bin

float

The standard deviation for the fraction of less-immunogenic minor surface epitopes (non-specific) seen by an individual for each age bin for each reporting interval.

PfEMP1 StdDev by Age Bin

float

The standard deviation for the fraction of Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) seen by the individual for each age bin for each reporting interval.
Example

The following is an example of MalariaImmunityReport.json. Notice there are five age bins and two reporting intervals.

{
    "Age Bins": [
        25,
        50,
        75,
        100,
        125
    ],
    "MSP Mean by Age Bin": [
        [
            0.8632667310552,
            0.8663234088753,
            0.8725000023842,
            0.857499986887,
            0.8826284248331
        ],
        [
            0.8624046362191,
            0.8712405961259,
            0.873493152122,
            0.857499986887,
            0.8831850525747
        ]
    ],
    "Non-Specific Mean by Age Bin": [
        [
            0.7913650278989,
            0.9880864695119,
            0.9972526126973,
            1,
            1
        ],
        [
            0.7751695395588,
            0.9881515987902,
            0.9972555594525,
            1,
            1
        ]
    ],
    "PfEMP1 Mean by Age Bin": [
        [
            0.9790955765201,
            0.979491552852,
            0.9802499935031,
            0.9786335564639,
            0.9819375015795
        ],
        [
            0.9790281954887,
            0.979908272019,
            0.9804023946189,
            0.9790821977674,
            0.9818309637788
        ]
    ],
    "MSP StdDev by Age Bin": [
        [
            0.03146989378593,
            0.03586982024275,
            0.01299036866778,
            0.02046338790071,
            0.02644183223304
        ],
        [
            0.03046207382119,
            0.036680426133,
            0.01342621718435,
            0.02046338790071,
            0.02496969378762
        ]
    ],
    "Non-Specific StdDev by Age Bin": [
        [
            0.2602374122976,
            0.01315735830347,
            0.00446382233485,
            0,
            0
        ],
        [
            0.2789481711119,
            0.01310852104812,
            0.004462334073195,
            0,
            0
        ]
    ],
    "PfEMP1 StdDev by Age Bin": [
        [
            0.005103897532884,
            0.005738237822243,
            0.002277617507036,
            0.002825865931333,
            0.004145300627574
        ],
        [
            0.005032654177563,
            0.005761350326143,
            0.002171759356921,
            0.002869313618544,
            0.004228758281701
        ]
    ]
}
MalariaPatientJSONReport

The malaria patient data report (MalariaPatientJSONReport.json) is a JSON-formatted report that provides medical data for each individual on each day of the simulation. For example, for a specified number of time steps, each “patient” will have information collected on the temperature of their fever, their parasite counts, treatments they received, and other relevant data.

Configuration

To generate this report, the following parameters must be configured in the custom_reports.json file:

Parameter

Data type

Min

Max

Default

Description

Filename_Suffix

string

NA

NA

(empty string)

Augments the filename of the report. If multiple reports are being generated, this allows you to distinguish among the multiple reports.

Start_Day

float

0

3.40282e+38

0

The day of the simulation to start collecting data.

End_Day

float

0

3.40282e+38

3.40282e+38

The day of the simulation to stop collecting data.

Node_IDs_Of_Interest

array of integers

0

2.14748e+09

[]

Data will be collected for the nodes in this list. Empty list implies all nodes.

Min_Age_Years

float

0

9.3228e+35

0

Minimum age in years of people to collect data on.

Max_Age_Years

float

0

9.3228e+35

9.3228e+35

Maximum age in years of people to collect data on.

Must_Have_IP_Key_Value

string

NA

NA

(empty string)

A Key:Value pair that the individual must have in order to be included. Empty string means to not include IPs in the selection criteria.

Must_Have_Intervention

string

NA

NA

(empty string)

The name of the intervention that the person must have in order to be included. Empty string means to not include interventions in the selection criteria.

 
{
    "Reports": [
        {
            "class": "MalariaPatientJSONReport",
            "Filename_Suffix": "Node1",
            "Start_Day": 365,
            "End_Day": 465,
            "Node_IDs_Of_Interest": [ 1 ],
            "Min_Age_Years": 5,
            "Max_Age_Years": 10,
            "Must_Have_IP_Key_Value": "Risk:LOW",
            "Must_Have_Intervention": "UsageDependentBednet"
        }
    ],
    "Use_Defaults": 1
}
Output file data

The report contains the following information:

Parameter

Data type

Description

ntsteps

integer

Number of time steps in the simulation in which the report is active. Each channel for each person has this number of elements People that are born after the start of the simulation or die before the end will have fewer entries.

patient_array

array of JSON objects

For each patient in patient_array, there will be a dictionary of key:value pairs. Some dictionary entries will contain a single, constant value that does not change through time, such as the individual’s birthday. Other dictionary entries will be arrays of daily measures for that individual, such as the count of count of asexual parasites on each day of the simulation. Please see the example below for an illustration.
Patient data channels

Each of the following statistics has one value for each timestep or day.

Parameter

Data type

Description

id

string

The unique ID of the individual.

initial_age

float

The initial age of the person, in days, when the report started tracking them. This value will be non-zero for individuals created at initialization, but should be zero for the rest of the population.

birthday

float

The day that the individual was born/created in relation to the start of the report.
Patient array data channels

Each of the following statistics is an array of data, where each entry is the value of that field at the time of the event.

Parameter

Data type

Description

asexual_parasites

integer

The results of the BLOOD_SMEAR_PARASITES diagnostic that reports the number of parasites detected.

asexual_positive_fields

integer

The count of parasites using positive slide fields.

gametocyte_positive_fields

integer

The count of gametocytes using positive slide fields.

gametocytes

integer

The results of the BLOOD_SMEAR_GAMETOCYTES diagnostic that reports the number of gametocytes detected.

hemoglobin

float

The amount of hemoglobin the individual has based on their red blood cell count.

infected_mosquito_fraction

float

The fraction of mosquito bites the patient received from infectious mosquitoes.

temps

float

The individual’s body temperature, in Celsius, when they had a fever. It is -1 if they do not have a fever. This is based on their cytokine level.

treatment

string

The list of drug names that the user is currently taking, which includes any drugs in the person’s system during that timestep. The names are separated by ‘space+space.’

true_asexual_parasites

float

The actual number of parasites that the individual has.

true_gametocytes

float

The actual number of gametocytes that the individual has.
Example

The following is an example of MalariaPatientReport.json.

{
    "ntsteps": 5,
    "patient_array": [{
        "asexual_parasites": [
            [
                0,
                30,
                30,
                730,
                570

            ]
        ],
        "asexual_positive_fields": [
            [
                0,
                21,
                19,
                154,
                147
            ]
        ],
        "birthday": -3031.024658203,
        "gametocyte_positive_fields": [
            [
                0,
                0,
                0,
                0,
                1
            ]
        ],
        "gametocytes": [
            [
                0,
                0,
                0,
                0,
                10
            ]
        ],
        "hemoglobin": [
            [
                13.95295619965,
                13.94942092896,
                13.94591999054,
                13.94245052338,
                13.93901348114
            ]
        ],
        "id": 1,
        "infected_mosquito_fraction": [
            [
                0,
                0.02632671967149,
                0.1359317451715,
                0.3907080292702,
                2.00230383873
            ]
        ],
        "initial_age": 3031.024658203,
        "temps": [
            [
                -1,
                38.39496612549,
                40.47192382813,
                40.53329849243,
                43.32601165771
            ]
        ],
        "treatment": [
            [
                "",
                "",
                "",
                "",
                ""
            ]
        ],
        "true_asexual_parasites": [
            [
                0,
                0.01264353003353,
                0.2007576525211,
                2.86963891983,
                41.02872848511
            ]
        ],
        "true_gametocytes": [
            [
                0,
                0.3181047737598,
                4.729532718658,
                67.70833587646,
                967.8447875977
            ]
        ]
    }]
}
MalariaSQLReport

The MalariaSQL report outputs epidemiological and transmission data. Because of the quantity and complexity of the data, the report output is a multi-table SQLite relational database (see DB Browser for SQLite for more information). Use the configuration parameters to manage the size of the database.

Configuration

To generate this report, configure the following parameters in the custom_report.json file:

Parameter

Data type

Min

Max

Default

Description

Include_Drug_Status_Table

boolean

NA

NA

0

A true value (1) includes the table that provides data at each time step for each drug the person has.

Include_Health_Table

boolean

NA

NA

1

A true value (1) includes the Health table which has data for each time step for the health of an individual.

Include_Infection_Data_Table

boolean

NA

NA

1

A true value (1) includes the InfectionData table which has data for each time step for each active infection.

End_Day

float

0

3.40E+38

3.40E+38

The day to stop collecting data for the report.

Start_Day

float

0

3.40E+38

0

The day to start distributing interventions.

 
{
    "Reports": [
        {
            "Include_Drug_Status_Table": 1,
            "Include_Health_Table": 1,
            "Include_Infection_Data_Table": 1,
            "Start_Day": 100,
            "End_Day": 900,
            "class": "MalariaSQLReport"
        }
    ],
    "Use_Defaults": 1
}
SQL database and table structures

Because the output is a relational database, most of the tables have a primary key (highlighted in yellow on the figure below) that is a combination of RunNumber and another value. For example, in the Humans table, the primary key is the combination of RunNumber and HumanID. Because the RunNumber is part of the primary key, you can combine data from multiple runs.

The following chart and tables describe the SQL data tables produced by this report. The “many” and “1” notations in the chart show you which tables have a one-to-many row relationship and which have a many-to-one row relationship with the rows in another table.

_images/malaria-sql-report.png

Schematic of the SQL data tables produced by this report

Humans table

The Humans table tracks of all the humans in the simulation. It contains one row for each individual in the scenario. This table has a one-to-many relationship with the Infections and Health tables.

ColumnName

Data type

Description

RunNumber

integer

This value from the Run_Number parameter is the seed to the model’s random number generator.

HumanID

integer

The unique ID of the individual in the simulation.

Gender

text

The gender of the individual. Possible values are M or F.

HomeNodeID

integer

The external ID (NodeID in demographics) of the home node where the individual starts out.

InitialAgeDays

float

The age, in days, of the individual when they entered the simulation.

SimTimeAdded

float

The time in the simulation when the individual was added.
Health table

The Health table tracks the health of each individual while they are alive in the simulation. There is one record for each timestep during which an individual was alive. This table has a many-to-one relationship with the Humans table. Query the SevereCaseType table if you want to translate the integer value of ServerCaseTypeID to a text value.

ColumnName

Data type

Description

RunNumber

integer

This value from the Run_Number parameter is the seed to the model’s random number generator.

HumanID

integer

The unique ID of the individual in the simulation.

NodeID

integer

The external ID of the node the individual is in at this time.

SimTime

float

The time in the simulation that this data was collected.

Infectiousness

float

An infectiousness value based on the individual’s number of mature female gametocytes and fever status.

RelativeBitingRate

float

A number indicating if this individual is more likely to be bitten by mosquitoes. This can include any biting rates you set and/or a value based on the age/area of the individual.

IsClinicalCase

boolean

A value of true (1) means that the individual has a fever greater than both Clinical_Fever_Threshold_Low and Clinical_Fever_Threshold_High.

IsSevereCase

boolean

A value of true (1) means the individual has a fever greater than Clinical_Fever_Threshold_Low and was probabilistically determined to have severe disease.

SevereCaseTypeID

integer

The unique ID of the type of severe disease.

InvMicrolitersBlood

float

The inverse amount of blood the individual has in microliters.

RedBloodCellCount

float

The actual number of red blood cells the individual has.

Cytokines

float

The number of cytokines that have been stimulated by the presence of all malaria parasites.

HRP2

float

The amount of Histidine Rich Protein 2 in the individual’s blood stream.

PfEMP1VariantFraction

float

The number of PfEMP1 antibodies that an individual has divided by the total number possible in the scenario (Falciparum_PfEMP1_Variants).
SevereCaseType table

This is a small, lookup table for translating the integer value of SevereCaseTypeID in the Health table into the actual name of the case type. The SevereCaseType names are based on the SevereCaseTypesEnum parameter.

ColumnName

Data type

Description

SevereCaseTypeID

integer

The unique ID of the severe case type.

Name

enum

The name of the cause of the sever disease. Possible values are NONE, ANEMIA, PARASITES, or FEVER.
Infections table

The Infections table tracks the infections that occurred in humans during the simulation. There is one row for each infection that occurred. This table has a many-to-one relationship with the Infections and ParasiteGenomes tables.

ColumnName

Data type

Description

RunNumber

integer

This value from the Run_Number parameter is the seed to the model’s random number generator.

InfectionID

integer

The unique ID of the infection in the simulation.

HumanID

integer

The unique ID of the individual in the simulation.

GenomeID

integer

The unique ID of the genome derived from a hash code function on the nucleotide sequence of the genome.

SimTimeCreated

float

The time in the simulation that the infection was created.
InfectionData table

The InfectionData table contains data about each infection while it was active in the simulation. This table has a one-to-many relationship with the Infections table.

ColumnName

Data type

Description

RunNumber

integer

This value from the Run_Number parameter is the seed to the model’s random number generator.

InfectionID

integer

The unique ID of the infection in the simulation.

SimTime

float

The time in the simulation that this data was collected.

InfectedRedBloodCells

integer

The number of infected red blood cells due to this infection at this time.

NumMatureGametocytesFemale

integer

The number of mature female gametocytes in this infection at this time.

NumMatureGametocytesMale

integer

The number of mature male gametocytes in this infection at this time.
ParasiteGenomes table

The ParasiteGenomes table contains one row of information about each unique parasite genome. This table has a one-to many relationship with the GenomeSequenceData and Infections tables.

ColumnName

Data type

Description

RunNumber

integer

This value from the Run_Number parameter is the seed to the model’s random number generator.

GenomeID

integer

The unique ID of the genome derived from a hash code function on the nucleotide sequence of the genome.

BarcodeID

integer

The unique ID/hash of the barcode portion of the genome, based just on the barcode. It does not include the allele roots.

Barcode

string

The text representation of the barcode where each allele is A, C, G, or T.
DrugStatus table

The DrugStatus table keeps track of the status of each drug in a person. Each person may have multiple records per time step, so the table may be large. Information on each drug will continue until the drug has expired.

ColumnName

Data type

Description

RunNumber

integer

This value from the Run_Number parameter is the seed to the model’s random number generator.

HumanID

integer

The unique ID of the person in the simulation.

SimTime

float

The time in the simulation that the data is being collected.

DrugName

string

The name of the drug indicated in the intervention as defined by the configuration parameter Malaria_Drug_Params. Depending on the intervention, this may be a concatenated value when a person takes multiple pills in one dose.

CurrentEfficacy

float

The current efficacy of the drug(s).

NumRemainingDoses

integer

The number of doses that the person has yet to take. Depending on the intervention, the dose may involve multiple pills.
GenomeSequenceData table

This table contains the sequence and allele root information for each genome. The RunNumber and GenomeID indicate the particular genome, and the GenomeLocation indicates the location of the values. The number of rows for a particular genome depend on the number of barcode positions and whether or not the simulation tracked the MSP and PfEMP1 variants in the genome. This table has a many-to-one relationship with the ParasiteGenomes and Locations tables.

ColumnName

Data type

Description

RunNumber

integer

This value from the Run_Number parameter is the seed to the model’s random number generator.

GenomeID

integer

The unique ID of the genome derived from a hash code function on the nucleotide sequence of the genome.

NucleotideSequence

integer

The value of the allele at the genome location.

AlleleRoots

integer

the Infection ID from where this allele originated.

GenomeLocation

integer

The location in the genome, which is a value from 1 to 22790000.
Locations table

The Locations table tracks what type of data is contained in a particular genome location. This table has a one-to-many relationship with the GenomeSequenceData table and a many-to-one relationship with the LocationType lookup table.

ColumnName

Data type

Description

RunNumber

integer

This value from the Run_Number parameter is the seed to the model’s random number generator.

GenomeLocation

integer

The location in the genome, which is a value from 1 to 22790000.

LocationTypeID

integer

The unique ID of the location type, which indicates if the location is used in the Barcode, Drug Resistance, HRPstatus, MSP, or Major Epitopes. Query the LocationType table to obtain the actual location name.
LocationType table

This is a small lookup table that provides the full name of the location (via the keys in the Locations and GenomeSequenceData tables) used when querying the ParasiteGenomes table.

ColumnName

Data type

Description

LocationTypeID

integer

The unique ID of the location type.

Name

enum

The name of the location. Possible values are BARCODE, DRUG_RESISTANCE, HRP_STATUS, MPS, or PFEMP1_VARIANTS.
MalariaSummaryReport

The malaria summary report (MalariaSummaryReport.json) is a JSON-formatted report that provides a population-level summary of malaria data grouped into different bins such as age, parasitemia, and infectiousness.

Configuration

To generate this report, the following parameters must be configured in the custom_reports.json file:

Parameter

Data type

Min

Max

Default

Description

Filename_Suffix

string

NA

NA

(empty string)

Augments the filename of the report. If multiple reports are being generated, this allows you to distinguish among the multiple reports.

Start_Day

float

0

3.40282e+38

0

The day of the simulation to start collecting data.

End_Day

float

0

3.40282e+38

3.40282e+38

The day of the simulation to stop collecting data.

Node_IDs_Of_Interest

array of integers

0

2.14748e+09

[]

Data will be collected for the nodes in this list. Empty list implies all nodes.

Must_Have_IP_Key_Value

string

NA

NA

(empty string)

A Key:Value pair that the individual must have in order to be included. Empty string means to not include IPs in the selection criteria.

Must_Have_Intervention

string

NA

NA

(empty string)

The name of the intervention that the person must have in order to be included. Empty string means to not include interventions in the selection criteria.

Reporting_Interval

integer

1

1000000

1000000

Defines the cadence of the report by specifying how many time steps to collect data before writing to the file. This will limit system memory usage and is advised when large output files are expected.

Max_Number_Reports

integer

0

1000000

1

The maximum number of report output files that will be produced for a given campaign per simulation.

Pretty_Format

boolean

0

1

0

True (1) sets pretty JSON formatting. The default, false (0), saves space.

Age_Bins

array of floats

0

125

[10,20,30,40,50,60,70,80,90,100,1000]

The age bins to aggregate within and report. Data must be in ascending order.

Parasitemia_Bins

float

-3.40E+38

3.40E+38

[50,500,5000,50000,FLT_MAX]

Parasitemia Bins to aggregate within and report. A value greater than or equal to zero in the first bin indicates that the uninfected people should be added to this bin. The values must be in ascending order.

Infectiousness_Bins

float

-3.40E+38

3.40E+38

[20,40,60,80,100]

Infectiousness bins to aggregate within and report.

Individual_Property_Filter

string

NA

NA

(empty string)

The individual ‘property:value’ to filter on. The default of an empty string means the report is not filtered. For example: ‘Risk:High’.

 
{
    "Reports": [
        {
            "class": "MalariaSummaryReport",
            "Filename_Suffix": "Node1",
            "Start_Day": 365,
            "End_Day": 465,
            "Node_IDs_Of_Interest": [ 1 ],
            "Must_Have_IP_Key_Value": "Risk:LOW",
            "Must_Have_Intervention": "UsageDependentBednet"
            "Reporting_Interval": 10,
            "Max_Number_Reports": 1,
            "Pretty_Format": 1,
            "Age_Bins": [
                10,
                20,
                30,
                40,
                50,
                60,
                70,
                80,
                90,
                100,
                125
            ],
            "Parasitemia_Bins": [
                50,
                500,
                5000,
                50000
            ],
            "Infectiousness_Bins": [
                20,
                40,
                60,
                80,
                100
            ],
            "Individual_Property_Filter": "Risk:High"
        }
    ],
    "Use_Defaults": 1
}
Output file data

These output files may be very large. They contain several sections of data:

  • Metadata

  • DataByTime

  • DataByTimeAndAgeBins

  • DataByTimeAndPfPRBinsAndAgeBins

  • DataByTimeAndInfectiousnessBinsAndPfPRBinsAndAgeBins

Metadata

This section contains the group of parameters used to configure the report, and includes the following data channels:

Parameter

Data type

Description

Start_Day

integer

The first day that the first interval started.

Reporting_Interval

integer

The number of days to accumulate data.

Age Bins

array of integers

The max age in years per bin, listed in ascending order. Note that by using a large value for the last bin, it will collect all remaining people.

Parasitemia Bins

array of integers

The max parasite density in infected red blood cells per microliter per bin, listed in ascending order.

Gametocytemia Bins

array of integers

The max gametocyte density in infected red blood cells per microliter per bin, listed in ascending order.

Infectiousness Bins

array of integers

The max percent infectiousness of each bin, listed in ascending order.
DataByTime

This section contains a group of statistics where there is just one entry for each reporting interval. The following statistics are collected:

Parameter

Description

Time Of Report

Each entry is the final day of the reporting interval, in days.

Annual EIR

The average Entomological Inoculation Rate (EIR) per year over the reporting interval.

PfPR_2to10

The fraction of individuals whose age is 2 < age < 10 that would have been detected with the BLOOD_SMEAR_PARASITES diagnostic type of MalariaDiagnostic where the sensitivity is Report_Parasites_Smear_Sensitivity and the detection threshold is Report_Detection_Threshold_Blood_Smear_Parasites.

No Infection Streak

The maximum number of days without an infection during the interval.

Fraction Days Under 1pct Infected

The percentage of days during the interval in which the percentage of infected individuals was less than 1%.
DataByTimeAndAgeBins

This section contains statistics in two-dimensional arrays by Time (the reporting interval) and Age Bin.

Parameter

Description

PfPR by Age Bin

The fraction of individuals in this age bin that would have been detected using the BLOOD_SMEAR_PARASITES diagnostic type of the MalariaDiagnostic intervention where the sensitivity is Report_Parasites_Smear_Sensitivity and the detection threshold is Report_Detection_Threshold_Blood_Smear_Parasites.

pf Gametocyte Prevalence by Age Bin

The fraction of individuals in this age bin that would have been detected using the BLOOD_SMEAR_GAMETOCYTES diagnostic type of the MalariaDiagnostic intervention where the sensitivity is Report_Gametocyte_Smear_Sensitivity and the detection threshold is Report_Detection_Threshold_Blood_Smear_Gametocytes.

Mean Log Parasite Density by Age Bin

The average Log10 parasite density of the population for that age bin based on the count of parasites using the BLOOD_SMEAR_PARASITES diagnostic type of MalariaDiagnostic where the sensitivity is Report_Parasites_Smear_Sensitivity.

New Infections by Age Bin

The number of new infections during the reporting interval for each age bin.

Annual Clinical Incidence by Age Bin

The number of new clinical symptoms per person per year. This channel is controlled by the Clinical_Fever_Threshold_Low and Clinical_Fever_Threshold_High parameters. The amount that an individual’s fever is above normal must be greater than both of these values to be considered clinical. This can also be influenced by the Min_Days_Between_Clinical_Incidents parameter.

Annual Severe Incidence by Age Bin

The number of new severe symptoms per person per year. An individual is considered to be a severe case if the combined probability of anemia, parasite density, and fever is greater than a uniform random number. This combined probability is the combination of sigmoid using the following parameters: Anemia_Severe_Threshold and Anemia_Severe_Inverse_Width, Parasite_Severe_Threshold and Parasite_Severe_Inverse_Width, Fever_Severe_Threshold and Fever_Severe_Inverse_Width.

Average Population by Age Bin

The average population of the people in the given age bin over the reporting internal/period.

Annual Severe Incidence by Anemia by Age Bin

The number of severe incidences by anemia per person per year. The sum of the people who have severe symptoms of type: anemia during each timestep during the reporting interval multiplied by 365 and divided by the sum of the people in the age bin during each timestep during the reporting interval. Impacted by Anemia_Severe_Threshold and Anemia_Severe_Inverse_Width.

Annual Severe Incidence by Parasites by Age Bin

The number of severe incidences by parasites per person per year. The sum of the people who have severe symptoms of type: parasites during each timestep during the reporting interval multiplied by 365 and divided by the sum of the people in the age bin during each time step during the reporting interval. Impacted by Parasite_Severe_Threshold and Parasite_Severe_Inverse_Width.

Annual Severe Incidence by Fever by Age Bin

The number of severe incidences by fever per person per year. The sum of the people who have severe symptoms of type fever during each timestep during the reporting interval multiplied by 365 and divided by the sum of the people in the age bin during each time step during the reporting interval. Impacted by Fever_Severe_Threshold and Fever_Severe_Inverse_Width.

Annual Severe Anemia by Age Bin

The number of times a person’s hemoglobin count is less than 5 per person per year. The sum of the people whose hemoglobin is < 5 during each timestep during the reporting interval multiplied by 365 and divided by the sum of the people in the age bin during each time step during the reporting interval.

Annual Moderate Anemia by Age Bin

The number of times a person’s hemoglobin count is less than 8 per person per year. This includes everyone who was Severe. The sum of the people whose hemoglobin is < 8 during each timestep during the reporting interval multiplied by 365 and divided by the sum of the people in the age bin during each time step during the reporting interval.

Annual Mild Anemia by Age Bin

The number of times a person’s hemoglobin count is less than 11 per person per year. This includes everyone who was Severe and Moderate. The sum of the people whose hemoglobin is < 11 during each timestep during the reporting interval multiplied by 365 and divided by the sum of the people in the age bin during each time step during the reporting interval.
DataByTimeAndPfPRBinsAndAgeBins

This section contains statistics in three-dimensional arrays: Time (the reporting interval), Parasitemia Bins, and Age Bins.

Parameter

Description

PfPR by Parasitemia and Age Bin

The fraction of individuals whose parasite density and age fall into this bin. The sum of the people whose true parasite density in the PfPRBin and age bin divided by the total number of people in the age bin.

PfPR by Gametocytemia and Age Bin

The fraction of individuals whose gametocyte density and age fall into this gametocyte bin.

Smeared PfPR by Parasitemia and Age Bin

The fraction of individuals in this age bin whose true parasite density when smeared by CountPositiveSlideFields falls into this parasitemia bin.

Smeared PfPR by Gametocytemia and Age Bin

The fraction of individuals in this age bin whose true gametocyte density when smeared by CountPositiveSlideFields falls into this gametocyte bin.

Smeared True PfPR by Parasitemia and Age Bin

The fraction of individuals in this age bin whose true parasite density when smeared by NASBADensityWithUncertainty falls into this parasitemia bin.

Smeared True PfPR by Gametocytemia and Age Bin

The fraction of individuals in this age bin whose true gametocyte density when smeared by NASBADensityWithUncertainty falls into this parasitemia bin.
DataByTimeAndInfectiousnessBinsAndPfPRBinsAndAgeBins

This section contains statistics in four-dimensional arrays: Time (the reporting interval), Infectiousness, Parasitemia Bins, and Age Bins.

Parameter

Description

Infectiousness by Gametocytemia and Age Bin

The fraction of individuals whose infectiousness, gametocyte density, and age fall into these bins.

Age scaled Infectiousness by Gametocytemia and Age Bin

The fraction of individuals whose true infectiousness is scaled by their age-dependent Surface Area Biting, gametocyte density and age fall into these bins.

Infectiousness by smeared Gametocytemia and Age Bin

The fraction of individuals whose true infectiousness, gametocyte density smeared by NASBADensityWithUncertainty, and age fall into these bins.

Smeared Infectiousness by smeared Gametocytemia and Age Bin

The fraction of individuals whose true infectiousness is smeared by BinomialInfectiousness, gametocyte density smeared by NASBADensityWithUncertainty, and age falls into these bins.

Age scaled Smeared Infectiousness by smeared Gametocytemia and Age Bin

The fraction of individuals whose true infectiousness is first scaled by Surface Area Biting and then smeared by BinomialInfectiousness, gametocyte density smeared by NASBADensityWithUncertainty, and age falls into these bins.
Example

The following is a sample of a MalariaSummaryReport.json file.

{
    "Metadata": {
        "Start_Day": 0,
        "Reporting_Interval": 73,
        "Age Bins": [
            25,
            50,
            75,
            125
        ],
        "Parasitemia Bins": [
            500,
            50000,
            9999999
        ],
        "Gametocytemia Bins": [
            500,
            50000,
            9999999
        ],
        "Infectiousness Bins": [
            50,
            100
        ]
    },
    "DataByTime": {
        "Time Of Report": [
            73,
            146,
            219
        ],
        "Annual EIR": [
            3.162024962367,
            17.51441525354,
            11.87080321251
        ],
        "PfPR_2to10": [
            0.1704726251755,
            0.4665846270352,
            0.7762127578304
        ],
        "No Infection Streak": [
            0,
            0,
            0
        ],
        "Fraction Days Under 1pct Infected": [
            0,
            0,
            0
        ]
    },
    "DataByTimeAndAgeBins": {
        "PfPR by Age Bin": [
            [
                0.1339885339885,
                0.09905544651619,
                0.09631578947368,
                0.05854084753167
            ],
            [
                0.4092613233357,
                0.3057670056635,
                0.301073312716,
                0.3378867014865
            ],
            [
                0.6964023568685,
                0.4945852743663,
                0.5197093551317,
                0.5671100362757
            ]
        ],
        "Pf Gametocyte Prevalence by Age Bin": [
            [
                0.101678951679,
                0.0721908734053,
                0.07070175438596,
                0.0349497597204
            ],
            [
                0.3455263957848,
                0.2407892332988,
                0.2230307440422,
                0.2619525914022
            ],
            [
                0.6942707181209,
                0.4758303587634,
                0.511353315168,
                0.5461507456671
            ]
        ],
        "Mean Log Parasite Density": [
            [
                2.306478442565,
                1.629361294962,
                1.661861693924,
                1.524076431545
            ],
            [
                2.401303822193,
                1.681284855785,
                1.677762827268,
                1.61520629859
            ],
            [
                2.423047828561,
                1.609734215751,
                1.605083507743,
                1.682926607454
            ]
        ],
        "Annual Clinical Incidence by Age Bin": [
            [
                1.741298153531,
                0.2686457410455,
                0.3201754391193,
                0.1594582796097
            ],
            [
                4.401155002415,
                0.9113330598921,
                0.863198146224,
                0.4399356991053
            ],
            [
                6.150984396227,
                0.8612136561424,
                0.7293369323015,
                1.618299007416
            ]
        ],
        "Annual Severe Incidence by Age Bin": [
            [
                1.091113864444,
                0.08954858034849,
                0.1921052634716,
                0
            ],
            [
                3.017934858799,
                0.4445527121425,
                0.3319992870092,
                0.1466452330351
            ],
            [
                3.611317807809,
                0.3312360215932,
                0.4641235023737,
                0.4413542747498
            ]
        ],
        "Average Population by Age Bin": [
            [
                669.0410958904,
                223.3424657534,
                78.08219178082,
                31.35616438356
            ],
            [
                675.9589041096,
                224.9452054795,
                75.30136986301,
                34.09589041096
            ],
            [
                681.1917808219,
                226.4246575342,
                75.41095890411,
                33.98630136986
            ]
        ],
        "Annual Severe Incidence by Anemia by Age Bin": [
            [
                0,
                0,
                0,
                0
            ],
            [
                0,
                0,
                0,
                0
            ],
            [
                0.007340076845139,
                0,
                0,
                0
            ]
        ],
        "Annual Severe Incidence by Parasites by Age Bin": [
            [
                0,
                0,
                0,
                0
            ],
            [
                0,
                0,
                0,
                0
            ],
            [
                0.007340076845139,
                0,
                0,
                0
            ]
        ],
        "Annual Severe Incidence by Fever by Age Bin": [
            [
                1.091113864444,
                0.08954858034849,
                0.1921052634716,
                0
            ],
            [
                3.017934858799,
                0.4445527121425,
                0.3319992870092,
                0.1466452330351
            ],
            [
                3.596637654118,
                0.3312360215932,
                0.4641235023737,
                0.4413542747498
            ]
        ],
        "Annual Severe Anemia": [
            [
                0,
                0,
                0,
                0
            ],
            [
                0,
                0,
                0,
                0
            ],
            [
                0,
                0,
                0,
                0
            ]
        ],
        "Annual Moderate Anemia": [
            [
                0.05978706106544,
                0,
                0,
                0
            ],
            [
                1.33144184947,
                0,
                0,
                0
            ],
            [
                9.461359053385,
                0,
                0,
                0
            ]
        ],
        "Annual Mild Anemia": [
            [
                4.087940300349,
                0,
                0,
                0
            ],
            [
                21.04417812079,
                0,
                0,
                0
            ],
            [
                43.99642060976,
                0,
                0,
                0
            ]
        ]
    },
    "DataByTimeAndPfPRBinsAndAgeBins": {
        "PfPR by Parasitemia and Age Bin": [
            [
                [
                    0.2156224406224,
                    0.2732458292444,
                    0.2470175438596,
                    0.1799912625601
                ],
                [
                    0.04694922194922,
                    0.005642787046124,
                    0.005789473684211,
                    0.001310615989515
                ],
                [
                    0.0009213759213759,
                    0,
                    0,
                    0
                ]
            ],
            [
                [
                    0.512513932516,
                    0.6836368065282,
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                [
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        ],
        "Smeared PfPR by Parasitemia and Age Bin": [
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                [
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        "Smeared PfPR by Gametocytemia and Age Bin": [
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        "Smeared True PfPR by Parasitemia and Age Bin": [
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        "Smeared True PfPR by Gametocytemia and Age Bin": [
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    "DataByTimeAndInfectiousnessBinsAndPfPRBinsAndAgeBins": {
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        "Smeared Infectiousness by smeared Gametocytemia and Age Bin": [
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                ]
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        ],
        "Age scaled Smeared Infectiousness by smeared Gametocytemia and Age Bin": [
            [
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        ]
    }
}
MalariaSurveyJSONAnalyzer

The malaria survey report (MalariaSurveyJSONAnalyzer.json) is a JSON-formatted report that provides detailed information on an individual for each event that occurs during the reporting interval. Multiple files can be created for each reporting interval.

Configuration

To generate this report, the following parameters must be configured in the custom_reports.json file:

Parameter

Data Type

Min

Max

Default

Description

Filename_Suffix

string

NA

NA

(empty string)

Augments the filename of the report. If multiple reports are being generated, this allows you to distinguish among the multiple reports.

Start_Day

float

0

3.40282e+38

0

The day of the simulation to start collecting data.

End_Day

float

0

3.40282e+38

3.40282e+38

The day of the simulation to stop collecting data. Empty list implies all nodes.

Node_IDs_Of_Interest

array of integers

0

2.14748e+09

[]

Data will be collected for the nodes in this list.

Min_Age_Years

float

0

9.3228e+35

0

Minimum age in years of people to collect data on.

Max_Age_Years

float

0

9.3228e+35

9.3228e+35

Maximum age in years of people to collect data on.

Must_Have_IP_Key_Value

string

NA

NA

(empty string)

A Key:Value pair that the individual must have in order to be included. Empty string means to not include IPs in the selection criteria.

Must_Have_Intervention

string

NA

NA

(empty string)

The name of the intervention that the person must have in order to be included. Empty string means to not include interventions in the selection criteria.

Event_Trigger_List

list of strings

NA

NA

NA

The list of event triggers for the events included in the report.

Reporting_Interval

integer

1

1000000

1000000

Defines the cadence of the report by specifying how many time steps to collect data before writing to the file. This will limit system memory usage and is advised when large output files are expected.

Max_Number_Reports

integer

0

1000000

1

The maximum number of report output files that will be produced for a given campaign.

Pretty_Format

boolean

0

1

0

True (1) sets pretty JSON formatting. The default, false (0), saves space.

IP_Key_To_Collect

string

NA

NA

(empty string)

The name of the IndividualProperty Key whose value to collect; an empty string means collect values for all IPs.

 
{
    "Reports": [
        {
            "class": "MalariaSurveyJSONAnalyzer",
            "Filename_Suffix": "Node1",
            "Start_Day": 365,
            "End_Day": 465,
            "Node_IDs_Of_Interest": [ 1 ],
            "Min_Age_Years": 5,
            "Max_Age_Years": 10,
            "Must_Have_IP_Key_Value": "Accessibility:YES",
            "Must_Have_Intervention": "UsageDependentBednet"
            "Event_Trigger_List": [
                "EveryUpdate"
            ],
            "Reporting_Interval": 10,
            "Max_Number_Reports": 1,
            "Pretty_Format": 1,
            "IP_Key_To_Collect": "Risk"
        }
    ],
    "Use_Defaults": 1
}
Output file data

The report contains the following information:

Parameter

Data type

Description

ntsteps

integer

The number of days of the simulation for which data was collected. It equals the reporting interval unless the simulation ended before the reporting interval.

patient_array

array of strings

An array where there is an entry for each individual that experiences the specified event(s) during the reporting interval. If no events are listed, an exception is thrown. The data in a patient array contain two types of data: data that has one value for each timestep or day and data that is an array of data.
Patient_array channels

Parameter

Data type

Description

id

string

The individual ID of the person.

node_id

string

The External ID of the node that the person is currently in on the first event.

initial_age

float

The initial age of the person (in days) when the report started tracking them. This value will be non-zero for individuals created at initialization, but should be zero for the rest of the population.

local_birthday

float

The day that the individual was born/created, in relation to the start of the report.

Each of the following statistics is presented as an array, where each entry is the value of that field at the time of the event.

Parameter

Description

strain_ids

The antigen/clade ID and the genome ID of the individual’s current infection.

ip_data

If an IP_Key_To_Collec’ was specified, this will be that value. If it was not specified, this will show the value for all of the IPs.

true_asexual_parasites

The actual parasite density of the individual.

true_gametocytes

The actual gametocyte density of the individual.

smeared_true_asexual_parasites

The actual parasite density, smeared using NASBADensityWithUncertainty.

smeared_true_gametocytes

The actual gametocyte density, smeared using NASBADensityWithUncertainty.

asexual_parasites

The parasite density measured using the BLOOD_SMEAR_PARASITES diagnostic.

gametocytes

The gametocyte density measured using the BLOOD_SMEAR_GAMETOCYTES diagnostic.

pcr_parasites

The parasite density measured using the PCR_PARASITES diagnostic.

pcr_gametocytes

The gametocyte density measured using the PCR_GAMETOCYTES diagnostic.

pfhrp2

The HRP2 measured using the PF_HRP2 diagnostic.

smeared_asexual_parasites

Positive fields of view (pos_asexual_fields) with parasite density.

smeared_gametocytes

Positive fields of view (pos_gametocyte_fields) with gametocyte density.

infectiousness

Infectious of the individual at the time of the event.

infectiousness_smeared

Binomial infectiousness smearing.

infectiousness_age_scaled

Infectiousness adjusted for age dependent Surface Area Biting.

pos_asexual_fields

The number of positive fields of view for parasite smears.

pos_gametocyte_fields

The number of positive fields of view for gametocyte smears.

temps

The individual’s body temperature in Celsius if they have a fever, otherwise it is -1.
Example

The following is a sample of a MalariaSurveyJSONAnalyzer file.

{
    "ntsteps": 73,
    "patient_array": [{
            "id": 5,
            "node_id": 340461476,
            "initial_age": 2497.267822266,
            "local_birthday": -2466.267822266,
            "strain_ids": [
                [
                    0,
                    9947867
                ]
            ],
            "ip_data": [
                ""
            ],
            "true_asexual_parasites": [
                392.6246337891
            ],
            "true_gametocytes": [
                0
            ],
            "smeared_true_asexual_parasites": [
                48.01840209961
            ],
            "smeared_true_gametocytes": [
                0
            ],
            "asexual_parasites": [
                380
            ],
            "gametocytes": [
                0
            ],
            "pcr_parasites": [
                98.8094329834
            ],
            "pcr_gametocytes": [
                0
            ],
            "pfhrp2": [
                9.302060127258
            ],
            "smeared_asexual_parasites": [
                397.7009277344
            ],
            "smeared_gametocytes": [
                0
            ],
            "infectiousness": [
                0
            ],
            "infectiousness_smeared": [
                0
            ],
            "infectiousness_age_scaled": [
                0
            ],
            "pos_asexual_fields": [
                126
            ],
            "pos_gametocyte_fields": [
                0
            ],
            "temps": [
                38.01483154297
            ]
        },
        {
            "id": 11,
            "node_id": 340461476,
            "initial_age": 1387.354614258,
            "local_birthday": -1315.354614258,
            "strain_ids": [
                [
                    0,
                    505461
                ],
                [
                    0,
                    0
                ]
            ],
            "ip_data": [
                ""
            ],
            "true_asexual_parasites": [
                444.5673217773
            ],
            "true_gametocytes": [
                0
            ],
            "smeared_true_asexual_parasites": [
                1481.205932617
            ],
            "smeared_true_gametocytes": [
                0
            ],
            "asexual_parasites": [
                490
            ],
            "gametocytes": [
                0
            ],
            "pcr_parasites": [
                463.1502380371
            ],
            "pcr_gametocytes": [
                0
            ],
            "pfhrp2": [
                10.62057304382
            ],
            "smeared_asexual_parasites": [
                449.572052002
            ],
            "smeared_gametocytes": [
                0
            ],
            "infectiousness": [
                0
            ],
            "infectiousness_smeared": [
                0
            ],
            "infectiousness_age_scaled": [
                0
            ],
            "pos_asexual_fields": [
                135
            ],
            "pos_gametocyte_fields": [
                0
            ],
            "temps": [
                38.0454750061
            ]
        }
    ]
}
ReportDrugStatus

The drug status report (ReportDrugStatus.csv) provides status information on the drugs that an individual has taken or is waiting to take. Because the report provides information for each drug, for each individual, and for each time step, you may want to use the Start_Day and End_Day parameters to limit the size of the output file. You cannot filter based on the demographics parameter IndividualProperties.

Configuration

To generate this report, configure the following parameters in the custom_report.json file:

Parameter name

Data type

Min

Max

Default

Description

End_Day

float

0

3.40E+38

3.40E+38

The day to stop collecting data for the report.

Start_Day

float

0

3.40E+38

0

The day to start collecting data for the report.

 
{
    "Reports": [
        {
            "Start_Day": 300,
            "End_Day": 900,
            "class": "ReportDrugStatus"
        }
    ],
    "Use_Defaults": 1
}
Output data

The report contains the following stratification columns:

Parameter

Data type

Description

Time

integer

The day of the simulation on which the data was collected for the data in the row.

NodeID

string

The external ID of the node.

The report contains the following data columns:

Parameter

Data type

Description

IndividualID

integer

The ID of the individual who received the drug.

Gender

enum

The gender of the individual. Possible values are M or F.

AgeYears

integer

The max age in years of the age bin for the individual.

Infected

boolean

A true value (1) indicates the individual is infected and a false value (0) indicates the individual is not infected.

Infectiousness

float

A value from 0 to 1 that indicates how infectious an individual is, with 0 = not infectious and 1 = very infectious. This is the probability that an individual will infect a mosquito during a successful blood meal.

DrugName

string

The name of the drug indicated in the intervention (Malaria_Drug_Params in config.json). Depending on the intervention, this might be a concatenated value when an individual takes multiple pills in one dose.

CurrentEfficacy

float

The current efficacy of the drug(s). The efficacy is determined by the selected PKPD model; see Antimalarial drugs for more information.

NumRemainingDoses

integer

The number of remaining doses the individual will receive.
Example

The following is an example of ReportDrugStatus.csv.

Time

NodeID

IndividualID

Gender

AgeYears

Infected

Infectiousness

DrugName

CurrentEfficacy

NumRemainingDoses

40

7

8

F

6610.81

1

0

TestDrugA+TestDrugB

0.25125

1

40

7

15

M

4807.43

1

0

TestDrugA+TestDrugB

0.25125

1

40

7

22

F

5834.48

1

0

TestDrugA+TestDrugB

0.25125

1

40

6

392

M

11910

1

0

TestDrugB

0.239501

1

40

6

399

M

5829.22

1

0

TestDrugA

0.015448

0

40

6

399

M

5829.22

1

0

TestDrugB

0.239501

1

40

6

420

F

13548.3

1

0

TestDrugB

0.239501

1

40

6

427

F

3751.18

1

0

TestDrugA

0.015448

0

40

6

427

F

3751.18

1

0

TestDrugB

0.239501

1

40

5

6985

F

11397.3

1

0

TestDrugB

0.239501

1

40

5

6992

M

712.512

1

0

TestDrugB

0.239501

1

40

5

6999

M

11219.9

1

0

TestDrugB

0.239501

1

40

4

5

F

2423.01

1

0

TestDrugB

0.239501

1

40

4

12

F

7146.54

1

0

TestDrugB

0.239501

1

40

4

19

F

8099.7

1

0

TestDrugB

0.239501

1

40

4

26

F

16387.3

1

0

TestDrugB

0.239501

1

40

4

33

F

11535

1

0

TestDrugB

0.239501

1

40

4

40

M

6686.1

1

0

TestDrugB

0.239501

1
ReportMalariaFiltered

The malaria filtered report (ReportMalariaFiltered.json) is the same as the default InsetChart report, but provides filtering options to enable the user to select the data to be displayed for each time step or for each node. See InsetChart for more information about InsetChart.json.

Configuration

To generate this report, the following parameters must be configured in the custom_reports.json file:

Parameter name

Data type

Min

Max

Default

Description

Filename_Suffix

string

NA

NA

(empty string)

Augments the filename of the report. If multiple reports are being generated, this allows you to distinguish among the multiple reports.

Start_Day

float

0

3.40282e+38

0

The day of the simulation to start collecting data.

End_Day

float

0

3.40282e+38

3.40282e+38

The day of the simulation to stop collecting data.

Node_IDs_Of_Interest

array of integers

0

2.14748e+09

[]

Data will be collected for the nodes in this list. Empty list implies all nodes.

Min_Age_Years

float

0

9.3228e+35

0

Minimum age in years of people to collect data on.

Max_Age_Years

float

0

9.3228e+35

9.3228e+35

Maximum age in years of people to collect data on.

Must_Have_IP_Key_Value

string

NA

NA

(empty string)

A Key:Value pair that the individual must have in order to be included. Empty string means to not include IPs in the selection criteria.

Must_Have_Intervention

string

NA

NA

(empty string)

The name of the intervention that the person must have in order to be included. Empty string means to not include interventions in the selection criteria.

Has_Interventions

array of strings

NA

NA

[ ]

A channel is added to the report for each InterventionName you specify in the campaign file. In the InsetChart report, the channel name is Has _ and is the fraction of the population that has that intervention. The Intervention_Name values in the campaign should be the values in this parameter.

Include_30Day_Avg_Infection_Duration

boolean

NA

NA

1

If set to true (1), the 30-Day Avg Infection channel is included in the report.

 
{
    "Reports": [
        {
            "class": "ReportMalariaFiltered",
            "Filename_Suffix": "AllNodes",
            "Start_Day": 365,
            "End_Day": 1000,
            "Node_IDs_Of_Interest": [],
            "Min_Age_Years": 5,
            "Max_Age_Years": 10,
            "Must_Have_IP_Key_Value": "Risk:HIGH",
            "Must_Have_Intervention": "UsageDependentBednet",
            "Has_Interventions": ["SimpleVaccine","IRSHousingModification"],
            "Include_30Day_Avg_Infection_Duration": 1
        }
    ],
    "Use_Defaults": 1
}
Channels

The channels section contains the following parameters.

Parameter

Data type

Description

<Channel_Title>

string

The title of the particular channel.

Units

string

The units used for this channel.

Data

array

A list of the channel data at each time step.
Example

The following is a sample of an InsetChart.json file.

{
    "Header": {
        "DateTime": "Thu Dec 03 11:28:34 2015",
        "DTK_Version": "5738 HIV-Ongoing 2015/11/18 13:00:39",
        "Report_Type": "InsetChart",
        "Report_Version": "3.2",
        "Start_Time": 0,
        "Simulation_Timestep": 1,
        "Timesteps": 5,
        "Channels": 17
    },
    "Channels": {
        "Births": {
            "Units": "Births",
            "Data": [
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Campaign Cost": {
            "Units": "USD",
            "Data": [
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Cumulative Infections": {
            "Units": "",
            "Data": [
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Cumulative Reported Infections": {
            "Units": "",
            "Data": [
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Disease Deaths": {
            "Units": "",
            "Data": [
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Exposed Population": {
            "Units": "Exposed Fraction",
            "Data": [
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Human Infectious Reservoir": {
            "Units": "Total Infectivity",
            "Data": [
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Infected": {
            "Units": "Infected Fraction",
            "Data": [
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Infectious Population": {
            "Units": "Infectious Fraction",
            "Data": [
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Log Prevalence": {
            "Units": "Log Prevalence",
            "Data": [-10, -10, -10, -10, -10]
        },
        "New Infections": {
            "Units": "",
            "Data": [
                0,
                0,
                0,
                0,
                0
            ]
        },
        "New Reported Infections": {
            "Units": "",
            "Data": [
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Daily (Human) Infection Rate": {
            "Units": "Infection Rate",
            "Data": [
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Recovered Population": {
            "Units": "Recovered (Immune) Fraction",
            "Data": [
                0,
                0,
                0,
                0,
                0
            ]
        },
        "Statistical Population": {
            "Units": "Population",
            "Data": [
                900,
                900,
                900,
                900,
                900
            ]
        },
        "Susceptible Population": {
            "Units": "Susceptible Fraction",
            "Data": [
                1,
                1,
                1,
                1,
                1
            ]
        },
        "Waning Population": {
            "Units": "Waning Immunity Fraction",
            "Data": [
                0,
                0,
                0,
                0,
                0
            ]
        }
    }
}
ReportNodeDemographicsMalaria

The malaria node demographics report is a CSV-formatted report that extends the ReportNodeDemographics report (see ReportNodeDemographics) by adding malaria-specific statistics. The report still contains population information stratified by node, but the data columns now also contain information about malaria parasite counts.

Configuration

To generate this report, the following parameters must be configured in the custom_reports.json file:

Parameter name

Data type

Min

Max

Default

Description

Age_Bins

array of floats

-3.04E+38

3.04E+38

[ ]

The age bins (in years, in ascending order) to aggregate within and report. An empty array does not stratify by age.

IP_Key_To_Collect

string

NA

NA

(empty string)

The name of the IndividualProperties (IP) key by which to stratify the report. An empty string means the report is not stratified by IP.

Stratify_By_Gender

boolean

0

1

1

Set to true (1) to stratify by gender; a value of 0 will not stratify by gender.

Stratify_By_Has_Clinical_Symptoms

bool

NA

NA

1

If set to 1, the data will have an extra stratification for people who have clinical symptoms and those that do not. Default is 0 or no extra stratification.

 
{
    "Reports": [
        {
            "class": "ReportNodeDemographicsMalaria",
            "Age_Bins": [
                10,
                20,
                30,
                40,
                50,
                60,
                70,
                80,
                90,
                100,
                125
            ],
            "IP_Key_To_Collect": "",
            "Stratify_by_Gender": 1,
            "Stratify_By_Has_Clinical_Symptoms": 0
        }
    ],
    "Use_Defaults": 1
}
Output file data

The report will contain the following output data, divided between stratification columns and data columns.

Stratification columns

Parameter

Data type

Description

Time

float

The day of the simulation that the data was collected.

NodeID

integer

The External ID of the node for the data in the row in the report.

Gender

enum

Possible values are M or F; the gender of the individuals in the row in the report. This column only appears if Stratify_By_Gender = 1.

AgeYears

float

The max age in years of the bin for the individuals in the row in the report. If Age_Bins is empty, this column does not appear.

IndividualProp

string

The value of the IP for the individuals in the row in the report. If IP_Key_To_Collect is an empty string, then this column does not appear.

HasClinicalSymptoms

enum

T implies that the people in the row are having clinical symptoms. F implies they do not. This column only appears if Stratify_By_Has_Clinical_Symptoms = 1.
Data columns

Parameter

Data type

Description

NumIndividuals

integer

The number of individuals that meet the stratification values.

NumInfected

integer

The number of infected individuals that meet the stratification values.

NodeProp = <Node Property Keys>

string

For each possible Node Property, there is one column where the data in the column is the value of that particular property. If there are no NodeProperties, then there are no columns.

AvgInfectiousness

float

The average infectiousness to mosquitos for the individuals of this row. Infectiousness is based on the number of mature gametocytes that the person has.

AvgParasiteDensity

float

The average true parasite density for the individuals of this row.

AvgGametocyteDensity

float

The average true gametocyte density for the individuals of this row.

AvgVariantFractionPfEMP1Major

float

For each individual, a count is made of the number of PfEMP1 Major antibodies the individual has and is divided by the total number of possible variants (Falciparum_PfEMP1_Variants). This is the average of this value for all the individuals represented in this row.

AvgNumInfections

float

The average number of infections for the people of this row.

AvgInfectionClearedDuration

float

The average duration to clear infections for the people of this row.

NumInfectionsCleared

integer

The number of cleared infections for the people of this row.

NumHasFever

integer

The number of people in the row that have a fever according to the diagnostic using the Report_Detection_Threshold_Fever parameter.

NumHasClinicalSymptoms

integer

If Stratify_By_Has_Clinical_Symptoms = 0, then this column is present with the number of people in the row that are considered to have ‘clinical’ symptoms.
Example

The following is an example of ReportNodeDemographicsMalaria.csv.

Time

NodeID

Gender

AgeYears

IndividualProp=StudyGroups

HasClinicalSymptoms

NumIndividuals

NumInfected

AvgInfectiousness

AvgParasiteDensity

AvgGametocyteDensity

AvgVariantFractionPfEMP1Major

AvgNumInfections

AvgInfectionClearedDuration

NumInfectionsCleared

NumHasFever

50

1

M

10

arm1

F

366

0

0

0

0

0

0

0

0

0

50

1

M

10

arm1

T

151

151

0.00593093

112435

1.06936

0.0165342

1

0

0

151

50

1

M

10

arm2

F

1256

0

0

0

0

0

0

0

0

0

50

1

M

10

arm2

T

292

292

0.00615811

111366

1.11031

0.0165525

1

0

0

292

50

1

M

10

arm3

F

2744

0

0

0

0

0

0

0

0

0

50

1

M

10

arm3

T

282

282

0.00600333

111783

1.08239

0.0165839

1

0

0

282

50

1

M

100

arm1

F

335

0

0

0

0

0

0

0

0

0

50

1

M

100

arm1

T

159

159

0.00205596

140531

0.371012

0.0166038

1

0

0

159

50

1

M

100

arm2

F

946

0

0

0

0

0

0

0

0

0

50

1

M

100

arm2

T

588

588

0.00205613

140583

0.371029

0.0165647

1

0

0

588

50

1

M

100

arm3

F

2645

0

0

0

0

0

0

0

0

0

50

1

M

100

arm3

T

297

297

0.00205629

140105

0.371017

0.0165545

1

0

0

297

50

1

F

10

arm1

F

379

0

0

0

0

0

0

0

0

0

50

1

F

10

arm1

T

164

164

0.00606003

110326

1.09222

0.0165447

1

0

0

164

50

1

F

10

arm2

F

1143

0

0

0

0

0

0

0

0

0

50

1

F

10

arm2

T

288

288

0.00595153

111696

1.07296

0.0165162

1

0

0

288

50

1

F

10

arm3

F

2809

0

0

0

0

0

0

0

0

0

50

1

F

10

arm3

T

302

302

0.00597365

111095

1.07672

0.0165011

1

0

0

302

50

1

F

100

arm1

F

355

0

0

0

0

0

0

0

0

0

50

1

F

100

arm1

T

144

144

0.00205568

141105

0.37102

0.0166435

1

0

0

144

50

1

F

100

arm2

F

887

0

0

0

0

0

0

0

0

0

50

1

F

100

arm2

T

579

579

0.00205593

140874

0.371017

0.0165688

1

0

0

579

50

1

F

100

arm3

F

2584

0

0

0

0

0

0

0

0

0

50

1

F

100

arm3

T

305

305

0.00205636

140043

0.371022

0.0165465

1

0

0

305

50

2

M

10

arm1

F

340

0

0

0

0

0

0

0

0

0

50

2

M

10

arm1

T

147

147

0.00608663

112032

1.09768

0.016644

1

0

0

147

50

2

M

10

arm2

F

1161

0

0

0

0

0

0

0

0

0

50

2

M

10

arm2

T

311

311

0.00599196

111639

1.08021

0.0165274

1

0

0

311

50

2

M

10

arm3

F

2742

0

0

0

0

0

0

0

0

0

50

2

M

10

arm3

T

285

285

0.00596162

112175

1.07493

0.0165731

1

0

0

285

50

2

M

100

arm1

F

327

0

0

0

0

0

0

0

0

0

50

2

M

100

arm1

T

160

160

0.0020567

139714

0.371016

0.0164792

1

0

0

160

50

2

M

100

arm2

F

899

0

0

0

0

0

0

0

0

0

50

2

M

100

arm2

T

583

583

0.00205599

140620

0.371006

0.0165638

1

0

0

583

50

2

M

100

arm3

F

2697

0

0

0

0

0

0

0

0

0

50

2

M

100

arm3

T

278

278

0.00205585

141016

0.371021

0.0165828

1

0

0

278

50

2

F

10

arm1

F

332

0

0

0

0

0

0

0

0

0

50

2

F

10

arm1

T

160

160

0.00600168

112563

1.08236

0.0166042

1

0

0

160

50

2

F

10

arm2

F

1184

0

0

0

0

0

0

0

0

0

50

2

F

10

arm2

T

333

333

0.00607677

111940

1.09565

0.0165566

1

0

0

333

50

2

F

10

arm3

F

2731

0

0

0

0

0

0

0

0

0

50

2

F

10

arm3

T

294

294

0.00601945

111251

1.08511

0.0165306

1

0

0

294

50

2

F

100

arm1

F

341

0

0

0

0

0

0

0

0

0

50

2

F

100

arm1

T

156

156

0.00205657

139663

0.371027

0.0165171

1

0

0

156

50

2

F

100

arm2

F

914

0

0

0

0

0

0

0

0

0

50

2

F

100

arm2

T

626

626

0.00205598

140771

0.371019

0.0165549

1

0

0

626

50

2

F

100

arm3

F

2728

0

0

0

0

0

0

0

0

0

50

2

F

100

arm3

T

271

271

0.00205597

140890

0.370996

0.0165068

1

0

0

271
ReportNodeDemographicsMalariaGenetics

The malaria genetics node demographics report (ReportNodeDemographcsMalariaGenetics.csv) extends the data collected in the malaria node demographics report by adding data about the number of infections with specific gentic barcodes. The malaria node demographics genetics report does not include columns for Genome_Markers because this report assumes that the simulation setup parameter Malaria_Model is set to MALARIA_MECHANISTIC_MODEL_WITH_PARASITE_GENETICS.

Note

If you need detailed data on the infections with different barcodes, use the MalariaSQLReport. That report contains data on all barcodes, without specifying what they are.

Configuration

To generate this report, the following parameters must be configured in the custom_reports.json file:

Parameter

Data type

Min

Max

Default

Description

Age_Bins

array of floats

-3.04E+38

3.04E+38

[ ]

The age bins (in years, in ascending order) to aggregate within and report. An empty array does not stratify by age.

Barcodes

array of strings

NA

NA

[ ]

A list of barcode strings. The report contains the number of human infections with each barcode. Use ‘*’ for a wild card at a loci to include all values at that loci. For example, ‘A*T’ includes AAT, ACT, AGT, and ATT. The report contains an OtherBarcodes column for barcodes that are not defined. Note: There is no validation that the barcode strings are valid barcodes for the scenario.

Drug_Resistant_Statistic_Type

enum

NA

NA

NUM_PEOPLE_WITH_RESISTANT_INFECTION

Indicates the statistic in the DrugResistant columns. Possible values are: NUM_PEOPLE_WITH_RESISTANT_INFECTION: A person is counted if they have one infection with that drug resistant marker; NUM_INFECTIONS: the total number of infections with that marker.

Drug_Resistant_Strings

array of strings

NA

NA

[ ]

A list of strings representing the set of drug resistant markers. A column will be created with the number of human infections with that barcode. One can use a ‘*’ for a wild card. An ‘OtherBarcodes’ column will be created for barcodes not defined.

IP_Key_To_Collect

string

NA

NA

(empty string)

The name of the IndividualProperties (IP) key by which to stratify the report. An empty string means the report is not stratified by IP.

Stratify_By_Gender

bool

NA

NA

1

Set to true (1) to stratify by gender. Set to false (0) to not stratify by gender.

Stratify_By_Has_Clinical_Symptoms

bool

NA

NA

1

If set to 1, the data will have an extra stratification for people who have clinical symptoms and those that do not. Default is 0 or no extra stratification.

 
{
    "Reports": [
        {
            "Class": "ReportNodeDemographicsMalariaGenetics",
            "Age_Bins": [
                10,
                100
            ],
            "Barcodes": [
                "TA",
                "AT",
                "TT"
            ],
            "Drug_Resistant_Stat_Type": "NUM_INFECTIONS",
            "Drug_Resistant_Strings": [
                "TA",
                "AT",
                "TT"
            ],
            "IP_Key_To_Collect": "",
            "Stratify_By_Gender": 0,
            "Stratify_By_Has_Clinical_Symptoms": 0
        }
    ],
    "Use_Defaults": 1
}
Output file data

The report will contain the following output data, divided between stratification columns and data columns.

Stratification columns

Parameter

Data type

Description

Time

float

The day of the simulation that the data was collected.

NodeID

integer

The External ID of the node for the data in the row in the report.

Gender

enum

Possible values are M or F; the gender of the individuals in the row in the report. This column only appears if Stratify_By_Gender = 1.

AgeYears

float

The max age in years of the bin for the individuals in the row in the report. If Age_Bins is empty, this column does not appear.

IndividualProp

string

The value of the IP for the individuals in the row in the report. If IP_Key_To_Collect is an empty string, then this column does not appear.

HasClinicalSymptoms

enum

T implies that the people in the row are having clinical symptoms. F implies they do not. This column only appears if Stratify_By_Has_Clinical_Symptoms = 1.
Data columns

Parameter

Data type

Description

NumIndividuals

integer

The number of individuals who meet the stratification values.

NumInfected

integer

The number of infected individuals who meet the stratification values.

NodeProp = <Node Property Keys>

string

For each possible Node Property, there is one column where the data in the column is the value of that particular property. If there are no Node Properties, then there are no columns.

AvgInfectiousness

float

The average infectiousness to mosquitos for the individuals of this row. Infectiousness is based on the number of mature gametocytes that the person has.

AvgParasiteDensity

float

The average true parasite density for the individuals of this row.

AvgGametocyteDensity

float

The average true gametocyte density for the individuals of this row.

AvgVariantFractionPfEMP1Major

float

For each individual, a count is made of the number of PfEMP1 Major antibodies the individual has and is divided by the total number of possible variants (Falciparum_PfEMP1_Variants). This is the average of this value for all the individuals represented in this row.

AvgNumInfections

float

The average number of infections for the people of this row.

AvgInfectionClearedDuration

float

The average duration to clear infections for the people of this row.

NumInfectionsCleared

integer

The number of cleared infections for the people of this row.

NumHasFever

integer

The number of people in the row that have a fever according to the diagnostic using the Report_Detection_Threshold_Fever parameter.

NumHasClinicalSymptoms

integer

If Stratify_By_Has_Clinical_Symptoms = 0, then this column is present with the number of people in the row that are considered to have ‘clinical’ symptoms.

[barcodes]

integer

The number of human infections with the barcode that is the column header. If you used a wild card at a loci, it includes all the barcodes that match the other loci exactly but ignores differences at this loci. For example, A*T includes AAT, ACT, AGT, and ATT. There will be one column for each barcode.

OtherBarcodes

integer

The number of human infections whose barcode is not counted by the other columns.
Example

The following is an example of a ReportNodeDemographicsMalariaGenetics.csv

Time

NodeID

AgeYears

NumIndividuals

NumInfected

AvgInfectiousness

AvgParasiteDensity

AvgGametocyteDensity

AvgVariantFractionPfEMP1Major

AvgNumInfections

AvgInfectionClearedDuration

NumInfectionsCleared

NumHasFever

NumHasClinicalSymptoms

TA

AT

TT

OtherBarcodes

TA

AT

TT

NoDrugResistance

200

4

10

339

339

0.61637

12162.5

1038.6

0.180659

8.50443

61.6944

72

153

199

1491

1094

163

135

1491

1094

163

135

200

4

100

661

661

0.627337

12805.6

1084.05

0.174226

8.72315

64.2336

107

275

374

2989

2206

276

295

2989

2206

276

295

200

3

10

343

343

0.610594

10458.5

1086.55

0.180894

8.43149

61.4211

57

123

172

0

2892

0

0

0

2892

0

0

200

3

100

657

657

0.639379

14241.1

1074.46

0.175525

8.78691

64.4853

136

260

359

0

5773

0

0

0

5773

0

0

200

2

10

330

330

0.62785

15945.3

1107.33

0.173889

8.56364

61.6377

69

134

179

2826

0

0

0

2826

0

0

0

200

2

100

670

670

0.649023

13305.3

1150.4

0.175338

8.68806

64.8

115

268

361

5821

0

0

0

5821

0

0

0

200

1

10

321

321

0.650348

12543.2

1097.84

0.173842

8.64486

61.678

59

134

191

0

0

0

2775

0

0

0

2775

200

1

100

679

679

0.65758

14437.5

1330.21

0.170324

8.72607

65.3988

100

295

408

0

0

0

5925

0

0

0

5925

201

4

10

339

339

0.605389

11346.4

995.784

0.183186

8.39233

61.7711

83

145

202

1465

1075

163

142

1465

1075

163

142

201

4

100

661

661

0.625909

13623.3

1074.37

0.175986

8.66566

63.8224

133

286

376

2960

2191

277

300

2960

2191

277

300

201

3

10

342

342

0.616016

10373.4

1081.56

0.183762

8.43275

62.6765

68

142

176

0

2884

0

0

0

2884

0

0

201

3

100

658

658

0.631187

12559.5

1054.92

0.177351

8.71125

65.0115

141

259

350

0

5732

0

0

0

5732

0

0

201

2

10

330

330

0.618134

13675.8

1121.95

0.176455

8.52424

61.2059

68

137

184

2813

0

0

0

2813

0

0

0

201

2

100

670

670

0.648521

13363.5

1191.18

0.177448

8.68507

64.4876

121

269

359

5819

0

0

0

5819

0

0

0

201

1

10

321

321

0.655716

12432.2

1214.83

0.175867

8.65732

62.1475

61

134

180

0

0

0

2779

0

0

0

2779

201

1

100

679

679

0.644713

13488.7

1274.26

0.172356

8.67894

65.386

114

283

400

0

0

0

5893

0

0

0

5893

202

4

10

339

339

0.598077

10570.8

975.812

0.185811

8.38053

62.5556

72

149

204

1454

1076

165

146

1454

1076

165

146

202

4

100

661

661

0.613423

12068.9

1060.33

0.178084

8.65053

64.641

117

270

372

2950

2183

281

304

2950

2183

281

304

202

3

10

342

342

0.593883

10130.2

1061.35

0.185741

8.41521

62.3333

63

132

186

0

2878

0

0

0

2878

0

0

202

3

100

658

658

0.625583

12888.2

1052.63

0.179271

8.68541

64.9604

139

256

347

0

5715

0

0

0

5715

0

0

202

2

10

330

330

0.606342

14340

1131.78

0.17805

8.44545

61.1493

67

137

182

2787

0

0

0

2787

0

0

0

202

2

100

670

670

0.637783

12996.4

1159.33

0.179343

8.67015

64.4728

115

260

362

5809

0

0

0

5809

0

0

0

202

1

10

321

321

0.646784

12130

1165.26

0.177684

8.64486

61.4776

67

131

179

0

0

0

2775

0

0

0

2775

202

1

100

679

679

0.641917

13286.9

1271.64

0.174335

8.69072

65.7283

115

280

399

0

0

0

5901

0

0

0

5901
ReportSimpleMalariaTransmissionJSON

The simple malaria transmission report (ReportSimpleMalariaTransmissionJSON.json) is a JSON-formatted report that provides data on malaria transmission, by tracking who transmitted malaria to whom. The report can only be used when the simulation setup parameter Malaria_Model is set to MALARIA_MECHANISTIC_MODEL_WITH_CO_TRANSMISSION. This report is typically used as input to the GenEpi model. See the Output file data section for more details.

..To do: need section on co-transmission!

Configuration

To generate this report, the following parameters must be configured in the custom_reports.json file:

Parameter

Data Type

Min

Max

Default

Description

Filename_Suffix

string

NA

NA

(empty string)

Augments the filename of the report. If multiple reports are being generated, this allows you to distinguish among the multiple reports.

Start_Day

float

0

3.40282e+38

0

The day of the simulation to start collecting data.

End_Day

float

0

3.40282e+38

3.40282e+38

The day of the simulation to stop collecting data.

Node_IDs_Of_Interest

array of integers

0

2.14748e+09

[]

Data will be collected for the nodes in this list. Empty list implies all nodes.

Min_Age_Years

float

0

9.3228e+35

0

Minimum age in years of people to collect data on.

Max_Age_Years

float

0

9.3228e+35

9.3228e+35

Maximum age in years of people to collect data on.

Must_Have_IP_Key_Value

string

NA

NA

(empty string)

A Key:Value pair that the individual must have in order to be included. Empty string means to not include IPs in the selection criteria.

Must_Have_Intervention

string

NA

NA

(empty string)

The name of the intervention that the person must have in order to be included. Empty string means to not include interventions in the selection criteria.

Pretty_Format

boolean

0

1

0

True (1) sets pretty JSON formatting. The default, false (0), saves space.

Start_Day

float

0

3.40E+38

0

The day to start collecting data for the report.

Include_Human_To_Vector_Transmission

boolean

NA

NA

0

If set to true (1), Human-to-Vector transmission events will be included. One can identify these events because the ‘acquireIndividualId’=0 and transmitTime=acquireTime. WARNING: This can make the file size quite large.

 
{
    "Reports": [{
        "class": "ReportSimpleMalariaTransmissionJSON",
        "Filename_Suffix": "Node1",
        "Start_Day": 365,
        "End_Day": 465,
        "Node_IDs_Of_Interest": [ 1 ],
        "Min_Age_Years": 5,
        "Max_Age_Years": 10,
        "Must_Have_IP_Key_Value": "Accessibility:YES",
        "Must_Have_Intervention": "UsageDependentBednet"
        "Pretty_Format": 1
    }],
    "Use_Defaults": 1
}
Output file data

The report contains transmissions as an array of CoTransmission objects that contain data on where the infection came from. Each object represents a new infection in the specified node. The following is the structure of a CoTransmission object:

Parameter

Data type

Description

acquireIndividualId

integer

The ID of the individual who received an infection from the vector.

acquireInfectionIds

integer

The list of infections created due to a bite from the vector. There is only one entry in this list at this time.

acquireTime

integer

The day the vector infected the individual with acquireIndividualId.

concurrentInfectionIds

array of integers

The IDs of other infections the acquiring individual had when they were infected with this new infection.

node_id

integer

The ID of the node where the infection occurred.

transmitTime

integer

The day when the vector was infected. Can be 0 if the infection was due to OutbreakIndividual.

transmitIndividualId

integer

The ID of the individual that infected the vector. Can be 0 if the infection was due to OutbreakIndividual.

transmitInfectionIds

array of integers

The list of infection IDs of the individuals infecting the vectors. Can be empty if the infection was due to OutbreakIndividual.

transmitGametocyteDensities

array of floats

A parallel list to transmitInfectionIds where each gametocyte density is the density for the corresponding infection. Can be empty if the infection was due to OutbreakIndividual.

vectorId

integer

The ID of the vector that was infected by the transmitIndividualId and who gave it to the acquireIndividualId. Can be 0 if the infection was due to OutbreakIndividual.
Example

The following is an example of a ReportSimpleMalariaTransmissionJSON file.

{
    "transmissions": [{
            "node_id": 1,
            "transmitTime": 0,
            "transmitIndividualId": 0,
            "transmitInfectionIds": [],
            "transmitGametocyteDensities": [],
            "vectorId": 0,
            "acquireTime": 100,
            "acquireIndividualId": 6,
            "acquireInfectionIds": [
                1
            ],
            "concurrentInfectionIds": []
        },
        {
            "node_id": 1,
            "transmitTime": 231,
            "transmitIndividualId": 62,
            "transmitInfectionIds": [
                579,
                625,
                693,
                715,
                747,
                785,
                819,
                865,
                935,
                965
            ],
            "transmitGametocyteDensities": [
                1154.381591797,
                935.8335571289,
                1806.419799805,
                60.45545959473,
                59.96842575073,
                57.74061584473,
                3.254687070847,
                2.291612148285,
                0.1661687344313,
                0.005024351645261
            ],
            "vectorId": 204416,
            "acquireTime": 249,
            "acquireIndividualId": 50,
            "acquireInfectionIds": [
                1557
            ],
            "concurrentInfectionIds": [
                603,
                645,
                667,
                691,
                739,
                769,
                807,
                849,
                1551
            ]
        }
    ]
}
ReportVectorStatsMalariaGenetics

The vector statistics and malaria genetics report (ReportVectorStatsMalariaGenetics.csv) is a CSV-formatted report similar to the ReportVectorStats report. It provides genetic barcode information in addition to detailed life-cycle data on the vectors in the simulation. The report is stratified by time, node ID, and (optionally) species.

When Malaria_Model is set to MALARIA_MECHANISTIC_MODEL_WITH_PARASITE_GENETICS, this report will provide detailed insight into the status of the parasites in the vector population, including details on the oocysts, sporozoites, biting, etc.

Configuration

To generate this report, the following parameters must be configured in the custom_reports.json file:

Parameter

Data type

Min

Max

Default

Description

Species_List

array of strings

NA

NA

[ ]

The species for which to include information. If the list is empty or absent, then data for all species will be collected.

Stratify_By_Species

boolean

NA

NA

0

If set to true (1), then data will be stratified by species for each node.

Include_Wolbachia_Columns

boolean

NA

NA

0

If set to true (1), columns will be added for each type of Wolbachia. Summation of columns should be equal to VectorPopulation.

Include_Gestation_Columns

boolean

NA

NA

0

If set to true (1), columns will be added for feeding and gestation.

Include_Death_By_State_Columns

boolean

NA

NA

0

Adds columns for the number of vectors that died in this state during this time step as well as the average age. It adds two columns for each of the following states: ADULT, INFECTED, INFECIOUS, and MALE.

Barcodes

array of strings

NA

NA

empty list

A list of barcode strings where a column will be created with the number of vectors with sporozoites with that barcode. Use ‘*’ for a wild card. An OtherBarcodes column will be created for those not defined.

 
{
    "Reports": [
        {
            "Species_List": [
                "arabiensis",
                "funestus"
            ],
            "Stratify_By_Species": 1,
            "Include_Wolbachia_Columns": 0,
            "Include_Gestation_Columns": 1,
            "Barcodes": [
                "AAAAAA",
                "AAAATTA"
            ],
            "class": "ReportVectorStatsMalariaGenetics"
        }
    ],
    "Use_Defaults": 1
}
Output file data

The output report will contain the following information.

Stratification columns

Parameter

Data type

Description

Time

integer

The day of the simulation that the data was collected.

NodeID

integer

The External ID of the node that the data is being collected for.

Species

string

If Stratify_By_Species = 1, then the species column will contain the name of the species for the given row.
Data columns

Parameter

Data type

Description

Population

integer

The number of people in the node.

VectorPopulation

integer

The number of adult female vectors that are in the STATE_INFECTIOUS, STATE_INFECTED, and STATE_ADULT.

STATE_INFECTIOUS

integer

The number of adult female vectors that are infectious.

STATE_INFECTED

integer

The number of adult female vectors that are infected.

STATE_ADULT

integer

The number of adult female vectors.

STATE_MALE

integer

The number of adult male vectors.

STATE_IMMATURE

integer

The number of immature vectors, includes male and female.

STATE_LARVA

integer

The number of larva, includes male and female.

STATE_EGG

integer

The number of eggs, includes male and female.

NumLookingToFeed

integer

If Include_Gestation_Columns = 1, then this column contains the number of adult female vectors that were looking to feed during this time step.

NumFedCount

integer

If Include_Gestation_Columns = 1, then this column contains the number of adult female vectors that had a blood meal during this time step.

NumGestatingBegin

integer

If Include_Gestation_Columns = 1, then this column contains the number of adult female vectors that were gestating and did not die due to non-feeding mortality.

NumGestatingEnd

integer

If Include_Gestation_Columns = 1, then this column contains the number of adult female vectors that were gestating at the end of the time step.

NumAttemptFeedIndoor

integer

If Include_Gestation_Columns = 1, then this column contains the number of adult female vectors that attempt to feed indoors. This group of vectors can still die indoors before they feed on a human.

NumAttemptFeedOutdoor

integer

If Include_Gestation_Columns = 1, then this column contains the number of adult female vectors that attempt to feed outdoors. This group of vectors can still die outdoors before they feed on a human.

NumAttemptButNotFeed

integer

If Include_Gestation_Columns = 1, then this column contains the number of adult female vectors that were attempting to feed indoors or outdoors but did not die or feed on a human. They will look to feed during the next day.

NewEggsCount

integer

The number of eggs that were laid this day.

IndoorBitesCount

float

The number of bites made on humans indoors. This includes vectors that live for another day as well as those that die trying to get away.

IndoorBitesCountInfectious

float

The number of bites made by infectious vectors on humans indoors. This includes vectors that live for another day as well as those that die trying to get away.

OutdoorBitesCount

float

The number of bites made on humans outdoors. This includes vectors that live for another day as well as those that die trying to get away.

OutdoorBitesCountInfectious

float

The number of bites made by infectious vectors on humans outdoors. This includes vectors that live for another day as well as those that die trying to get away.

UnmatedAdults

float

The number of adult females that have not mated. This can be an important statistic when the male vector population is suppressed. When the male population is low, females moving from immature to adult might not find a male to mate with for several days. The female continues to feed but she does not produce fertile eggs.

NewAdults

integer

The number of female vectors that matured to adulthood this day.

DiedBeforeFeeding

integer

The number of vectors that died this time step due to local mortality, sugar feeding, outdoor area killing, etc.

DiedDuringFeedingIndoor

integer

This is the number of vectors that died indoors while attempting to feed (e.g. due to bed nets).

DiedDuringFeedingOutdoor

integer

This is the number of vectors that died outdoors while attempting to feed.

NumDiedInfectious

integer

If Include_Death_By_State_Columns = 1, then this column contains the number of infectious, mature, female vectors that died during this time step.

NumDiedInfected

integer

If Include_Death_By_State_Columns = 1, then this column contains the number of infected, mature, female vectors that died during this time step.

NumDiedAdults

integer

If Include_Death_By_State_Columns = 1, then this column contains the number of mature female vectors that are neither infected or infectious that died during this time step.

NumDiedMale

integer

If Include_Death_By_State_Columns = 1, then this column contains the number of mature male vectors that died during this time step.

AvgAgeAtDeathInfectious

float

If Include_Death_By_State_Columns = 1, then this column contains the average age (in days) of the the infectious, mature, female vectors that died during this time step.

AvgAgeAtDeathInfected

float

If Include_Death_By_State_Columns = 1, then this column contains the average age (in days) of the the infected, mature, female vectors that died during this time step.

AvgAgeAtDeathAdults

float

If Include_Death_By_State_Columns = 1, then this column contains the average age (in days) of the the mature, female vectors that are neither infected or infectious that died during this time step.

AvgAgeAtDeathMale

float

If Include_Death_By_State_Columns = 1, then this column contains the average age (in days) of the the mature male vectors that died during this time step.

NumGestatingOnDay_0

integer

If Include_Gestation_Columns = 1, then this column contains the number of vectors that are gestating but with 0 more days before attempting to feed.

NumGestatingOnDay_1

integer

If Include_Gestation_Columns = 1, then this column contains the number of vectors that are gestating but with 1 more days before attempting to feed.

NumGestatingOnDay_2

integer

If Include_Gestation_Columns = 1, then this column contains the number of vectors that are gestating but with 2 more days before attempting to feed.

NumGestatingOnDay_3

integer

If Include_Gestation_Columns = 1, then this column contains the number of vectors that are gestating but with 3 more days before attempting to feed.

NumGestatingOnDay_4

integer

If Include_Gestation_Columns = 1, then this column contains the number of vectors that are gestating but with 4 more days before attempting to feed.

NumGestatingOnDay_5

integer

If Include_Gestation_Columns = 1, then this column contains the number of vectors that are gestating but with 5 more days before attempting to feed.

NumGestatingOnDay_6

integer

If Include_Gestation_Columns = 1, then this column contains the number of vectors that are gestating but with 6 more days before attempting to feed.

NumGestatingOnDay_7

integer

If Include_Gestation_Columns = 1, then this column contains the number of vectors that are gestating but with 7 more days before attempting to feed.

VECTOR_WOLBACHIA_FREE

integer

If Include_Wolbachia_Columns = 1, then this column contains the number of adult female vectors that are Wolbachia free.

VECTOR_WOLBACHIA_A

integer

If Include_Wolbachia_Columns = 1, then this column contains the number of adult female vectors that have Wolbachia A.

VECTOR_WOLBACHIA_B

integer

If Include_Wolbachia_Columns = 1, then this column contains the number of adult female vectors that have Wolbachia B.

VECTOR_WOLBACHIA_AB

integer

If Include_Wolbachia_Columns = 1, then this column contains the number of adult female vectors that have Wolbachia AB.

MigrationFromCountLocal

integer

This is the number of adult female vectors that made a local migration trip away from this node.

MigrationFromCountRegiona

integer

This is the number of adult female vectors that made a regional migration trip away from this node.

XXX_AvailableHabitat

integer

If Stratify_By_Species = 0, then this column title does not have the species name in it. If Stratify_By_Species = 1, then there is a column for each species. This column contains the number of larva that the habitat could add (e.g. number of spots open); equal to current capacity - current larval count.

XXX_EggCrowdingCorrection

float

If Stratify_By_Species = 0, then this column title does not have the species name in it. If Stratify_By_Species = 1, then there is a column for each species. This column contains the probability that eggs die due to overcrowding.

NumVectorsNone

integer

The number of uninfected/noninfectious vectors; they contain neither oocysts or sporozoites.

NumVectorsOnlyOocysts

integer

The number of vectors that are infected and contain only oocysts. The lack of sporozoites implies that the vectors are infected but not infectious. This is the stage before the oocysts turn into sporozoites.

NumVectorsOnlySporozoites

integer

The number of vectors that are infected and contain only sporozoites. The fact that the vectors have sporozoites means that they are infectious and give the sporozoites to humans.

NumVectorsBothOocystsSporozoites

integer

The number of vectors that have both oocysts and sporozoites. A vector can have both due to getting infected at different times by different bites.

NumBitesAdults

integer

The number of bites on humans made by uninfected vectors, neither infected or infectious. The vector that did the biting could have lived, died during feed, or died after feed.

NumBitesInfected

integer

The number of bites on humans made by a vector that is infected but not infectious. That is, a vector that only has oocysts.

NumBitesInfectious

integer

The number of bites on humans made by an infectious vector, e.g. has sporozoites. The vector could have both oocysts and sporozoites. This biting vector could die during feeding, after feeding, or live to the next day.

NumDiedAdults

integer

The number of uninfected vectors that died this day. They could have died from interventions, while trying to feed, or just old age.

NumDiedInfected

integer

The number of infected vectors that died this day. These vectors had oocysts but no sporozoites.

NumDiedInfectious

integer

The number of infectious vectors that died this day. These vectors must have had sporozoites, but they could have had oocysts as well.

NumParasiteCohortsOocysts

integer

The number of parasite cohorts in the oocyst state counted from all of the infected vectors. Each cohort is unique based on the vector it is in, the age, and the parasite genome.

NumParasiteCohortsSporozoites

integer

The number of parasite cohorts in the sporozoite state counted from all of the vectors that contain them. Each cohort is unique based on the vector it is in, the age, and the parasite genome.

NumOocysts

integer

The number of oocysts in the vector population.

NumSporozoites

integer

The number of sporozoites in the vector population.

NumInfectiousToAdult

integer

The number of vectors that transitioned from infectious (had sporozoites) to adult (having no sporozoites or oocysts).

NumInfectiousToInfected

integer

The number of vectors that transitioned from infectious (had sporozoites) to infected (having only oocysts).

<Barcode>

integer

Number of vectors with sporozoites with the indicated barcode. If a wild card (‘*’) is used, then it is the number of vectors in that group that contain sporozoites. The barcode indicated is provided by the user in the Barcodes parameter.

OtherBarcodes

integer

The number of vectors with sporozoites having a barcode different from those specified by the user. This column only appears when the user specifies barcodes in the Barcodes parameter.
Example

The following is an examples of a ReportVectorStats.csv file.

Time

NodeID

Population

VectorPopulation

STATE_INFECTIOUS

STATE_INFECTED

STATE_ADULT

STATE_MALE

STATE_IMMATURE

STATE_LARVA

STATE_EGG

NewEggsCount

IndoorBitesCount

IndoorBitesCount-Infectious

OutdoorBitesCount

OutdoorBitesCount-Infectious

NewAdults

UnmatedAdults

DiedBeforeFeeding

DiedDuringFeedingIndoor

DiedDuringFeedingOutdoor

MigrationFromCountLocal

MigrationFromCountRegional

SillySkeeter_AvailableHabitat

SillySkeeter_EggCrowdingCorrection

NumInfectousBitesGiven

NumInfectousBitesReceived

InfectiousBitesGivenMinusReceived

NumVectorsNone

NumVectorsOnlyOocysts

NumVectorsOnlySporozoites

NumVectorsBothOocystsSporozoites

NumBitesAdults

NumBitesInfected

NumBitesInfectious

NumDiedAdults

NumDiedInfected

NumDiedInfectious

NumParasiteCohortsOocysts

NumParasiteCohortsSporozoites

NumOocysts

NumSporozoites

NumInfectiousToAdult

NumInfectiousToInfected

AAAAAAAAAAAAAAAAAAAAAAAA

TAAAAAAAAAAAAAAAAAAAAAAA

AAAAAAAAAAAAAAAAAAAAAAAT

TAAAAAAAAAAAAAAAAAAAAAAT

OtherBarcodes

270

3

52

785

0

10

775

1046

1037

25045

37360

18680

260

0

0

0

99

0

69

30

0

0

0

2490.64

0.0669741

0

0

0

775

10

0

0

259

1

0

99

0

0

18

0

46

0

0

0

0

0

0

0

0

270

1

50

747

58

479

210

1067

1054

24802

28920

14460

246

15

0

0

83

0

76

20

0

0

0

2733.64

0.0787309

15

15

0

210

479

0

58

115

116

15

31

61

4

1895

829

3353

3683462

0

0

44

39

40

47

0

270

4

53

776

57

383

336

1061

1021

24880

29960

14980

244

20

0

0

110

0

67

22

0

1

0

2655.64

0.0821259

20

20

0

336

383

7

50

132

92

20

39

43

7

730

123

2263

2504765

0

0

4

3

2

56

0

270

2

50

782

0

0

782

1050

1018

24844

34600

17300

249

0

0

0

89

0

65

23

0

0

0

2691.64

0.0677958

0

0

0

782

0

0

0

249

0

0

88

0

0

0

0

0

0

0

0

0

0

0

0

0

271

3

52

787

0

12

775

1031

1102

24918

36240

18120

262

0

0

0

94

0

75

18

0

0

0

2617.69

0.066666

0

0

0

775

12

0

0

258

4

0

92

1

0

21

0

53

0

0

0

0

0

0

0

0

271

1

50

767

53

479

235

1059

1047

24872

28440

14220

228

13

0

0

108

0

70

18

0

0

0

2663.69

0.0945243

13

13

0

235

479

0

53

101

114

13

25

52

11

1864

788

3368

3632071

0

0

39

35

33

43

0

271

4

53

786

53

372

361

1064

993

24997

29800

14900

257

13

0

0

108

0

72

25

0

0

0

2538.69

0.0886396

13

13

0

361

372

6

47

139

105

13

41

45

11

691

124

2193

2590858

0

0

4

3

2

52

0

271

2

50

777

0

0

777

1055

1003

24796

36400

18200

238

0

0

0

101

0

73

33

0

0

0

2739.69

0.0777931

0

0

0

777

0

0

0

238

0

0

106

0

0

0

0

0

0

0

0

0

0

0

0

0

272

3

52

787

0

18

769

1028

1099

24819

38280

19140

266

0

0

0

99

0

67

32

0

0

0

2716.73

0.072232

0

0

0

769

18

0

0

263

3

0

99

0

0

33

0

81

0

0

0

0

0

0

0

0

272

1

50

756

55

467

234

1047

1039

24834

35040

17520

290

20

0

0

95

0

76

30

0

0

0

2701.73

0.09366

20

20

0

234

467

1

54

132

138

20

38

62

6

1932

811

3507

3542809

0

0

41

36

35

46

0

272

4

53

778

53

362

363

1064

1007

24839

33880

16940

271

12

0

0

98

0

81

25

0

1

0

2696.73

0.0851909

12

12

0

363

362

5

48

157

102

12

46

50

10

679

97

2147

2554876

0

0

1

1

1

53

0

272

2

50

768

0

0

768

1063

1024

24823

42400

21200

290

0

0

0

91

0

73

27

0

0

0

2712.73

0.0752662

0

0

0

768

0

0

0

290

0

0

100

0

0

0

0

0

0

0

0

0

0

0

0

0

273

3

52

787

0

21

766

1053

1073

24869

37880

18940

265

0

0

0

108

0

73

35

0

0

0

2666.78

0.0709701

0

0

0

766

21

0

0

262

3

0

108

0

0

39

0

95

0

0

0

0

0

0

0

0

273

1

50

768

59

474

235

1069

1053

24934

29200

14600

240

18

0

0

93

0

66

16

0

0

0

2601.78

0.0771043

18

18

0

235

474

1

58

106

116

18

28

49

5

1912

864

3489

3833429

0

0

46

43

39

48

0

273

4

53

778

56

363

359

1054

984

24803

31560

15780

251

17

0

0

90

0

70

19

0

1

0

2732.78

0.0795966

17

17

0

359

363

3

53

149

85

17

45

39

5

668

98

2119

2629423

0

0

1

1

1

56

0

273

2

50

773

0

0

773

1063

1040

24838

35600

17800

245

0

0

0

105

0

76

24

0

0

0

2697.78

0.0639796

0

0

0

773

0

0

0

245

0

0

100

0

0

0

0

0

0

0

0

0

0

0

0

0

274

3

52

799

0

29

770

1069

1078

24915

37400

18700

267

0

0

0

100

0

58

31

0

0

0

2620.82

0.0704007

0

0

0

770

29

0

0

263

4

0

88

1

0

53

0

115

0

0

0

0

0

0

0

0

274

1

50

790

61

475

254

1070

1036

24919

28360

14180

231

14

0

0

112

0

69

21

0

0

0

2616.82

0.089102

14

14

0

254

475

1

60

109

108

14

29

54

7

1944

782

3536

3469317

0

0

44

44

41

45

0

274

4

53

771

59

349

363

1033

1008

24794

32720

16360

254

19

0

0

85

0

71

20

0

0

0

2741.82

0.0865899

19

19

0

363

349

3

56

153

82

19

45

41

5

648

95

2048

2428792

0

0

0

0

0

59

0

274

2

50

766

0

0

766

1054

1008

24911

33600

16800

261

0

0

0

97

0

72

32

0

0

0

2624.82

0.0757803

0

0

0

766

0

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261

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104

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0
SpatialReportMalariaFiltered

The filtered malaria spatial report (SpatialReportMalariaFiltered.bin) provides spatial information on malaria simulations, similar to the SpatialReport, but allows for filtering the data and collection over different intervals. The output of this file differs from the spatial output report by adding Start_Day and a Reporting_Interval.

Configuration

To generate the report, configure the following parameters in the custom_report.json file:

Parameter

Data type

Min

Max

Default

Description

Filename_Suffix

string

NA

NA

(empty string)

Augments the filename of the report. If multiple reports are being generated, this allows you to distinguish among the multiple reports.

Start_Day

float

0

3.40282e+38

0

The day of the simulation to start collecting data.

End_Day

float

0

3.40282e+38

3.40282e+38

The day of the simulation to stop collecting data.

Node_IDs_Of_Interest

array of integers

0

2.14748e+09

[]

Data will be collected for the nodes in this list. Empty list implies all nodes.

Min_Age_Years

float

0

9.3228e+35

0

Minimum age in years of people to collect data on.

Max_Age_Years

float

0

9.3228e+35

9.3228e+35

Maximum age in years of people to collect data on.

Must_Have_IP_Key_Value

string

NA

NA

(empty string)

A Key:Value pair that the individual must have in order to be included. Empty string means to not include IPs in the selection criteria.

Must_Have_Intervention

string

NA

NA

(empty string)

The name of the intervention that the person must have in order to be included. Empty string means to not include interventions in the selection criteria.

Reporting_Interval

float

0

3.40E+38

1

The number of days to collect data and average over.

Spatial_Output_Channels

list of strings

NA

NA

(empty string)

An array of channel names for spatial output by node and time step. The data from each channel will be written to a separate binary file. See the table below for all possible channels.

For more information on spatial reports, see SpatialReport.

Example configuration 
{
    "class": "SpatialReportMalariaFiltered",
    "Filename_Suffix": "NewCases",
    "Start_Day": 0,
    "End_Day": 85,
    "Node_IDs_Of_Interest": [1, 2, 3, 4, 5, 6, 7, 8],
    "Min_Age_Years": 5,
    "Max_Age_Years": 10,
    "Must_Have_IP_Key_Value": "Risk:HIGH",
    "Must_Have_Intervention": "UsageDependentBednet",
    "Reporting_Interval": 1,
    "Spatial_Output_Channels": [
        "Population",
        "New_Infections",
        "Prevalence",
        "Adult_Vectors",
        "New_Diagnostic_Prevalence",
        "New_Clinical_Cases"
    ]
}
Report structure and data channel descriptions

The file contains the following data channels:

Data channel

Description

Adult_Vectors

The total number of adult female vectors in the node at each time step. Includes vectors from all species and all infectious sates (not infected, infected, and infectious).

Air_Temperature

The air temperature of that day for that node in Celcius.

Births

The live births cumulative up to that day for that node.

Blood_Smear_Gametocyte_Prevalence

The fraction of the population that is detectable with the BLOOD_SMEAR_GAMETOCYTES version of MalariaDiagnostic. The detectability of the diagnostic is controlled by the parameters Report_Gametocyte_Smear_Sensitivity and Report_Detection_Threshold_Blood_Smear_Gametocytes.

Blood_Smear_Parasite_Prevalence

The fraction of the population that is detectable with the BLOOD_SMEAR_PARASITES version of MalariaDiagnostic. The detectability of the diagnostic is controlled by the parameters Report_Parasite_Smear_Sensitivity and Report_Detection_Threshold_Blood_Smear_Parasites.

Campaign_Cost

The cumulative cost of the interventions that have been distributed to that node. This is based on the Cost_To_Consumer parameter in the interventions used.

Daily_Bites_Per_Human

The average number of mosquito bites received per individual on that day.

Daily_EIR

The number of infectious bites per person in the node made that day/reporting period as determined by the vector model. It includes the infectious bites of all species for both indoor and outdoor bites.

Disease_Deaths

The number of people that died in the node that day. The population for the node at the beginning of the next time step will be the population at this time step minus the number of deaths plus new births.

Fever_Prevalence

This fraction is the number of people whose fever is above the Report_Detection_Threshold_Fever threshold divided by the number of people in the node.

Human_Infectious_Reservoir

The sum of the infectiousness of each person in the node; a person’s infectiousness is the probability of infecting a mosquito during a successful blood meal.

Infected

The fraction of the population currently infected.

Infectious Vectors

The fraction of vectors in the simulation that are currently infected and infectious.

Land_Temperature

The temperature of the land/ground that day in that node in degrees Celsius.

Mean_Parasitemia

The geometric mean number of parasites per microliter of blood.

New_Clinical_Cases

The number of new clinical cases for a given day.

New_Infections

The number of people who received a new infection in that node on that time step.

New_Reported_Infections

The number of people reported to have received a new infection in that node on that time step. This number should be about 50% of the value in New_Infections.

New_Severe_Cases

The number of new severe cases of malaria on that day in the node. An individual is considered to be a severe case if the combined probability of anemia, parasite density, and fever is greater than a uniform random number. This combined probability is the combination of sigmoid using the following parameters: Anemia_Severe_Threshold and Anemia_Severe_Inverse_Width, Parasite_Severe_Threshold and Parasite_Severe_Inverse_Width, and Fever_Severe_Threshold and Fever_Severe_Inverse_Width.

PCR_Gametocyte_Prevalence

The fraction of the population that is detectable with the PCR_GAMETOCYTES version of MalariaDiagnostic. The detectability of the diagnostic is controlled by the parameter Report_Detection_Threshold_PCR_Gametocytes.

PCR_Parasite_Prevalence

The fraction of the population that is detectable with the PCR_PARASITES version of MalariaDiagnostic. The detectability of the diagnostic is controlled by the parameter Report_Detection_Threshold_PCR_Parasites.

PfHRP2_Prevalence

The fraction of the population that is detectable with the PF_HRP2 version of MalariaDiagnostic. The detectability of the diagnostic is controlled by the parameter Report_Detection_Threshold_PCR_PfHRP2.

Population

The total number of individuals in the node on that day.

Prevalence

The fraction of the population currently infected in that node on that day.

Rainfall

The number millimeters of rainfall in that node on that day.

Relative_Humidity

The amount of water vapor present in air expressed as a percentage of the amount needed for saturation at the same temperature for the node on that day.

True_Prevalence

The fraction of the population that is detectable with the TRUE_PARASITE_DENSITY version of MalariaDiagnostic. The detectability of the diagnostic is controlled by the parameter Report_Detection_Threshold_True_Parasite_Density.
ReportInterventionPopAvg

ReportInterventionPopAvg is a CSV-formatted report that gives population average data on the usage of interventions. It provides data on the fraction of people or nodes that have an intervention as well as averages on the intervention’s efficacy. For each persistant intervention that has been distributed to a node or person, the report provides one line in the CSV for each intervention used in that node. Since nodel-level interventions (usually vector control) can only have one per node, the data will be for that one intervention. The individual-level interventions will have data for the people in that node.

The report will only collect data on persistant intervensions. These are interventions that a person has between time steps. These are things like SimpleBednet. In contrast, diagnostic interventions are usually executed in the time step they are distributed and do not exist between time steps.

Not all interventions provide data for this report. If an intervention does not provide support, there should be one warning in the standard out indicating this.

For information on what interventions have what effects, please check the following:

  • :doc:’parameter-campaign-individual-interventions’

  • :doc:’parameter-campaign-node-interventions’

Configuration

To generate this report, the following parameters must be configured in the custom_reports.json file:

Parameter name

Data type

Min

Max

Default

Description

Filename_Suffix

string

NA

NA

(empty string)

Augments the filename of the report. If multiple reports are being generated, this allows you to distinguish among the multiple reports.

Start_Day

float

0

3.40282e+38

0

The day of the simulation to start collecting data.

End_Day

float

0

3.40282e+38

3.40282e+38

The day of the simulation to stop collecting data.

Node_IDs_Of_Interest

array of integers

0

2.14748e+09

[]

Data will be collected for the nodes in this list. Empty list implies all nodes.

Min_Age_Years

float

0

9.3228e+35

0

Minimum age in years of people to collect data on.

Max_Age_Years

float

0

9.3228e+35

9.3228e+35

Maximum age in years of people to collect data on.

Must_Have_IP_Key_Value

string

NA

NA

(empty string)

A Key:Value pair that the individual must have in order to be included. Empty string means to not include IPs in the selection criteria.

Must_Have_Intervention

string

NA

NA

(empty string)

The name of the intervention that the person must have in order to be included. Empty string means to not include interventions in the selection criteria.

 
{
    "Reports": [
        {
            "class": "ReportInterventionPopAvg",
            "Filename_Suffix": "AllNodes",
            "Start_Day": 90,
            "End_Day": 1000,
            "Node_IDs_Of_Interest": [],
            "Min_Age_Years": 5,
            "Max_Age_Years": 10,
            "Must_Have_IP_Key_Value": "Risk:HIGH",
            "Must_Have_Intervention": "UsageDependentBednet"
        }
    ],
    "Use_Defaults": 1
}
Output file data

The report will contain the following output data, divided between stratification columns and data columns.

Stratification columns

Parameter

Data type

Description

Time

integer

The day of the simulation that the data was collected.

NodeID

integer

The External ID of the node for the data in the row in the report.
Data columns

Parameter

Data type

Description

NodePopulation

integer

The population of the node that has the indicate intervention.

InterventionName

string

The name of the intervention. This can be custom using the Intervention_Name parameter of the intervention or it will be the default name which is usually just the class name.

FractionHas

float

For individual-level interventions, this will be the fraction of the people in the node that have at least one of the indicated interventions. For node-level interventions, this will almost always be 1.0 since most of those inteventions only allow one per node.

AvgNumberOfInterventions

float

For individuals that have the intervention, this will be the average number of instances of that intervention per person. For nodes that have the intervention, this will be the number of instances of that intervention in the node - it is a count and not an average.

AvgEfficacy-Attracting

float

For interventions that have an attracting effect like HumanHostSeekingTrap, individual-level interventions will have the average attracting efficacy for the people that have this intervention in this node during this time step. Node-level interventions will have the attracting efficacy of the intervention.

AvgEfficacy-Repelling

float

For interventions that have a repelling effect like IRSHousingModification, individual-level interventions will have the average repelling efficacy for the people that have this intervention in this node during this time step. Node-level interventions will have the repelling efficacy of the intervention.

AvgEfficacy-Blocking

float

For interventions that have a blocking effect like SimpleBednet, individual-level interventions will have the average blocking efficacy for the people that have this intervention in this node during this time step. Node-level interventions will have the blocking efficacy of the intervention.

AvgEfficacy-Killing

float

For interventions that have a killing effect like SimpleBednet, individual-level interventions will have the average killing efficacy for the people that have this intervention in this node during this time step. Node-level interventions will have the killing efficacy of the intervention.

AvgEfficacy-Usage

float

For interventions that have an usage effect like UsageDependentBednet, individual-level interventions will have the average usage efficacy for the people that have this intervention in this node during this time step. There are no node-level interventions with this effect at this time.

AvgEfficacy-AcquisitionBlocking

float

For interventions that have an acquisition blocking effect like SimpleVaccine, individual-level interventions will have the average acquisition blocking efficacy for the people that have this intervention in this node during this time step. There are no node-level interventions with this effect at this time.

AvgEfficacy-TransmissionBlocking

float

For interventions that have a transmission blocking effect like SimpleVaccine, individual-level interventions will have the average transmission blocking efficacy for the people that have this intervention in this node during this time step. There are no node-level interventions with this effect at this time.

AvgEfficacy-MortalityBlocking

float

For interventions that have a mortality blocking effect like SimpleVaccine, individual-level interventions will have the average mortality blocking efficacy for the people that have this intervention in this node during this time step. There are no node-level interventions with this effect at this time.

DrugConcentration

float

For drug interventions, this will be the concentration of the drugs involved in the intervention averaged over the people in the node with the drug during this time step. For drug interventions like MultiPackComboDrug, each person will contribute the sum of the concentrations of the drugs they have taken from the pack. There are no node-level interventions with this effect at this time.
Example

The following is an example of ReportInterventionPopAvg.csv.

Time

NodeID

NodePopulation

InterventionName

FractionHas

AvgNumberOfInterventions

AvgEfficacy-Attracting

AvgEfficacy-Repelling

AvgEfficacy-Blocking

AvgEfficacy-Killing

AvgEfficacy-Usage

AvgEfficacy-AcquisitionBlocking

AvgEfficacy-TransmissionBlocking

AvgEfficacy-MortalityBlocking

DrugConcentration

268

2

1000

AdherentDrug-Piperaquine

0.008

1

0

0

0

0

0

0

0

0

0.110735

268

2

1000

AntimalarialDrug

0.097

1

0

0

0

0

0

0

0

0

8.5187e-05

268

2

1000

ArtificialDiet

1

1

0.5

0

0

0

0

0

0

0

0

268

2

1000

HumanHostSeekingTrap

0.098

1

0

0

0

0.3

0

0

0

0

0

268

2

1000

Ivermectin

0.092

1

0

0

0

0.3

0

0

0

0

0

268

2

1000

Larvicides

1

1

0

0

0

0.0681233

0

0

0

0

0

268

2

1000

SimpleBednet

0.206

1

0

0.1

0

0.450683

1

0

0

0

0

268

2

1000

SimpleIndividualRepellent

0.102

1

0

0.2

0

0

0

0

0

0

0

268

2

1000

SpaceSpraying

1

1

0

0

0

0.075

0

0

0

0

0

268

2

1000

SpatialRepellentHousingModification

0.092

1

0

0.3

0

0

0

0

0

0

0

268

2

1000

UsageDependentBednet

0.086

1

0

0.1

1

0.5

1

0

0

0

0

268

1

1000

AdherentDrug-Piperaquine

0.006

1

0

0

0

0

0

0

0

0

0.104079

268

1

1000

AntimalarialDrug

0.1

1

0

0

0

0

0

0

0

0

8.50645e-05

268

1

1000

ArtificialDiet

1

1

0.5

0

0

0

0

0

0

0

0

268

1

1000

HumanHostSeekingTrap

0.112

1

0

0

0

0.3

0

0

0

0

0

268

1

1000

Ivermectin

0.106

1

0

0

0

0.3

0

0

0

0

0

268

1

1000

Larvicides

1

1

0

0

0

0.0681233

0

0

0

0

0

268

1

1000

SimpleBednet

0.193

1

0

0.1

0

0.450683

1

0

0

0

0

268

1

1000

SimpleIndividualRepellent

0.09

1

0

0.2

0

0

0

0

0

0

0

268

1

1000

SpaceSpraying

1

1

0

0

0

0.075

0

0

0

0

0

268

1

1000

SpatialRepellentHousingModification

0.101

1

0

0.3

0

0

0

0

0

0

0

268

1

1000

UsageDependentBednet

0.091

1

0

0.1

1

0.5

1

0

0

0

0

269

2

1000

AdherentDrug-Piperaquine

0.008

1

0

0

0

0

0

0

0

0

0.108067

269

2

1000

AntimalarialDrug

0.097

1

0

0

0

0

0

0

0

0

6.97451e-05

269

2

1000

ArtificialDiet

1

1

0.5

0

0

0

0

0

0

0

0

269

2

1000

HumanHostSeekingTrap

0.098

1

0

0

0

0.3

0

0

0

0

0

269

2

1000

Ivermectin

0.092

1

0

0

0

0.3

0

0

0

0

0

269

2

1000

Larvicides

1

1

0

0

0

0.0667608

0

0

0

0

0

269

2

1000

SimpleBednet

0.206

1

0

0.1

0

0.448428

1

0

0

0

0

269

2

1000

SimpleIndividualRepellent

0.102

1

0

0.2

0

0

0

0

0

0

0

269

2

1000

SpaceSpraying

1

1

0

0

0

0.075

0

0

0

0

0

269

2

1000

SpatialRepellentHousingModification

0.092

1

0

0.3

0

0

0

0

0

0

0

269

1

1000

AdherentDrug-Piperaquine

0.006

1

0

0

0

0

0

0

0

0

0.101571

269

1

1000

AntimalarialDrug

0.1

1

0

0

0

0

0

0

0

0

6.96448e-05

269

1

1000

ArtificialDiet

1

1

0.5

0

0

0

0

0

0

0

0

269

1

1000

HumanHostSeekingTrap

0.112

1

0

0

0

0.3

0

0

0

0

0

269

1

1000

Ivermectin

0.106

1

0

0

0

0.3

0

0

0

0

0

269

1

1000

Larvicides

1

1

0

0

0

0.0667608

0

0

0

0

0

269

1

1000

SimpleBednet

0.193

1

0

0.1

0

0.448428

1

0

0

0

0

269

1

1000

SimpleIndividualRepellent

0.09

1

0

0.2

0

0

0

0

0

0

0

269

1

1000

SpaceSpraying

1

1

0

0

0

0.075

0

0

0

0

0

269

1

1000

SpatialRepellentHousingModification

0.101

1

0

0.3

0

0

0

0

0

0

0

Tutorials and simulation examples

While the workings of the model are explained in detail in Understanding the model, it is often more useful to learn through hands-on implementation. To illustrate many of the concepts and capabilities of EMOD, IDM provides tested files to run example simulations that model a variety of disease scenarios. The directory for each scenario contains the files needed to run a simulation and generate the output graphs. Click EMOD scenarios to download a zipped folder containing all required files. These scenarios are referenced throughout the documentation where relevant.

Because all disease-specific EMOD simulation types are based on the generic model, the Generic model scenarios are helpful in learning the basics of modeling with EMOD, even if you intend to use one of the disease-specific simulation types. Additionally, the transmission-level scenarios are very relevant to modeling the diseases that build upon them.

Note

Because the EMOD model is stochastic, graphs you produce from the scenarios may appear slightly different from those included in the documentation.

Included files

The compiled EMOD executable (Eradication.exe) is included. Unlike other executables, you do not double-click this file to run simulations. Instead, EMOD simulations are run by calling Eradication.exe from the command line. For these scenarios, you can simply double-click the runEMOD.cmd file in each scenario directory, which contains the commands that will run the simulation.

Of course, you can also run simulations directly from the command line or using COmputational Modeling Platform Service (COMPS) if you choose. Review Running a simulation for complete information on running simulations from the command line, or just open the runEMOD.cmd file in a text editor to see what commands it contains.

The Scenarios directory contains subdirectories that contain scenarios for different simulation types. The README files in each of the scenario subdirectories describe what aspect of the model the scenario illustrates. Each scenario subdirectory contains the configuration and campaign files needed to run an EMOD simulation. Also included are plotting batch files that contain commands to run Python scripts. You can simply double-click the batch files to run these scripts and produce graphs.

The Demographics_Files directory contains the demographics files for all scenarios. The demographics files used with each scenario are listed in Demographics_Filenames in the configuration file. This directory also contains a few climate files used with the malaria scenarios.

The Scripts directory contains the Python scripts used to plot the output of the EMOD simulations. These are the scripts pointed to by the plotting batch files.

Most input files use JSON (JavaScript Object Notation) format. If you are unfamiliar with this format, see EMOD parameter reference.

Prerequisites

We recommend running these scenarios on a Windows computer. You must install the HPC, MPI, and Python packages described in Install EMOD on Windows. You do not need to download another copy of Eradication.exe, although it won’t hurt if you do.

Although EMOD also supports CentOS, the scripts to run simulations will not work and the installation instructions differ. However, you can still run simulations from the command line. See the CentOS installation instructions at Install EMOD on Linux.

General EMOD information

The features of the model are described in greater detail elsewhere in the documentation, but this provides a brief overview of the features most relevant to using these tutorials.

Manipulating population size

It is often useful to re-use the same demographics files for running different simulations. Depending on the dynamics of the simulation, or the output reports that are desired, it may be important to change the size of the population. An alternative to modifying the demographics file is to use the configuration parameter, x_Base_Population. This parameter lets you adjust the population set by the demographics parameter, InitialPopulation.

Overlay files

An alternative to changing parameter values or adding parameters to base files is to create what is called an “overlay file.” These files contain parameter settings or additional parameters that will overwrite the settings configured in the base configuration and demographic files. When using an overlay for configuration parameters, include the files in the working directory. For demographic overlay files, all files to be used must be listed in the array for Demographics_Filenames. The order of these files is important: the base file must be listed first, and the overlay listed second. When there is more than one overlay file, the order of the overlays determines what information will be overridden; the last file in order will overlay all files preceding it. Overlay files can make it simple to swap out different parameter values without having to modify the base files, so you can experiment with different settings. Demographic overlays make it simple to increase the complexity of the population structure without having to create complex files.

Creating campaigns

Much of the power and flexibility of EMOD comes from the customizable campaign interventions. For more information on creating campaigns, see Creating campaigns. Briefly, campaigns are created through the hierarchical organization of JSON objects (parameter groups).

campaign event

Campaign events determine when and where an intervention is distributed during a campaign. “When” can be the number of days after the beginning of the simulation or at a point during a particular calendar year. “Where” is the geographic node or nodes in which the event takes place.

event coordinator

Event coordinators are nested within the campaign event JSON object and determine who receives the intervention. “Who” is determined by filtering on age, gender, or on the individual properties configured in the demographics files, such as risk group or sociodemographic category. See Individual and node properties.

individual-level intervention

Individual-level interventions determine what will be distributed to individuals to reduce the spread of a disease. For example, distributing vaccines or drugs are individual-level interventions. In the schema, these are labeled as IndividualTargeted.

It is also possible (but not required) to configure why a particular intervention is distributed by adding trigger conditions to the intervention. For example, interventions can be triggered by notifications broadcast after some an event, such as Births (the individual’s own birth), GaveBirth, NewInfectionEvent, and more. It’s also possible to have one intervention trigger another intervention by asking the first intervention to broadcast a unique string, and having the second intervention be triggered upon receipt of that string. See Event list.

Individual-level interventions can be used as part of configuring a cascade of care along with the individual properties set in the demographics file. Use Disqualifying_Properties to disqualify individuals who would otherwise receive the intervention and New_Property_Value to assign a new value when the intervention is received. For example, you can assign a property value after receiving the first-line treatment for a disease and prevent anyone from receiving the second-line treatment unless they have that property value and are still symptomatic.

node-level intervention

Node-level interventions determine what will be distributed to each node to reduce the spread of a disease. For example, spraying larvicide in a village to kill mosquito larvae is a node-level malaria intervention. Sometimes this can be an intermediate intervention that schedules another intervention. Node-level disease outbreaks are also configured as “interventions”. In the schema, these are labeled as NodeTargeted.

It is also possible (but not required) to configure why a particular intervention is distributed by adding trigger conditions to the intervention. For example, interventions can be triggered by notifications broadcast after some an event, such as Births, NewInfectionEvent, and more. It’s also possible to have one intervention trigger another intervention by asking the first intervention to broadcast a unique string, and having the second intervention be triggered upon receipt of that string. See Event list.

All campaign events and event coordinators can be found in Events and event coordinators; node-level interventions can be found in Node-level interventions; and individual-level interventions can be found in Individual-level interventions.

Output reports

When you run a simulation using the runEMOD.cmd file, an output directory will be created that contains the output reports from the model. Some of these will be in JSON format and others in CSV. The standard report is InsetChart.json, which includes many different channels including births, deaths, cumulative infections, new infections, and more. You can enable other built-in reports by setting the appropriate parameter to 1 in the configuration file.

For a complete list of all available output reports, see Output settings.

Generic model scenarios

The EMOD generic model is explained in detail in Model overview. While the various components that comprise the model are explained with examples, it may be more useful to learn the model through hands-on implementation. The following sections will introduce sets of example files that illustrate how the generic model works on particular topics. All files are available in the downloadable EMOD scenarios > Scenarios > Generic folder and, in addition to the explanations below, each scenario will have a more detailed README file to cover relevant information.

EMOD supports different simulation types for various diseases and disease classes. The features present in the GENERIC_SIM simulation type, which can be configured to model a variety of different diseases, are inherited by the more specific simulation types, such as malaria or HIV. Because all disease-specific EMOD simulation types are based on the generic model, the following scenarios are helpful in learning the basics of modeling with EMOD, even if you intend to use one of the disease-specific simulation types.

For more information on the software architecture and inheritance, see Overview of EMOD software.

Compartmental models

Before working with an agent-based model like EMOD, you will likely be familiar with deterministic compartmental models. In these models, an ordinary differential equation (ODE) controls the rate at which the population transitions from each disease state (compartment) to another. These models assume that individuals within each compartment are identical and the entire population is well-mixed. Given the same inputs, a compartmental model will always produce the same outcomes.

While compartmental models are very useful, in many situations an agent-based model is a better tool for describing complex phenomena. In these models, each agent (such as a human or a vector) is simulated individually. Their behavior and interactions with one another are determined using decision rules. For a more detailed comparison of EMOD and compartmental models, see Compartmental models and EMOD.

The following scenarios under EMOD scenarios > Scenarios > Generic show how you can use EMOD to simulate disease scenarios that might also be simulated using a compartmental model. For example, a disease like measles could be modeled using a compartmental SIR model or it could be modeled using EMOD configured such that there is no incubation period or waning immunity after infection.

  • SI

  • SIS

  • SIR

  • SIRS

  • SEIR

  • SEIR_VitalDynamics

  • SEIRS

Density scaling of infection

By default, EMOD uses frequency-dependent transmission and the overall transmissibility does not change with population size or density. An infected individual will infect the same number of people either in a small village or in a metropolitan area. The EMOD scenarios > Scenarios > Generic >DensityScaling scenario shows how you can configure EMOD so population density has an effect on the transmissibility of disease. For a detailed description of the mathematics behind this functionality, see Population density and transmission scaling.

Heterogeneous transmission

By default, the probability of transmitting disease to another is the same within each node in an EMOD simulation. However, we know that some people may be at greater risk based on their behavior, environment, or biology. The Heterogeneous Intra-Node Transmission (HINT) feature enables you to specify values for heterogeneity in transmission based on individual properties in a WAIFW matrix. For detailed information, see Property-based heterogeneous disease transmission (HINT).

EMOD scenarios > Scenarios > Generic > HINT_AgeAndAccess illustrates a common scenario in which the population has properties applied based on age and accessibility. Transmission is higher among individuals of similar ages and accessibility levels. Interventions are targeted based on these properties.

EMOD scenarios > Scenarios > Generic > HINT_SeattleCommuting illustrates a more novel scenario that uses the HINT feature. To simulate migration in EMOD, you can run multi-node simulations where individuals have a certain probability of migrating to a different node at each timestep (typically set to one day). This works well when individuals move to another location permanently or seasonally. However, for daily movement like commuting for work, you can use HINT to make transmission higher for people in the same area during the workday. This scenario assigns individual properties that represent the area codes surrounding Seattle and configures higher transmission for people in the same area code.

Disease reservoirs

Generally with EMOD, you model a disease by introducing an outbreak at some point in the simulation. However, endemic settings may involve a non-human disease reservoir that periodically reintroduces the disease to the human population. The EMOD scenarios > Scenarios > Generic > Zoonosis scenario illustrates how to configure EMOD to simulate such zoonotic diseases. For detailed information, see Disease outbreaks, reservoirs, and endemicity.

Interventions

The EMOD scenarios > Scenarios > Generic > Vaccinations scenario introduces the concept of adding interventions to stop disease transmission. It compares the outcomes when a disease outbreak has no interventions applied, when the entire population is vaccinated, and when you target the vaccination to a particular segment of the population.

Because interventions are often very specific to the disease being modeled, the other simulation types introduce diagnostic tests, drugs, and health system elements relevant to that disease. Therefore, the scenarios for the disease-specific simulation types provide more examples of intervention configuration. For detailed information, see Creating campaigns.

Vector model scenarios

The EMOD vector model is explained in detail in Vector model overview. While the various components that comprise the model are explained with examples, it may be more useful to learn the model through hands-on implementation. The following sections will introduce sets of example files that illustrate how the vector model works on particular topics. All files are available in a downloadable EMOD scenarios zip file and, in addition to the explanations below, each scenario will have a more detailed README file to cover relevant information.

For more information on the software architecture and inheritance, see Overview of EMOD software.

The scenarios included for learning the vector model can be broken down into two categories: “baseline simulation scenarios,” which focus on the configuration file, and “scenarios with campaigns,” which introduce the use of a campaign file. All scenarios utilize vector-based transmission. Note that climate is an important aspect of vector-based transmission, so every scenario will utilize climate files in addition to demographic files.

Baseline simulation scenarios

These scenarios introduce users to the basic structure of the model, including how climate is important in vector-based transmission models. There are no campaign interventions included for the baseline scenarios.

Simple vector model

This scenario introduces users to the VECTOR_SIM, which utilizes vector-based disease transmission. In this simulation type, parameters for multiple vector species can be configured. The purpose of this scenario is to familiarize users with the vector model, and to introduce parameters that are species-specific to the included vectors. The scenario includes three species of mosquitoes, but does not include any campaign interventions.

Impact of climate on seasonality

While climate is used in every vector simulation type, this scenario explores how differences in climate impact vector-borne disease transmission. The simulation utilizes the VECTOR_SIM to show how transmission is impacted differentially in three locations that vary in their climate. The base configuration is the same for the different locales, yet vector populations will vary drastically due to temperature, humidity, and rainfall. The purpose of this scenario is to help familiarize the user with the importance of accurate climate files, as they have a large impact on the seasonality of disease transmission.

Scenarios with campaigns

The baseline scenarios are meant to familiarize the user with basic model functionality and setup. The scenarios in this section build upon the concepts previously demonstrated, and add in the use of campaign interventions. Each scenario below includes a campaign file, and each will demonstrate the functionality of various interventions.

Bednet distribution

Bednets are commonly used to protect individuals from vector-borne diseases; in this scenario, insecticide-treated nets (ITN) are introduced as an intervention. These nets, also known as bednets, are a commonly used and effective strategy to prevent disease transmission. The purpose of this scenario is twofold: first, the use of campaign files is introduced, and second, the use of bednets as an intervention is demonstrated.

Bednets and acquisition-blocking vaccines

This scenario builds upon the “Bednet distribution” scenario by combining multiple interventions. The same configuration file is used, and bednets are included; in addition to the bednets, an acquisition-blocking vaccine is included in the campaign file. The purpose of this scenario is to familiarize users with campaign files that utilize multiple strategies. Eradication programs typically rely on combinations of interventions, not a single intervention in isolation. Therefore, it is important for modelers to be familiar with creating such complex strategies.

Multiple interventions: Bednets, IRS, acquisition- and transmission-blocking vaccines

This scenario builds upon the concept of complex strategies that was introduced in the “Bednets and acquisition-blocking vaccines” scenario by using a combination of four interventions. The campaign file includes the bednets and acquisition-blocking vaccines that were previously introduced, and adds in indoor residual spraying (IRS) and transmission-blocking vaccines. The purpose of this scenario is to both introduce a different type of vaccine, and to demonstrate that campaign files can be layered with numerous interventions to help recreate specific situations.

Sugar-baited traps

Previous scenarios utilized individual-level interventions. This scenario introduces a node-targeted intervention. These sugar-baited traps are applied at the node instead of distributed to individuals, and are used to collect host-seeking mosquitoes. The purpose of this scenario is to familiarize the user with node-level interventions (here, sugar traps). An interesting aspect of this intervention is that the efficacy can be modified by mosquito feeding behavior and how frequently they seek sugar meals. This behavior can be configured in the configuration file.

Insect-killing fence

As with the “Sugar-baited traps” scenario, this scenario utilizes a node-targeted intervention, insect-killing fences. There are several types of fences, such as photonic fences that kill mosquitoes using lasers. These fences can work to kill mosquitoes outdoors when they are either on their way into a house to seek a blood meal, or on their way out of the house to oviposit. The purpose of this scenario is to familiarize the user with node-level interventions (here, insect-killing fences).

Linear combination vector habitat

This scenario provides more detail on how to realistically configure larval habitat for mosquitoes. Previous scenarios utilized single habitat types for the mosquitoes in the model; here, multiple habitat types can be configured for each species. As mosquito species will typically have a predominant habitat type, but opportunistically utilize other available habitat types, this is an especially important feature of the model. The purpose of this scenario is to illustrate how to configure multiple habitat types for a single vector species.

Malaria model scenarios

The EMOD malaria model is explained in detail in Malaria model and the vector model it is built upon is described in Vector model overview. While the various components that comprise the model are explained with examples, it may be more useful to learn the model through hands-on implementation. The following sections will introduce sets of example files that illustrate how the malaria model works on particular topics. All files are available in a downloadable EMOD scenarios zip file and, in addition to the explanations below, each scenario will have a more detailed README file to cover relevant information.

For more information on the software architecture and inheritance, see Overview of EMOD software.

Full malaria model

This scenario introduces users to the MALARIA_SIM, which builds upon the vector-transmission of the VECTOR_SIM and adds malaria-specific parameters. Specifically, users can configure malaria drug parameters, and disease parameters are specific to malaria (instead of having a more generic form). The purpose of this scenario is to introduce the user to the model features that are unique to the malaria model, and to demonstrate how they contribute to facilitate understanding of malaria epidemiology.

Garki project retrospective: IRS and MDA

This scenario uses a historical data set from the The Garki Project to examine how indoor residual spraying (IRS) and mass drug administration (MDA) impact malaria transmission. Previous scenarios introduced IRS, but utilized vaccination campaigns instead of MDA. To use MDA, the simulation type will be the MALARIA_SIM, and the configuration file will contain parameters to set the efficacy of specific drugs. As with generic vaccinations, the campaign file will then be used to distribute the vaccines to the desired portion of the population. The purpose of this scenario is twofold: first, it will allow the user to explore the Garki dataset, and second, it will introduce a new intervention (MDA).

Troubleshooting EMOD simulations

If you encounter any of the following problems when attempting to run EMOD simulations, see the information below to resolve the issue.

If you need assistance, you can contact support for help with solving issues. You can contact Institute for Disease Modeling (IDM) support at support@idmod.org. When submitting the issue, please include any error information. See Debugging and testing for troubleshooting issues when attempting to build Eradication.exe or Eradication binary for Linux.

Exceptions

Whenever EMOD encounters an error condition while running a simulation, it should throw an exception. These exceptions are designed to help you diagnose any problems so that you can quickly resolve the issue. You can find the exceptions code in the utils directory of the EMOD source code within the files Exceptions.h and Exceptions.cpp.

Each exception will return, at a minimum, the following information:

  • Exception type caught

  • The filename where the exception occurred

  • The line number in the file where the exception was thrown (which may not be exactly where the error actually occurred in the code)

  • The function name where the exception was thrown

Specific exceptions may also return additional information in a message format. This message (msg) may contain variable names or the values of those variables, the name of a file that wasn’t found, and other informational text regarding the nature of the problem. For example, a file not found exception (FileNotFoundException) might look similar to the following:

00:00:00 [0] [E] [Eradication] FileNotFoundException caught: msg = Could not find
file ./Namawala_single_node_demographics.compiled.json, filename = NodeDemographics.cpp,
lineNumber = 227
BadEnumInSwitchStatementException

This exception is thrown when an enumeration value is not handled in the switch statement. In other words, this exception signals that there is a problem with an enumeration value, typically stemming from one of the configuration files, though this is not always the case.

It is possible the enumeration value is not a valid value (out of range of the numeric range of the enumeration), or perhaps the string value is currently not implemented in the code (and should not be used in configuration settings yet).

BadMapKeyException

This exception is thrown when there is a standard template library (STL) mapping error. Usually, this occurs where spatial output channel names are specified in the configuration file if an unrecognized channel name is used.

If you have not modified the EMOD source code or used an unrecognized channel name, this error could signal an internal problem with the code. Contact support@idmod.org.

CalculatedValueOutOfRangeException

EMOD performs a large number of mathematical operations on parameter values. Therefore, it is possible that, despite original parameter values being with range, the values resulting from these multiple calculations may end up outside its valid range. For example, a probability value (range: 0 to 1.0) that after multiple calculations during a simulation now exceeds 1.0.

This exception is thrown when such a situation is detected. This exception only applies to numeric or Boolean values, not enumeration values.

ConfigurationRangeException

This exception is thrown if a parameter value read from a configuration file is detected to be outside its valid range. This exception is similar to the more general case OutOfRangeException. However, this exception is only thrown if the out of range exception comes from a numeric or Boolean value, not enumeration value, read from a configuration file.

DllLoadingException

The EMOD architecture is modularized and can be built as a core Eradication.exe along with a series of custom reporter EMODules, as opposed to a single monolithic Eradication.exe. This exception indicates that EMOD couldn’t load on of the EMODule DLLs.

This situation could occur for several reasons:

  • EMOD couldn’t find the EMODule

  • Unresolved symbols were found (Windows)

  • EMOD could not find the necessary symbol during the GetProcAddress call

  • The custom reporter EMODules were built using a version of Visual Studio that is no longer supported. Rebuild the EMODules using Visual Studio 2022 (Professional, Premium, or Ultimate).

FactoryCreateFromJsonException

EMOD implements class factories that instantiate objects at run time and use information from JSON- based configuration information in the creation of these objects. The exception indicates something is incorrect with the JSON information.

In particular, in some cases, the JSON information is nested into a hierarchy of information. Therefore, as the factories are called to create the objects described by the outer layers of one of these nested hierarchies, the factories do not have any knowledge yet of the inner layers of the hierarchies. This inner information contains information the factory needs to complete the object instantiation, but this information might not be correct. If that happens, then the factory will throw this exception.

Campaign files often have this kind of nested hierarchical structure, so it’s important to t verify that the hierarchy is set up correctly. For example, if the class name were mistyped and EMOD had no implementation of that class, this exception will be thrown.

FileIOException

This exception is generated if there is an unrecoverable problem loading data from a file. The data might be corrupted or there may be a mismatch. For example, if the file format or configuration information indicates that there should be ten values of some array and there are only nine included in the file, then this exception would be thrown.

This exception is not the same as the exception thrown for a file that is not found. In this case, the file is found and loaded, but there is a problem with the data in the file.

FileNotFoundException

This exception is thrown if a file cannot be found. Possible causes might include a incorrectly typed filename in the configuration file, a wrong path to the file, or even the path not being set in the system environment leading to the system not finding a relative path to the file. One of the most likely causes is that quotes are missing around the file name.

GeneralConfigurationException

This exception is only thrown if a more specific exception cannot be used for the configuration problem detected. This exception is likely thrown when there is very little information available about the root problem.

For example, this exception might be thrown if a parameter name is invalid, such as using an older, deprecated version of a parameter name.

IllegalOperationException

This exception is thrown if an illegal operation was detected. In most cases, a more specific exception will be thrown rather than this more general one. This exception is likely thrown when there is very little information available about the root problem. For example, when a utility function error is detected, there is very sparse information available as to what may have led to the error. As a result, calling a more specific exception with more context is not an option.

IncoherentConfigurationException

This exception is thrown if mutually contradictory or incompatible configuration settings have been detected. For example, if mutually exclusive parameters are set, the minimum parameter value is greater than the maximum value, or two distribution axes are specified in a demographics file but there is a mismatch with the number of axes scale factors included. The exception can also occur if there isn’t a corresponding mapping between an reference ID in the metadata of a demographics file and its corresponding data file.

InitializationException

This exception is thrown if a problem with initialization was detected. In most cases, a more specific exception will be thrown rather than this more general one. This exception is likely thrown when there is very little information available about the root problem.

For example, if the very first part of a JSON file has corrupted or badly formatted data, this exception may be thrown instead of the more expected file input/output exception, FileIOException.

InvalidInputDataException

This exception is thrown when a problem with an input file is detected. For example, if the wrong data type was detected, such as a float being detected when a string is expected you would see this exception thrown, or even, if a parameter has an invalid value even if the value is of the correct type. As the input file most likely to have significant modifications, verify that the demographics file is set up correctly.

MissingParameterFromConfigurationException

This exception occurs when required parameters are missing. Verify that you are not using deprecated parameters and that all required parameters are specified (or set Use_Defaults to 1).

MPIException

This exception is thrown if there is an MPI error. As such, these types of issues are related to interfacing with MPI (and/or networking issues) and do not necessary imply something wrong with the EMOD code or JSON files.

NotYetImplementedException

This exception is thrown if an attempt is made to execute code that is not yet implemented. For example, there are areas of EMOD where placeholder enumeration values are defined but not yet implemented. If you specify a value like this, it is considered within a valid range, but this exception will be thrown in response. Verify that any enumeration values use one of the available values as described in the documentation and do not contain any typos.

NullPointerException

This exception is thrown when a NULL pointer is detected in the code, or rather when a NULL pointer - that should NOT be NULL - is used. When thrown at the application level, a NULL pointer exception is usually caused by some sort of initialization error, for example, a file not being found.

As a result, in most cases, a more specific exception will be thrown before the code execution reaches a point where this exception would occur. Therefore, this exception is uncommon and likely thrown only when there is very little information available about the root problem.

OutOfRangeException

This exception is thrown when a numeric or Boolean value is out of range. For example, if you index an array outside of its valid range, this exception will be thrown. There are other situations where more specific exceptions are thrown instead of this more general one. For example, when the numeric or Boolean values are from a configuration file, but are detected to be out of range, the ConfigurationRangeException is thrown. Likewise, if the value goes out of range as the result of a calculation, the CalculatedValueOutOfRangeException is thrown instead.

QueryInterfaceException

The EMOD architecture is modularized and many components now implement QueryInterface. This exception is thrown when a required interface is queried on an object and the object that returns that the interface is not supported.

If you have not modified the EMOD source code and receive this error, it could signal an internal problem with the code. Contact support@idmod.org.

SerializationException

This exception is thrown when there is a serialization (or de-serialization) issue. For example, if data is being passed over the network (MPI) and the connection drops, then the serialization fails and this exception is thrown.

CentOS environment

The following problems are specific to running simulations using the Eradication binary for Linux on CentOS 7.1.

Regression test graphs differ when run on CentOS

After you run regression simulations on CentOS using runemod.sh in the Scripts directory, it plots graphs from the simulation output data with a red line for the reference output and a blue line for the new output. The reference output was created by running the simulation on Windows, which in some cases may be slightly different than the output from CentOS.

For simulations that plot a baseline, you can override the Windows reference output by modifying runemod.sh to use output/InsetChart.linux.json as the output location. In that case, the red reference plots should always be completely covered by the blue plots.

Eradication binary for Linux cannot locate the input files

If you chose not to have the PrepareLinuxEnvironment.sh script download the EMOD source code and input files, you need to set up the environment variable, path and symlink that are needed to run simulations on CentOS. See Install EMOD on Linux.

Model overview

Disease models play an important role in understanding and managing the transmission dynamics of various pathogens. We can use them to describe the spatial and temporal patterns of disease prevalence, as well as to explore or better understand the factors that influence infection incidence. Modeling is a key step in understanding what treatments and interventions can be most effective, how cost-effective these approaches may be, and what specific factors need to be considered when trying to eradicate disease. The findings can be used to guide policy for implementing practical real-world solutions.

To understand the complex dynamics underlying disease transmission, epidemiologists often use a set of models called compartmental models. Developed in the early 20th century, these models stratify a population into groups, generally based on their risk or infection status. Underlying these models is a system of differential equations that track the number of people in each category over time. If you would like a more in-depth introduction to epidemiology and disease modeling, you may want to take the Epidemics course from The Pennsylvania State University through Coursera.

Epidemiological MODeling software (EMOD) is an agent-based model (ABM), another powerful tool used to help understand the complexity inherent in disease transmission systems. EMOD is a discrete time, Monte Carlo method simulator that simulates the simultaneous interactions of agents in an effort to recreate complex phenomena. Each agent (such as a human or vector) can be assigned a variety of properties (for example, age, gender, etc.), and their behavior and interactions with one another are determined using decision rules. EMOD and other ABMs must be run many times to produce probability distributions of potential outcomes, better capturing uncertainty than compartmental models.

These models have strong predictive power and are able to leverage spatial and temporal dynamics. Further, complex environments can be developed in which the agents act, and agents may “learn” from interactions or “adapt” to their environment. As a result, ABMs are excellent for identifying emerging properties of the system: patterns that are not explicitly modeled, but instead occur as a consequence of the rules that govern the agents.

EMOD can be calibrated to particular geographic locations, and the microsolver framework enables the model’s functionality to be highly modifiable. Further, the framework includes the ability to add intervention campaigns, and those interventions can be specified to target particular populations or sub-populations of human or vector groups. This intervention targeting is capable of simulating complex cascade of care systems, such as those used in the treatment of HIV. EMOD is very useful for determining the best intervention strategies to use in particular areas for burden reduction or elimination.

EMOD supports different simulation types for various diseases and transmission types. EMOD uses a layered architecture in which the base functionality is contained in the generic model and inherited by the transmission-level models that in turn are inherited by specific disease models. Because the EMOD modeling software can simulate all of these diseases, each “model” is more accurately referred to as a simulation type. For more information on the software architecture and inheritance, see Overview of EMOD software.

_images/simulation-types.png

Simulation type inheritance

The generic model can be configured to simulate a variety of different diseases, such as measles or influenza. Support for other transmission mechanisms, such as sexual networks or vector transmission, is provided by the transmission-level models. These can be configured to model diseases we do not specifically support. For example, IDM collaborators have used the vector model to simulation the transmission of dengue and chikungunya. Finally, there are models for specific diseases that include logic for disease biology and treatment. Because of this inheritance, we recommend reviewing the documentation for the generic model and the transmission-level model relevant to your disease simulation. The fundamentals for configuring outbreaks, heterogeneity, and interventions are the same across all simulation types.

Generic model overview

The EMOD generic model forms the foundation for all other disease models in EMOD. It provides the fundamental logic for contact-based disease transmission and interventions aimed at controlling the spread of disease that are targeted to individuals or geographic nodes. You can easily add heterogeneity to your simulation by configuring he infectivity of the disease, susceptibility of individuals, and more. The generic model does not contain logic for disease biology, but it can be configured to model a variety of diseases, such as influenza, Ebola, measles, and more. To use the generic model, set the configuration parameter Simulation_Type to GENERIC_SIM.

The figure below demonstrates the main components of the generic EMOD simulation type. Individuals reside in geographic nodes and can migrate from one node to another. Infected individuals shed contagion into a pool that can infect susceptible individuals. You can assign properties to individuals and nodes to vary how transmission or interventions occur.

_images/GenericModel_Flowchart.png

In the generic model, you can add heterogeneous transmission in a population using the Heterogeneous Intra-Node Transmission (HINT) feature. Using this feature, you assign different properties to individuals in a population and differentially scale the infectivity from one group to another. For example, you might have high infectivity between schoolchildren and lower infectivity from the elderly to children. For more information, see Property-based heterogeneous disease transmission (HINT). This feature is not available in most other disease models, where heterogeneity in transmission is handled more mechanistically. For example, the malaria model simulates parasite density, mosquito populations, and biting frequency.

If you are familiar with compartmental models but new to agent-based models, we recommend reviewing the Compartmental models and EMOD section, which compares the mathematics of the differential equations that govern compartmental models with the mathematics of the EMOD agent-based model. It describes how to configure EMOD to model diseases like SI, SIR, or SEIR models. Additionally, review Tutorials and simulation examples for hands-on experience running EMOD simulations.

The configuration of the model regarding infectivity, immune response, and other qualities is handled via several JSON (JavaScript Object Notation) files. For more information, see Overview of EMOD software.

Compartmental models and EMOD

This section describes the common compartmental models, the ordinary differential equations governing them, and how to configure EMOD to model similar disease scenarios. The simplest way to model epidemic spread in populations is to classify people into different population groups or compartments. Compartmental models are governed by a system of differential equations that track the population as a function of time, stratifying it into a different groups based on risk or infection status. The models track the number of people in each of the following categories:

Susceptible

Individual is able to become infected.

Exposed

Individual has been infected with a pathogen, but due to the pathogen’s incubation period, is not yet infectious.

Infectious

Individual is infected with a pathogen and is capable of transmitting the pathogen to others.

Recovered

Individual is either no longer infectious or “removed” from the population.

Different diseases are represented by different compartmental models. For example, a disease without an incubation period is represented by an SIR model and a disease that has lifelong infectiousness is represented by an SI model. Each of these models are discussed in more detail in the topics in this section.

Compartmental models are deterministic, that is, given the same inputs, they produce the same results every time. They are able to predict the various properties of pathogen spread, can estimate the duration of epidemics, and can be used to understand how different situations or interventions can impact the outcome of pathogen spread.

Agent-based models like EMOD model individual agents (such as humans or vectors) and are extensively used in epidemiology due to their predictive power in modeling the spread (or conversely, control) of epidemics. A popular type of ABM for this is one in which each agent’s rules follow the dynamics specified in the compartmental models, where each agent flows through the compartments as a function of both “within-host” rules (such as duration of infection) and interactions between agents (such as becoming infected when coming into contact with an infectious agent).

By combining the epidemiological basis of compartmental models with the flexibility of an agent- based model, this type of ABM is quite powerful due to their ability to simultaneously address the ecology, epidemiology, and pathology of complex systems. In addition, EMOD is a stochastic model that better captures the randomness involved in real-world disease transmission. Therefore, you must run multiple simulations and and then run statistical analyses of the output to achieve valid predictions.

Detailed model comparison

Because the SIR model is the most commonly-used compartmental model from which many others are derived, it is used for the detailed comparison between compartmental models and the EMOD agent- based model. However, the principles can be extended to all compartmental models discussed in this section.

Compartmental models

The SIR/SIRS diagram below shows how individuals move through each compartment in the model. The dashed line shows how the SIR model becomes an SIRS (Susceptible - Infectious - Recovered - Susceptible) model, where recovery does not confer lifelong immunity, and individuals may become susceptible again.

_images/SIR-SIRS.png

SIR - SIRS model

The infectious rate β controls the rate of spread, which represents the probability of transmitting disease between a susceptible and an infectious individual. Recovery rate, γ = 1/D, is determined by the average duration, D, of infection. Including births and deaths (where the rates are equal), the SIR model can be written as the following ordinary differential equation (ODE):

\[\begin{split}\begin{aligned} \frac{dS}{dt} & = \mu N -\frac{\beta S I}{N} - \nu S\\ \frac{dI}{dt} & = \frac{\beta S I}{N} - \gamma I - \nu I\\ \frac{dR}{dt} & = \gamma I - \nu R \end{aligned}\end{split}\]

with \(N = S + I + R\) and \(\mu\) is the birth rate and \(\nu\) is the death rate.

It is challenging to derive exact analytical solutions of the previous equations because of the non- linear dynamics. However, the key metrics that control the spread can be derived. At the initial seeding of the infection, the following condition needs to be satisfied for a disease to spread:

\[\frac{dI}{dt} = \frac{\beta SI}{N} - \gamma I > 0\]

If the number of infections at the initial stage is small then S is close to N and the condition becomes:

\[\frac{\beta}{\gamma} > 1\]

where \(\frac{\beta}{\gamma}\) is named the reproductive number (R0). R0 is the average number of secondary cases generated by an index case in a fully susceptible population. The disease will spread in the population when R0 > 1 and will die out if R0 < 1. This is true for all compartmental models.

EMOD model

The EMOD model is a discrete and stochastic version of the SIR model with state changes occurring at fixed time steps and an exponentially distributed duration of infection. Because transmission is an inherently stochastic process that unfolds in a population of finite size, this is preferable though you cannot precisely replicate the deterministic and continuous dynamics of an ODE model. Because EMOD represents individuals, and to be clear about mechanisms by which the transmission rate varies as a function of the node population, we instead present the dynamics in terms of the number of individuals (X, Y ,Z) in place of (S, I, R), respectively. The discrete form of the previous equation at each time step can be written as:

\[\begin{split}\begin{aligned} X(t+\delta) & = X(t) \mu N - \delta\frac{\beta X(t)Y(t)}{N} - \nu X(t)\\ Y(t+\delta) & = Y(t) + \delta\frac{\beta X(t)Y(t)}{N} - \delta\gamma Y(t) - \nu Y(t)\\ Z(t+\delta) & = Z(t) +\delta\gamma Y(t) - \nu Z(t) \end{aligned}\end{split}\]

In EMOD, the state changes at fixed time steps. The size of the time step, denoted \(\delta\), is selected to be small compared to the characteristic timescale of the disease dynamics. By default, \(\delta\) = 1, which is one day but you can choose a smaller time step in order to get more accurate results. The infection and recovery process can be represented as probabilistic binomial draws where each update step consists of three primary sub-steps:

  1. Shed: The default behavior in a homogeneous generic simulation is for each infected individual to shed contagion at a fixed rate. The total rate of contagion shedding from all infected individuals is called the force of infection, \(\lambda = \frac{\beta I}{N}\).

  2. Expose: Each susceptible individual becomes infected with probability \(p_i = 1-\mathrm{e}^{\lambda\delta}\), where \(\delta\) is the time step.

  3. Finalize: The final part of each time step contains recovery from infection and disease mortality for infected individuals, along with basic demographic updates. EMOD supports advanced demographics, but here we use the per-capita birth and death rates.

    1. Recovery: Each infected individual recovers in one time step with probability, \(p_r = 1 - \text{exp}(-\gamma\delta)\).

    2. Birth: For birthrate \(\mu\), the number of new susceptible individuals will be Poisson distributed with rate \(p_b = \mu N\delta\).

    3. Natural Death: The probability of death for each individual is \(p_d = 1 - \text{exp}(-\nu\delta)\).

Putting these pieces together over the course of a time step, let:

\[\begin{split}\Delta{N_i} & \sim \text{Binomial}(X,p_i) \qquad [\text{Infection}] \\ \Delta{N_r} & \sim \text{Binomial}(Y,p_r) \qquad [\text{Recovery}] \\ \Delta{N_b} & \sim \text{Binomial}(\mu N\delta) \qquad [\text{Birth}] \\ \Delta{N_{X,d}} & \sim \text{Binomial}(X,p_d) \qquad [\text{Death of susceptibles}] \\ \Delta{N_{Y,d}} & \sim \text{Binomial}(Y,p_d) \qquad [\text{Death of infected}] \\ \Delta{N_{Z,d}} & \sim \text{Binomial}(Z,p_d) \qquad [\text{Death of recovered}]\end{split}\]

With these numbers in mind and assuming that only one state transition event happens to each individual in a time step:

\[\begin{split}\begin{aligned} X(t + \delta) &= X(t) - \Delta{N_i} + \Delta{N_b} - \Delta{N_{X,d}} \\ Y(t + \delta) &= Y(t) + \Delta{N_i} - \Delta{N_r} - \Delta{N_{Y,d}} \\ Z(t + \delta) &= Z(t) + \Delta{N_r} - \Delta{N_{Z,d}} \\ t &= t + \delta \end{aligned}\end{split}\]

One of the key differences between stochastic and deterministic systems is the value of R0. An ODE model will never predict an outbreak when R0 < 1, especially when R0 is close to 1. In stochastic simulations it is possible to see outbreaks.

Additionally, the EMOD model is individual-based, which allows the implementation of flexible distributions. In an ODE model the time constants are exponentially distributed, however, this is not the case for some diseases. You can test the effects of different distributions in the EMOD executable by changing Infectious_Period_Distribution in the configuration file. A fixed duration will result in a much faster spread of disease.

SIR and SIRS models

This topic describes the differential equations that govern the classic deterministic SIR and SIRS compartmental models and describes how to configure EMOD, an agent-based stochastic model, to simulate an SIR/SIRS epidemic. In SIR models, individuals in the recovered state gain total immunity to the pathogen; in SIRS models, that immunity wanes over time and individuals can become reinfected. The EMOD generic simulation uses an SEIR-like disease model by default. You can modify the default SEIR model to an SIR model by turning off the incubation period.

The SIR/SIRS diagram below shows how individuals move through each compartment in the model. The dashed line shows how the SIR model becomes an SIRS (Susceptible - Infectious - Recovered - Susceptible) model, where recovery does not confer lifelong immunity, and individuals may become susceptible again.

_images/SIR-SIRS.png

SIR - SIRS model

The infectious rate, \(\beta\), controls the rate of spread which represents the probability of transmitting disease between a susceptible and an infectious individual. Recovery rate, \(\gamma\) = 1/D, is determined by the average duration, D, of infection. For the SIRS model, \(\xi\) is the rate which recovered individuals return to the susceptible statue due to loss of immunity.

For a detailed comparison of how the SIR ordinary differential equation (ODE) can be rewritten in the stochastic EMOD model, see Compartmental models and EMOD.

SIR model

The SIR model was first used by Kermack and McKendrick in 1927 and has subsequently been applied to a variety of diseases, especially airborne childhood diseases with lifelong immunity upon recovery, such as measles, mumps, rubella, and pertussis. S, I and R represent the number of susceptible, infected, and recovered individuals, and N = S + I + R is the total population.

SIR without vital dynamics

If the course of the infection is short (emergent outbreak) compared with the lifetime of an individual and the disease is non-fatal, vital dynamics (birth and death) can be ignored. In the deterministic form, the SIR model can be written as the following ordinary differential equation (ODE):

\[\begin{split}\begin{aligned} \frac{dS}{dt} & = -\frac{\beta SI}{N}\\ \frac{dI}{dt} & = \frac{\beta SI}{N} - \gamma I\\ \frac{dR}{dt} & = \gamma I \end{aligned}\end{split}\]

where \(N = S + I + R\) is the total population.

In a closed population with no vital dynamics, an epidemic will eventually die out due to an insufficient number of susceptible individuals to sustain the disease. Infected individuals who are added later will not start another epidemic due to the lifelong immunity of the existing population.

The following graphs show the inset chart and charts for all channels in a typical SIR outbreak. To run this example simulation, see the Generic/SIR scenario in the downloadable EMOD scenarios zip file. Review the README files there for more information.

_images/SIR_InsetChart_default.png

Figure 1: Growth of infection and depletion of the susceptible population in an SIR outbreak

_images/SIR_AllCharts_default.png

Figure 2: All output channels for SIR without vital dynamics

SIR with vital dynamics

However in a population with vital dynamics, new births can provide more susceptible individuals to the population, sustaining an epidemic or allowing new introductions to spread throughout the population. In a realistic population like this, disease dynamics will reach a steady state. This is the case when diseases are endemic to a region.

Let \(\mu\) and \(\nu\) represent the birth and death rates, respectively, for the model. To maintain a constant population, assume that \(\mu = \nu\). In steady state \(\frac{dI}{dt} = 0\). The ODE then becomes:

\[\begin{split}\begin{aligned} \frac{dS}{dt} & = \mu N -\frac{\beta S I}{N} - \nu S\\ \frac{dI}{dt} & = \frac{\beta S I}{N} - \gamma I - \nu I\\ \frac{dR}{dt} & = \gamma I - \nu R \end{aligned}\end{split}\]

where \(N = S + I + R\) is the total population.

SIRS model

The SIR model assumes people carry lifelong immunity to a disease upon recovery; this is the case for a variety of diseases. For another class of airborne diseases, for example seasonal influenza, an individual’s immunity may wane over time. In this case, the SIRS model is used to allow recovered individuals to return to a susceptible state.

SIRS without vital dynamics

If there is sufficient influx to the susceptible population, at equilibrium the dynamics will be in an endemic state with damped oscillation. The ODE then becomes:

\[\begin{split}\begin{aligned} \frac{dS}{dt} & = -\frac{\beta SI}{N} + \xi R\\ \frac{dI}{dt} & = \frac{\beta SI}{N} - \gamma I\\ \frac{dR}{dt} & = \gamma I - \xi R \end{aligned}\end{split}\]

where \(N = S + I + R\) is the total population.

EMOD simulates waning immunity by a delayed exponential distribution. Individuals stay immune for a certain period of time then immunity wanes following an exponential distribution. For more information, see Immunity parameters.

The graphs below show damped oscillation due to people losing immunity and becoming susceptible again.

Note

Individuals who are susceptible people due to waning immunity are not classified as susceptible in the simulation. They are reported under the “Waning Population” channel.

_images/SIRS_InsetChart_default.png

Figure 3: Damped oscillation in SIRS outbreak

_images/SIRS_AllCharts_default.png

Figure 4: All output channels for SIRS without vital dynamics

To run this example simulation, see the Generic/SIRS scenario in the EMOD scenarios zip file. Review the README files there for more information.

SIRS with vital dynamics

You can also add vital dynamics to an SIRS model, where \(\mu\) and \(\nu\) again represent the birth and death rates, respectively. To maintain a constant population, assume that \(\mu = \nu\). In steady state \(\frac{dI}{dt} = 0\). The ODE then becomes:

\[\begin{split}\frac{dS}{dt} & = \mu N -\frac{\beta S I}{N} + \xi R - \nu S\\ \frac{dI}{dt} & = \frac{\beta S I}{N} - \gamma I - \nu I\\ \frac{dR}{dt} & = \gamma I - \xi R - \nu R\end{split}\]

where \(N = S + I + R\) is the total population.

SEIR and SEIRS models

This topic describes the differential equations that govern the classic deterministic SEIR and SEIRS compartmental models and describes how to configure EMOD, an agent-based stochastic model, to simulate an SEIR/SEIRS epidemic. In this category of models, individuals experience a long incubation duration (the “exposed” category), such that the individual is infected but not yet infectious. For example chicken pox, and even vector-borne diseases such as dengue hemorrhagic fever have a long incubation duration where the individual cannot yet transmit the pathogen to others.

The SEIR/SEIRS diagram below shows how individuals move through each compartment in the model. The dashed line shows how the SEIR model becomes an SEIRS (Susceptible - Exposed - Infectious - Recovered - Susceptible) model, where recovered people may become susceptible again (recovery does not confer lifelong immunity). For example, rotovirus and malaria are diseases with long incubation durations, and where recovery only confers temporary immunity.

_images/SEIR-SEIRS.png

SEIR - SEIRS model

The infectious rate, \(\beta\), controls the rate of spread which represents the probability of transmitting disease between a susceptible and an infectious individual. The incubation rate, \(\sigma\), is the rate of latent individuals becoming infectious (average duration of incubation is 1/\(\sigma\)). Recovery rate, \(\gamma\) = 1/D, is determined by the average duration, D, of infection. For the SEIRS model, \(\xi\) is the rate which recovered individuals return to the susceptible state due to loss of immunity.

SEIR model

Many diseases have a latent phase during which the individual is infected but not yet infectious. This delay between the acquisition of infection and the infectious state can be incorporated within the SIR model by adding a latent/exposed population, E, and letting infected (but not yet infectious) individuals move from S to E and from E to I. For more information, see Incubation parameters.

SEIR without vital dynamics

In a closed population with no births or deaths, the SEIR model becomes:

\[\begin{split}\begin{aligned} \frac{dS}{dt} & = -\frac{\beta SI}{N}\\ \frac{dE}{dt} & = \frac{\beta SI}{N} - \sigma E\\ \frac{dI}{dt} & = \sigma E - \gamma I\\ \frac{dR}{dt} & = \gamma I \end{aligned}\end{split}\]

where \(N = S + E + I + R\) is the total population.

Since the latency delays the start of the individual’s infectious period, the secondary spread from an infected individual will occur at a later time compared with an SIR model, which has no latency. Therefore, including a longer latency period will result in slower initial growth of the outbreak. However, since the model does not include mortality, the basic reproductive number, R0 = \(\beta/\gamma\), does not change.

The complete course of outbreak is observed. After the initial fast growth, the epidemic depletes the susceptible population. Eventually the virus cannot find enough new susceptible people and dies out. Introducing the incubation period does not change the cumulative number of infected individuals.

The following graphs show the inset chart and charts for all channels in a couple typical SEIR outbreaks, one with an incubation period of 8 days and one with an incubation period of 2 days. Notice how the outbreak depletes the susceptible population more quickly when the incubation period is shorter but that the cumulative infections remains the same. To run this example simulation, see the Generic/SEIR scenario in the downloadable EMOD scenarios zip file. Review the README files there for more information.

_images/SEIR_InsetChart_default.png

Figure 1: SEIR epidemic course for 8-day incubation period

_images/SEIR_AllCharts_default.png

Figure 2: All output channels for SEIR 8-day incubation period

_images/SEIR_InsetChart_2day.png

Figure 3: SEIR epidemic course for 2-day incubation period

_images/SEIR_AllCharts_2day.png

Figure 4: All output channels for SEIR 2-day incubation period

SEIR with vital dynamics

As with the SIR model, enabling vital dynamics (births and deaths) can sustain an epidemic or allow new introductions to spread because new births provide more susceptible individuals. In a realistic population like this, disease dynamics will reach a steady state. Where \(\mu\) and \(\nu\) represent the birth and death rates, respectively, and are assumed to be equal to maintain a constant population, the ODE then becomes:

\[\begin{split}\begin{aligned} \frac{dS}{dt} & = \mu N - \nu S - \frac{\beta SI}{N}\\ \frac{dE}{dt} & = \frac{\beta SI}{N} - \nu E - \sigma E\\ \frac{dI}{dt} & = \sigma E - \gamma I - \nu I\\ \frac{dR}{dt} & = \gamma I - \nu R \end{aligned}\end{split}\]

where \(N = S + E + I + R\) is the total population.

The following graphs show periodic reintroductions of an SEIR outbreak in a population with vital dynamics. To run this example simulation, see the Generic/SEIR_VitalDynamics scenario in the EMOD scenarios folder. Review the README files there for more information.

_images/SEIR_Vital_InsetChart_default.png

Figure 5: SEIR periodic outbreaks on reintroduction in a population with vital dynamics

_images/SEIR_Vital_AllCharts_default.png

Figure 6: All output channels for SEIR outbreaks

SEIRS model

The SEIR model assumes people carry lifelong immunity to a disease upon recovery, but for many diseases the immunity after infection wanes over time. In this case, the SEIRS model is used to allow recovered individuals to return to a susceptible state. Specifically, \(\xi\) is the rate which recovered individuals return to the susceptible statue due to loss of immunity. If there is sufficient influx to the susceptible population, at equilibrium the dynamics will be in an endemic state with damped oscillation. The SEIRS ODE is:

\[\begin{split}\begin{aligned} \frac{dS}{dt} & = -\frac{\beta SI}{N} + \xi R\\ \frac{dE}{dt} & = \frac{\beta SI}{N} - \sigma E\\ \frac{dI}{dt} & = \sigma E - \gamma I\\ \frac{dR}{dt} & = \gamma I - \xi R \end{aligned}\end{split}\]

where \(N = S + E + I + R\) is the total population.

EMOD simulates waning immunity by a delayed exponential distribution. Individuals stay immune for a certain period of time then immunity wanes following an exponential distribution. For more information, see Immunity parameters.

SEIRS with vital dynamics

You can also add vital dynamics to an SEIRS model, where \(\mu\) and \(\nu\) again represent the birth and death rates, respectively. To maintain a constant population, assume that \(\mu = \nu\). In steady state \(\frac{dI}{dt} = 0\). The ODE then becomes:

\[\begin{split}\frac{dS}{dt} & = \mu N - \frac{\beta S I}{N} + \xi R- \nu S\\ \frac{dE}{dt} & = \frac{\beta S I}{N} - \sigma E - \nu E\\ \frac{dI}{dt} & = \sigma E - \gamma I - \nu I\\ \frac{dR}{dt} & = \gamma I - \xi R - \nu R\end{split}\]

where \(N = S + E + I + R\) is the total population.

The following graphs show the complete trajectory of a fatal SEIRS outbreak: the disease endemicity due to vital process and waning immunity and the effect of vaccination campaigns that eradicate the outbreak after day 500. To run this example simulation, see the Generic/SEIRS scenario in the downloadable EMOD scenarios zip file. Review the README files there for more information. For more information on creating campaigns, see Creating campaigns.

_images/SEIRS_InsetChart_default.png

Figure 7: Trajectory of the SEIRS outbreak

_images/SEIRS_AllCharts_default.png

Figure 8: All output channels for SEIRS outbreak

SI and SIS models

This topic describes the differential equations that govern the classic deterministic SI and SIS compartmental models and describes how to configure EMOD, an agent-based stochastic model, to simulate an SI/SIS epidemic. In SI models, people never leave the infectious state and have lifelong infections. For example, herpes is a disease with lifelong infectiousness.

The SI/SIS diagram below shows how individuals move through each compartment in the model. The dashed line shows how the model becomes an SIS (Susceptible - Infectious - Susceptible) model, where infection does not confer immunity (or there is waning immunity). Individuals have repeat or reoccurring infections, and infected individuals return to the susceptible state. For example, sexually transmitted diseases such as gonorrhea or chlamydia fall into this group.

_images/SI-SIS.png

SI - SIS model

The infectious rate, \(\beta\), controls the rate of spread which represents the probability of transmitting disease between a susceptible and an infectious individual. Recovery rate, \(\gamma\) = 1/D, is determined by the average duration, D, of infection.

SI model

The SI model is the simplest form of all disease models. Individuals are born into the simulation with no immunity (susceptible). Once infected and with no treatment, individuals stay infected and infectious throughout their life, and remain in contact with the susceptible population. This model matches the behavior of diseases like cytomegalovirus (CMV) or herpes.

SI without vital dynamics

The dynamics of I in a SI model are also known as logistic growth. If there are no vital processes (birth and death), every susceptible will eventually become infected. The EMOD generic simulation uses an SEIR model by default. However, it can be modified to an SI model by turning off incubation and setting the infectious period to be longer than a human lifespan.

The SI model can be written as the following ordinary differential equation (ODE):

\[\begin{split}\begin{aligned} \frac{dS}{dt} & = -\frac{\beta SI}{N}\\ \frac{dI}{dt} & = \frac{\beta SI}{N} = \beta I \left(1-\frac{I}{N}\right) \end{aligned}\end{split}\]

where \(N = S + I\) is the total population.

The following graphs show the inset chart and charts for all channels in an SI simulation without vital dynamics. To run this example simulation, see the Generic/SI scenario in the downloadable EMOD scenarios folder and set Enable_Vital_Dynamics to 0. Review the README files there for more information.

_images/SI_InsetChart_vitaloff.png

Figure 1: SI outbreak showing logistic grown

_images/SI_AllCharts_vitaloff.png

Figure 2: All output channels for an SI outbreak

SI with vital dynamics

To add vital dynamics to a population, let \(\mu\) and \(\nu\) represent the birth and death rates, respectively, for the model. To maintain a constant population, assume that \(\mu = \nu\). Therefore, the ODE becomes:

\[\begin{split}\begin{aligned} \frac{dS}{dt} & = \mu N - \frac{\beta SI}{N} - \nu S\\ \frac{dI}{dt} & = \frac{\beta SI}{N} - \nu I \end{aligned}\end{split}\]

where \(N = S + I\) is the total population.

The final proportion of infected people is related to both the vital dynamics and \(\beta\). \(\beta\) can be calculated by looking at the steady state:

\[\mu = \nu = \frac{\beta S}{N} = \beta \left(1 - \frac{I}{N}\right)\]

The following graphs show the inset chart and charts for all channels in the Generic/SI scenario when vital dynamics remains on. The anticipated final epidemic size, \(I/N\), is 85%, and the birth and death rate equals 0.0000548 per day (2% per year). Therefore, \(\beta\) = 0.00003653.

_images/SI_InsetChart_default.png

Figure 3: SI outbreak approaching 85% infected population at steady state

_images/SI_AllCharts_default.png

Figure 4: All output channels for an SI outbreak with vital dynamics

SIS

Similar to the SIRS model, the infected individuals return to the susceptible state after infection. This model is appropriate for diseases that commonly have repeat infections, for example, the common cold (rhinoviruses) or sexually transmitted diseases like gonorrhea or chlamydia.

The generic simulation type uses an SEIR model by default. However, it can be modified to an SIS model by configuration no incubation period and no immunity. For more information, see Incubation and Immunity parameters.

SIS without vital dynamics

Because individuals remain susceptible after infection, the disease attains a steady state in a population, even without vital dynamics. The ODE for the SIS model without vital dynamics can be analytically solved to understand the disease dynamics. The ODE is as follows:

\[\begin{split}\begin{aligned} \frac{dS}{dt} & = -\frac{\beta SI}{N} + \gamma I\\ \frac{dI}{dt} & = \frac{\beta SI}{N} - \gamma I\\ \end{aligned}\end{split}\]

At equilibrium, solving:

\[\frac{dI}{dt} = \beta I \left(1-\frac{I}{N}\right) - \gamma I = 0\]

There are two equilibrium states for the SIS model, the first is \(I = 0\) (disease free state), and the second is:

\[I = \frac{(\beta - \gamma)N}{\beta} = \left(1-\frac{\gamma}{\beta}\right)N\]

For disease to spread, \(dI/dt > 0\). Therefore, similar to the previously described concept of the basic reproductive number, when \(\beta/\gamma > 1\), the disease will spread and approach the second steady state; otherwise, it will eventually reach the disease-free state.

The following graphs show the inset chart and charts for all channels in an SIS simulation without vital dynamics that eventually approaches steady state. You can compare the fraction of infected people with the anticipated value based on the previous calculation. If we have a reproductive number of 1.2, the infected fraction at equilibrium will be 1 - (1/1.2) ~ 17%.

To run this example simulation, see the Generic/SIS scenario in the EMOD scenarios zip file. Review the README files there for more information.

_images/SIS_InsetChart_default.png

Figure 5: SIS outbreak approaching 17% infected population at steady state

_images/SIS_AllCharts_default.png

Figure 6: All output channels for an SIS outbreak without vital dynamics

SIS with vital dynamics

To add vital dynamics to a population, let \(\mu\) and \(\nu\) represent the birth and death rates, respectively, for the model. To maintain a constant population, assume that \(\mu = \nu\). Therefore, the ODE becomes:

\[\begin{split}\frac{dS}{dt} & = \mu N -\frac{\beta S I}{N} + \gamma I - \nu S\\ \frac{dI}{dt} & = \frac{\beta S I}{N} - \gamma I - \nu I\end{split}\]
Disease outbreaks, reservoirs, and endemicity

To configure disease outbreaks in EMOD simulations, you have a few different options. Generally, you will configure an outbreak event to occur at some point during the simulation. For zoonotic diseases, you can also introduce the disease through an animal reservoir.

Outbreak events

Somewhat counter-intuitively, disease outbreaks are configured as “interventions” in the campaign file. While the demographics and configuration files specify consistent qualities of the population or disease, the campaign file is organized into events that occur during the course of the simulation. Most of these events trigger disease interventions such as vaccinations and diagnostic tests, but the outbreak is included here as well. Like other interventions, outbreaks are nested within event coordinators and events, which control when, where, and within which demographic the outbreak occurs. For more information, see Creating campaigns.

The two available classes for including a disease outbreak are:

OutbreakIndividual

This intervention infects existing individuals with a specified disease. You can configure the outbreak to occur in individuals that meet certain criteria, such as being high risk.

Outbreak

This intervention adds new infected individuals to a node or infects existing individuals. You cannot specify the demographics of these individuals because the outbreak is at the node level.

Because of the ability to target outbreaks to specific individuals, OutbreakIndividual is much more commonly used. However, you can use either type of outbreak for the scenarios described below.

Emergent outbreak

In a population where the disease is not endemic, you will generally include one outbreak in the campaign file. If the population is naive (no immunity), the disease will likely spread. If the population has immunity, it may not. Immunity may be due to vaccination or, if the disease was recently endemic, prior infection.

For an example of the effect of vaccination on herd immunity and the spread of an outbreak, see Creating campaigns.

Endemic disease

If the disease is endemic to a region, outbreaks can be periodically repeated to simulate regular occurrences of the disease in the population. There will likely be some level of immunity within the population when a disease is endemic.

Alternatively, you can run a simulation for a period of time until the disease dynamics reach a steady state and disregard simulation output prior to that point. This is known as simulation burn-in, a modeling concept borrowed from the electronics industry where the first items produced by a manufacturing process are discarded. EMOD enables you to then serialize the population state at this point so you can reload in subsequent simulations to analyze the effect of interventions on an endemic disease. See Simulation setup for serialization parameters.

Infectivity reservoirs

You can also introduce outbreaks through infectivity reservoirs; for example, zoonotic diseases may have a background animal reservoir that continuously exposes humans to infection. Zoonotic diseases are of particular interest because typically they have not previously been in the human population, making the whole population susceptible.

To configure a simulation with an infectivity reservoir, you must enable the configuration parameter Enable_Infectivity_Reservoir and configure the demographics parameter InfectivityReservoirSize. You may also use and configure the demographics parameters InfectivityReservoirStartTime and InfectivityReservoirEndTime.

The rate of reservoir-to-human transmission is configured with the parameter InfectivityReservoirSize and human-to-human transmission by the Infectivity and transmission parameters. These values can be adjusted to shift the disease from a low rate of primary zoonotic infection with substantial secondary human transmission to a high zoonosis rate with low human transmission. This changes the fraction of cases that are primary zoonotic infections.

The following graphs show the inset chart and charts for all channels in an outbreak of a zoonotic disease. To run this example simulation, see the Generic/Zoonosis scenario in the downloadable EMOD scenarios zip file. Review the README files there for more information.

After the initial outbreak, there are no additional imported cases set in the campaign file. However, there are additional introductions from the animal reservoir, as shown in the log prevalence chart.

_images/Zoonosis_InsetChart_default.png

Figure 1: Zoonotic disease outbreak and animal reservoir

_images/Zoonosis_AllCharts_default.png

Figure 2: All output channels showing re-seeding caused by zoonosis and waning immunity

Adding heterogeneity

One of the benefits of an agent-based model like EMOD over compartmental models is that the the model can be configured to capture heterogeneity in population demographics, migration patterns, disease transmissibility, climate, interventions, and more. This heterogeneity can affect the overall course of the disease outbreak and campaign interventions aimed at controlling it.

Demographics

Built-in demographics options are available for running EMOD simulations, or you can create customized demographics files to represent particular locations. It is generally recommended that you create a demographics file instead of using built-in demographics.

Every individual within the simulation has a variety of attributes, represented by continuous or discrete state variables. Some are static throughout life, and others dynamically change through the course of the simulation, through response either to aging or to simulation events (such as infection). Static attributes are assigned upon instantiation (simulation initialization or birth after the beginning of the simulation) and include gender, time of birth, time of non-disease death, etc. Dynamic attributes include disease state, history of interventions, and more.

Vital dynamics

Vital dynamics within EMOD are derived from fertility and mortality tables that are passed to the model as input. Input demographic data can be used to construct a cumulative probability distribution function (CDF) of death date based on individuals’ birth dates. Then, in the model, individual agents will be sampled stochastically from this CDF using an inverse transform of this distribution. Female agents similarly sample the age at next childbirth, if any, upon instantiation and birth of a previous child. Pregnancy is not linked to relationship status, although newly born individuals are linked to a mother. The fertility rate changes by simulation year and female age, and the range for available estimates depends on input data. Values outside of this range can be chosen by “clamping,” or choosing the nearest value within the range. Clamping was also used when necessary to determine the non-disease mortality rate, which varies by gender, age, and simulation year.

For more information on the demographics file, see Demographics file.

Individual and node properties

One of the most powerful and flexible features of EMOD is the ability to assign properties to nodes or individuals that can then be used to target interventions or move individuals through a health care system. For example, you might assign various degrees of risk, socioeconomic status, intervention status, and more. In the generic, environmental, typhoid, airborne, and TBHIV simulation types, these properties can be leveraged to add heterogeneity in transmission based on the property values assigned to each individual. For example, you might configure higher transmission among school-age children.

Similarly, you can add properties to nodes in a simulation. For example, you might configure some regions to be more urban or rural, to have better health care, or to be difficult to access by health care workers. This feature of EMOD is known as Heterogeneous Intra-Node Transmission (HINT); for more information, see Property-based heterogeneous disease transmission (HINT) and Multi-route HINT.

Transmission

By default, EMOD assumes transmission is homogeneous in each node: transmission does not vary based on population density, the population is “well-mixed” in each node, each individual has an equal likelihood of transmitting or contracting a disease. However, all of these aspects can be configured for greater heterogeneity to more accurately simulate a realistic population and disease dynamics. One simple example is varying transmission rates based on population density.

Other EMOD simulation types capture heterogeneous transmission through more biologically mechanistic parameters that control aspects of the simulation such as parasite density, symptom severity, coital acts, and more. Review the model overview for those simulation types for more information.

Migration

EMOD can also simulate human and vector migration, which can be important in the transmission of many diseases. You can assign different characteristics to each geographic node to control how the disease spreads.

For more information on how you can target campaign interventions to individuals or locations based on certain criteria, see Creating campaigns.

Population density and transmission scaling

Disease transmission depends on the rate effective contacts as represented by parameter \(\beta_0\). But what happens to the effective contact rate as the size of the population changes? There are two commonly accepted answers: frequency-dependent and density-dependent transmission scaling.

Frequency-dependent scaling

By default, EMOD uses frequency-dependent transmission and the overall transmissibility does not change with population size or density. An infected individual will infect the same number of people either in a small village or in a metropolitan area. For this case,

\[\text{Transmission} \propto \frac{\text{Number of effective contacts per unit time}}{\text{Current size of the node population}},\]

so that the transmission rate inversely proportional to \(N(t)\). The dynamics are frequency- dependent because the \(\beta_0\) parameter is divided by the current node population, \(N(t)\).

Density-dependent scaling

However, when population size is low it’s possible that the transmissibility might be lower and follow density-dependent behavior. Think of a room containing \(N\) individuals. A single highly-infectious cough would infect more people if the number of people in the room (population density) was higher. The number of effective contacts per person per time is proportional to the population. For this case,

\[\text{Transmission} \propto \frac{\text{Number of effective contacts per unit time}}{\text{Fixed population size}},\]

Usually, the denominator is taken as \(N(t=0)\), the initial node population. Note that the transmission rate per capita is a constant in this case. Density-dependent transmission is often found in SIR models, which inherently neglect the total population size.

Saturating function of population density

Based on existing evidence, it is possible that neither a frequency-dependent nor a density- dependent mechanism may adequately describe the correct scaling over the entire population density spectrum, but that more closely follows a non-linear scaling function. That is, contact rates tend to increase with density but saturate at high densities. EMOD allows the transmission rate to scale as a saturating function of population density. The force of infection contributed by each infected individual under these two cases of transmission scaling are as follows:

\[\begin{split}\text{Force per infected} = \left\{ \begin{array}{ll} \frac{\beta_0}{N} & \text{Frequency dependence} \\ \left[1 - \exp\left(- \frac{\rho}{\rho_{50}}\right)\right] \frac{\beta_0}{N} & \text{Saturating function of density} \end{array} \right.\end{split}\]

Here, \(\rho\) is the population density and \(\rho_{50}\) is an input parameter the governs the transition from density to frequency dependence. The population density is computed as \(\rho =\frac{N}{A}\) where \(A\) is is the area of the node. This node area is in turn computed from the longitude and latitude of the node center point (from the demographics file) and the node size. It is assumed that all nodes have equal size in terms of the degrees of latitude and longitude, as determined by configuration parameter Node_Grid_Size. Denoting this node grid size by \(w\), the area of a node located at (lat, long) is computed based on the corresponding area of a sphere,

\[A = R^2\Big\lbrace\Big[\text{cos}\Big(\Big(90-\text{lat}-\frac{w}{2}\Big)\frac{\pi}{180}\Big)- \text{cos}\Big(\Big(90-\text{lat}+\frac{w}{2}\Big)\frac{\pi}{180}\Big)\Big]\frac{w\pi}{180}\Big\rbrace,\]

where \(R=6371.2213\) km is the radius of Earth.

The saturating function of density is enabled by setting the EMOD configuration parameter Population_Density_Infectivity_Correction to SATURATING_FUNCTION_OF_DENSITY. Finally, the \(\rho_{50}\) parameter is configured using the configuration parameter Population_Density_C50. For more information, see Infectivity and transmission parameters.

This is described in more detail in the article The scaling of contact rates with population density for the infectious disease models, by Hu et al., 2013 Mathematical Biosciences. 244(2):125-134. See the figure from that article below.

_images/DensityScaling_orig_cropped.png

Figure 1: Effect of population density on transmissibility

The Generic/DensityScaling example scenario in the EMOD scenarios zip file illustrates how population density can be configured to affect transmission. Review the README files there for more information.

The table below shows how setting Node_Grid_Size in that scenario to 0.1, 0.15, and 0.3 affects population density and R0 values.

Simulation

Population

Latitude, Longitude

Node_Grid_Size

Node Area

Density

R0

1

10,000

0, 0

0.1

124

80

R0> 1

2

10,000

0, 0

0.15

278

36

R0~ 1

3

10,000

0, 0

0.3

1112

9

R0< 1

The following graphs show the effect of population density on transmissibility, in terms of maintaining endemic status. When population density is large enough, it is easy to maintain an endemic status while in lower density is difficult to do so.

_images/Density_default.png

Figure 2: Node_Grid_Size = 0.1, population density 80/km2and R0 > 1

_images/Density_15.png

Figure 3: Node_Grid_Size = 0.15, population density 36/km2and R0 ~ 1

_images/Density_3.png

Figure 4: Node_Grid_Size = 0.3, population density 9/km2and R0 < 1

Individual and node properties

One of the strengths of an agent-based model, as opposed to a compartmental model governed by ODEs, is that you can introduce heterogeneity in individuals and regions. For example, you can define property values for accessibility, age, geography, risk, and other properties and assign these values to individuals or nodes in the simulation.

These properties are most commonly used to target (or avoid targeting) particular nodes or individuals with interventions. For example, you might want to put individuals into different age bins and then target interventions to individuals in a particular age bin. Another common use is to configure treatment coverage to be higher for nodes that are easy to access and lower for nodes that are difficult to access. For more information on creating campaign interventions, see Creating campaigns.

For some simulation types, you can also configure heterogeneous transmission between individuals with different property values. For more information, see Property-based heterogeneous disease transmission (HINT) and Multi-route HINT. For other simulation types, transmission is configured using mechanistic parameter settings, such as parasite density, viral load, biting frequency, and other measures relevant to the disease being modeled. See Infectivity and transmission for more information.

The following sections describe how to define individual properties and assign different values to individuals in a simulation. However, with the exception of setting up age bins, you can use the same process to assign properties to a node. To see all individual and node property parameters, see NodeProperties and IndividualProperties.

Create individual properties other than age

Assigning property values to individuals uses the IndividualProperties parameter in the demographics file. See Demographics parameters for a list of supported properties. The values you assign to properties are user-defined and can be applied to individuals in all nodes or only in particular nodes in a simulation.

Note that although EMOD provides several different properties, such as risk and accessibility, these properties do not add logic, in and of themselves, to the simulation. For example, if you define individuals as high and low risk, that does not add different risk factors to the individuals. Higher prevalence or any other differences must be configured separately. Therefore, the different properties are merely suggestions and can be used to track any property you like.

  1. In the demographics file, add the IndividualProperties parameter and set it to an empty array. If you want the values to apply to all nodes, add it in the Defaults section; if you want the values to be applied to specific nodes, add it to the Nodes section.

  2. In the array, add an empty JSON object. Within it, do the following:

    1. Add the Property parameter and set it to one of the supported values.

    2. Add the Values parameter and set it to an array of possible values that can be assigned to individuals. You can define any string value here.

    3. Add the Initial_Distribution parameter and set it to an array of numbers that add up to 1. This configures the initial distribution of the values assigned to individuals in one or all nodes.

  3. If you want to add another property and associated values, add a new JSON object in the IndividualProperties array as above.

Note

Multiple properties must be defined in one file. They can be defined in either the base layer demographics file or an overlay file, but they cannot be split between the files. The maximum number of property types that can be added is two.

Create properties for age ranges

Creating properties based on age ranges works a little differently than other properties. Age_Bin is tied to the simulated age of an individual rather than being an independent property. Some of its characteristics, such as initial distribution and transitions, are dependent on information from the demographics file and EMOD that manages individual aging during the simulation.

  1. In the demographics file, add the IndividualProperties parameter and set it to an empty array. If you want the values to apply to all nodes, add it in the Defaults section; if you want the values to be applied to specific nodes, add it to the Nodes section.

  2. In the array, add an empty JSON object. Within it, do the following:

    1. Add the Property parameter and set it to “Age_Bin”.

    2. Add the Age_Bin_Edges_In_Years parameter and set it to an array that contains a comma- delimited list of integers in ascending order that define the boundaries used for each of the age bins, in years. The first number must always be 0 (zero) to indicate the age at birth and the last number must be -1 to indicate the maximum age in the simulation.

The example below shows how to set up several property values based on disease risk and physical place. It also defines three age bins: 0 to 5 years, older than 5 to 13, and older than 13 to the maximum age.

{
    "Defaults": {
        "IndividualProperties": [{
            "Property": "Risk",
            "Values": ["Low", "Medium", "High"],
            "Initial_Distribution": [0.7, 0.2, 0.1]
        }, {
            "Property": "Place",
            "Values": ["Community", "School", "Work", "Vacation"],
            "Initial_Distribution": [0.3, 0.2, 0.4, 0.1]
        }, {
            "Property": "Age_Bin",
            "Age_Bin_Edges_In_Years": [0, 5, 13, -1],
            "Transitions": []
        }]
    }
}

For an example of a complete demographics file with individual properties set, see the demographics file used in the 11_HINT_AgeAndAccess scenario. This scenario is described in more detail in Property-based heterogeneous disease transmission (HINT).

Property-based heterogeneous disease transmission (HINT)

Only generic, environmental, typhoid, airborne, or TBHIV simulations can use Heterogeneous Intra-Node Transmission (HINT) to manually configure heterogeneous disease transmission within each node. All other simulation types have preconfigured solutions for heterogeneous disease transmission based on the biological processes of the disease being modeled. For example, you can set parameters for parasite density, viral load, mosquito bites, and other relevant transmission factors.

See Infectivity and transmission parameters for more information on configuring transmission in this simulation type. Because HINT cannot be used with this simulation type, the parameter Enable_Heterogeneous_Intranode_Transmission in the configuration file must be set to 0 (zero).

Multi-route HINT

Only simulations using the environmental model (or inherited from environmental, such as typhoid) can use Heterogeneous Intra-Node Transmission (HINT) or Multi-route HINT to configure heterogeneous disease transmission within each node and across multiple routes of exposure. Generic, airborne, or TBHIV simulations can only use Heterogeneous Intra-Node Transmission (HINT) for contact-based transmission, and all other simulation types have preconfigured solutions for heterogeneous disease transmission based on the biological processes of the disease being modeled. For example, you can set parameters for parasite density, viral load, mosquito bites, and other relevant transmission factors.

See Infectivity and transmission parameters for more information on configuring transmission in this simulation type.

Geographic migration

Human migration is a important factor in the spread of disease across a geographic region. EMOD represents geography using nodes. Migration occurs when individuals move from one node to another; disease transmission occurs within nodes. Therefore, infected individuals can migrate to nodes without disease and introduce disease transmission into that node. Nodes are very flexible and can represent everything from individual households to entire countries or anything in between. Therefore, to include migration in a simulation, you must define multiple nodes.

At each time step, individuals in each node have a defined probability of migrating out of their current node to another. You can also define the average length of time individuals will stay in their destination node before migrating again. If you are using timesteps longer than one day and the time to next migration falls between timesteps, individuals will migrate at the following timestep. For example, if you use 7-day timesteps and an individual draws a 12-day trip duration, they won’t migrate until day 14.

The mode of migration can be local (foot travel), regional (by roadway or rail), by air, or by sea. You can also define different migration patterns, such as one-way or roundtrip. Individuals have a “home node” that is relevant for some types of migration, such as migrating an entire family unit only when all members are home or returning home after passing through several waypoints. For more detailed information, see Migration parameters.

For vector-borne diseases, you can also include vector migration. Vectors can migrate locally or regionally, and you can include additional rules based on the availability of food or habitat.

You must include a separate migration file for each mode of travel that describes the migration patterns for each node. It lists the migration rate for each node. Migration rate is defined as the fraction of the node’s population that is migrating out of the node per day. Units are per person per day, meaning the number of people migrating per day divided by the total population of the node. For more information on the structure of these files, see Migration files.

The Generic/Zoonosis scenario in the downloadable EMOD scenarios zip file includes daily migration. Review the README files there for more information.

Creating campaigns

You define the initial disease outbreak and interventions used to combat it for a simulation through a JSON-formatted campaign file, typically called campaign.json. It is hierarchically organized into logical groups of parameters that can have multiple levels of nesting. It contains an Events array of campaign events, each of which contains a JSON object describing the event coordinator, which in turn contains a nested JSON object describing the intervention. Campaign events determine when and where an intervention is distributed, event coordinators determine who receives the intervention, and interventions determine what is actually distributed. For example, a vaccination or diagnostic test.

Interventions can be targeted to particular nodes or individuals, based on age or other characteristics. Additionally, you can structure campaigns to guide individuals through complex health care systems. For example, administering a second-line treatment only after the preferred treatment has proven ineffective for an individual.

For some interventions, there can be a very complex hierarchical structure, including recursion. This framework enables rigorous testing of possible control strategies to determine which events or combination of events will best aid in the elimination of disease for specific geographic locations.

Multiple interventions

When creating multiple interventions, either of the same type or different types, they will generally be distributed independently without regard to whether a person has already received another intervention.

For example, say you create two SimpleBednet interventions and both interventions have Demographic_Coverage set to 0.5 (50% demographic coverage). This value is the probability that each individual in the target population will receive the intervention. It does not guarantee that the exact fraction of the target population set by Demographic_Coverage receives the intervention.

By default, each individual in the simulation will have a 50% chance of receiving a bednet in both of the distributions and the two distributions will be independent. Therefore, each individual has a 75% chance of receiving at least one bednet.

_images/howto-multiple.png
Campaign file overview

For the interventions to take place, the campaign file must be in the same directory as the configuration file and you must set the configuration parameters Enable_Interventions to 1 and Campaign_Filename to the name of the campaign file. When you run a simulation, you must have a single campaign file. However, you can use a campaign overlay file that includes certain parameters of interest that will override the settings in a base file; these files must be flattened into a single file before running a simulation. See Campaign overlay files for more information flattening two campaign files.

Although you can create campaign files entirely from scratch, it is often easier to start from an existing campaign file and modify it to meet your needs. The simplest method is to edit the parameters or parameter values in the JSON file in any text editor. You may also create or modify the files using a scripting language, as with configuration files. See Example scripts to modify a configuration file for a couple examples.

The following is an example of campaign file that has two events (SimpleVaccine and Outbreak) that occur in all nodes at day 1 and day 30, respectively. Each event contains an event coordinator that describes who receives the intervention (everyone, with the vaccine repeated three times) and the configuration for the intervention itself. Note that the nested JSON elements have been organized to best illustrate their hierarchy, but that many files in the Regression directory list the parameters within the objects differently. See Campaign parameters for more information on the structure of these files and available parameters for this simulation type.

{
    "Campaign_Name": "Vaccine",
    "Use_Defaults": 1,
    "Events":
    [
        {
            "Event_Name": "SimpleVaccine",
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 1,
            "class": "CampaignEvent",
            "Event_Coordinator_Config": {
                "Demographic_Coverage": 0.5,
                "Number_Repetitions": 3,
                "Target_Demographic": "Everyone",
                "Timesteps_Between_Repetitions": 7,
                "class": "StandardInterventionDistributionEventCoordinator",
                "Intervention_Config": {
                    "Cost_To_Consumer": 10,
                    "Waning_Config": {
                        "class": "WaningEffectMapLinear",
                        "Initial_Effect" : 1.0,
                        "Expire_At_Durability_Map_End" : 0,
                        "Durability_Map" : {
                            "Times"  : [   0,  30,  60,  90, 120 ],
                            "Values" : [ 0.9, 0.3, 0.9, 0.6, 1.0 ]
                        }
                    },
                    "Vaccine_Take": 1,
                    "Vaccine_Type": "AcquisitionBlocking",
                    "class": "SimpleVaccine"
                }
            },
        },
        {
            "Event_Name": "Outbreak",
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 30,
            "class": "CampaignEvent",
            "Event_Coordinator_Config": {
                "Demographic_Coverage": 0.001,
                "Target_Demographic": "Everyone",
                "class": "StandardInterventionDistributionEventCoordinator",
                "Intervention_Config": {
                    "Antigen": 0,
                    "Genome": 0,
                    "Outbreak_Source": "PrevalenceIncrease",
                    "class": "OutbreakIndividual"
                }
            }
        }
    ]
}

For a complete list of campaign parameters that are available to use with this simulation type and more detail about the campaign file structure, see Campaign parameters. For more information about JSON, see EMOD parameter reference.

Targeting interventions to nodes or individuals

Generally, you want to target your outbreaks and campaign interventions to specific regions or individuals who meet certain criteria. For example, you may want to distribute bednets only to areas where a mosquito-borne disease is endemic or vaccinate only young children who are at highest risk of a disease like measles. This topic describes how to distribute interventions to specific geographic nodes or groups of individuals.

Target to nodes based on geography

Targeting geographic nodes with a particular intervention can be controlled by the Nodeset_Config parameter in the campaign event. You can configure the event to target all nodes, a list of nodes, or all nodes contained within a defined polygon. For more information, see CampaignEvent.

Target to nodes or individuals using properties

To target interventions to particular individuals or nodes based on their property values, you must first define those properties in the demographics file using IndividualProperties or NodeProperties. See NodeProperties and IndividualProperties parameters for more information. Then, in the event coordinator in the campaign file, you can target an intervention or outbreak to particular individuals or nodes based on the properties applied to them. You can target the intervention based on one or more combinations of property values. For example, you may target individuals who are both medium risk and easily accessible or high risk and easily accessible. See Events and event coordinators for all available event coordinators.

Just as defining properties based on age works a little differently than other properties, targeting an intervention to a particular age range works a little differently than targeting an intervention to other properties. Note that not all event coordinators support all of these options.

To target interventions to node properties, use the Node_Property_Restrictions parameter to list the node property key-value pairs that must be assigned to a node for it to receive the intervention.

To target interventions to individual properties, use the Property_Restrictions_Within_Node parameter to list the individual property key-value pairs that must be assigned to a node for it to receive the intervention. You may instead use the Property_Restrictions parameter, but it does not support and/or combinations of multiple property values.

To target interventions to age ranges use the Target_Demographic parameter. This parameter also enables targeting based on gender, migration status, women of childbearing age, and other characteristics.

For more information on the specific parameter values and syntax, see individual event coordinators documented in Events and event coordinators.

Illustrated examples

The following examples illustrate how to target interventions to different groups. This includes how to configure interventions when there are multiple relevant properties, such as targeting individuals who are both low-risk and in a suburban setting or individuals who are either low- risk or living in suburban settings.

Single property, single value

The following examples show how to target interventions based on a single property value.

This example restricts the intervention to urban individuals.

{
    "Property_Restrictions_Within_Node": [
        { "Place": "Urban" }
    ]
}
_images/howto-targeted-1.png

Even if you have multiple properties defined in the demographics file, you can target interventions to a single property value in the same way. Individuals can be assigned any of the values for the other property types.

In this example, the intervention targets suburban individuals, regardless of their risk.

{
    "Property_Restrictions_Within_Node": [
        { "Place": "Suburban" }
    ]
}
_images/howto-targeted-2.png
Single property, multiple values

If you want to individuals with multiple values for the same property type, list the key/value pairs as separate objects in the array.

In this example, the intervention targets both rural and urban individuals.

{
  "Property_Restrictions_Within_Node": [
      { "Place": "Rural" },
      { "Place": "Urban" }
  ]
}
_images/howto-targeted-3.png
Multiple properties, individuals must match all values

To target individuals who match particular values defined by multiple property types, list the key/value pairs in the same object. This is an AND combination.

In this example, a intervention is targeted at low risk, suburban individuals. Individuals must have both property values.

{
  "Property_Restrictions_Within_Node": [
      { "Risk": "Low", "Place": "Suburban" }
  ]
}
_images/howto-targeted-4.png
Multiple properties, individuals must match at least one value

However, if you want to target multiple properties, but individuals need to have only one of the specified values to qualify for the intervention, list the key/value pairs as separate objects. This is an OR combination.

In this example, the intervention is targeted at individuals who are either low risk or suburban.

{
  "Property_Restrictions_Within_Node": [
      {"Risk": "Low"},
      { "Place": "Suburban" }
  ]
}
_images/howto-targeted-5.png
Target an age range

Targeting an intervention to an age range is configured differently than targeting an intervention to other property types. You have a couple different options.

When individuals must match for both age AND another property, you can use Target_Demographic to limit the age range. To use this method to cover multiple segments using an OR combination requires you to configure multiple campaign events to cover each segment of the population.

In this example, the intervention is targeted at urban individuals who are also age 0 to 5.

{
    "Property_Restrictions_Within_Node": [
        { "Place": "Urban" }
    ],
    "Target_Demographic": "ExplicitAgeRanges",
    "Target_Age_Min": 0,
    "Target_Age_Max": 5
}
_images/howto-targeted-6.png

However, to create property value criteria in AND/OR combinations as above, you can use the Age_Bin property with Property_Restrictions_Within_Node instead. EMOD automatically creates Age_Bin_Property_From_X_To_Y values when you use the Age_Bin_Edges_In_Years demographics parameter.

In this example, the intervention is targeted at individuals 0-5 who are low risk, 5-13 who are medium risk, or 13-125 who are high risk.

{
    "Property_Restrictions_Within_Node": [{
            "Risk": "LOW",
            "Age_Bin": "Age_Bin_Property_From_0_To_5"
        },
        {
            "Risk": "MEDIUM",
            "Age_Bin": "Age_Bin_Property_From_5_To_13"
        },
        {
            "Risk": "HIGH",
            "Age_Bin": "Age_Bin_Property_From_13_To_125"
        }
    ]
}

The following graphs show the property reports for a HINT simulation with both age and accessibility properties in which transmission is lower for hard-to-access individuals and there are an equal number of “easy” and “hard” to access individuals. The first graph shows the effect of targeting vaccination to children aged 6 to 10. The second graph shows the effect of instead targeting it to all individuals who are easy to access.

To run this example simulation, see the Generic/HINT_AgeAndAccess scenario in the downloadable EMOD scenarios zip file. Review the README files there for more information.

_images/HINT_AgeAndAccess_PropertyReport_agetargeted.png

Figure 1: Age-targeted vaccination

In this example, the outbreak begins in the easily accessible population and vaccination is targeted to children aged 6 to 10. Notice the dramatic decrease of the infection in 6 to 10 year olds due to the vaccination of this group. Compare infections in other ages, noting the overall decrease in infection across age groups even though only 6 to 10 year olds were vaccinated.

_images/HINT_AgeAndAccess_PropertyReport_accesstargeted.png

Figure 2: Access-targeted vaccination

In this example, the outbreak begins in the inaccessible population and vaccination is targeted to all individuals who are easily accessed. Notice how the infection is almost entirely eliminated among the easily accessible population, but there is only a slight reduction in infection among the inaccessible population that was not reached by the vaccination campaign.

Vaccination and herd immunity

In a basic compartmental model, we often assume all individuals are susceptible before the importation of an outbreak. However, vaccination is one of the most effective ways to prevent or contain a disease outbreak.

Starting from the traditional SIR equation:

\[\begin{split}\begin{aligned} \frac{dS}{dt} & = -\frac{\beta SI}{N}\\ \frac{dI}{dt} & = \frac{\beta SI}{N} - \gamma I\\ \frac{dR}{dt} & = \gamma I \end{aligned}\end{split}\]

For an outbreak to start, the following condition needs to be satisfied:

\[\frac{dI}{dt} = \frac{\beta SI}{N} - \gamma I > 0\]

If a vaccination campaign with coverage p \(\in\) [0, 1] is performed before the outbreak, a fraction of susceptible people move to the recovered compartment. If the vaccine take is e \(\in\) [0, 1], representing the probability of being protected after receiving a vaccine dose, the fraction of immune people due to vaccination is ep. Therefore, the previous condition can be reduced to:

\[\begin{split}\begin{aligned} \left(\beta (1 - e p) - \gamma\right) I & > 0\\ R_0 (1 - e p) & > 1\\ R_{eff} & > 1 \end{aligned}\end{split}\]

Reff is also called the effective reproductive number. Vaccination can reduce the disease’s ability to spread, and the outbreak can be prevented or stopped with less than 100% coverage. When Reff= 1, there exists a minimum vaccination coverage that can prevent a disease outbreak. This minimum coverage required to prevent an outbreak is usually called herd immunity (represented as Pherd). Therefore, the analytical form of Pherd can be derived based on the previous condition:

\[P_{herd} = \frac{1}{e}\left(1-\frac{1}{R_0}\right)\]
Multiple rounds of vaccinations

If multiple vaccine interventions are performed in the same area, people are selected on a binomial basis and all individuals have the same probability of being included. If the vaccine coverage is p, every individual has a probability p of being selected in a given round. After the first round, the fraction of non-vaccinated is 1-p, and after n rounds this fraction is (1 - p)n. Therefore, the fraction of people getting at least 1 dose is:

\[1-(1-p)^n\]

For example, the fraction of people getting at least one vaccination with a 50% campaign coverage is shown in the following table. After a few rounds, the coverage increases significantly with the number of campaigns.

Number of campaigns

Covered percentage (>=1 dose)

1

50%

2

75%

3

87.5%

4

93.75%

5

96.875%

For an example simulation, see the Generic/Vaccinations scenario in the downloadable EMOD scenarios zip file. Review the README files there for more information.

The following graphs show a baseline SIR outbreak and then the effect of a vaccination campaign distributed to the entire population. The vaccination campaign is repeated three times, seven days apart. The vaccine has 100% take and 50% demographic coverage. With this configuration, the fraction of immune people is above herd immunity and the outbreak does not spread.

_images/Vaccination_InsetChart_baseline.png

Figure 1: Baseline SIR outbreak

_images/Vaccination_InsetChart_broad.png

Figure 2: Broad vaccination campaign achieving herd immunity

However, this is usually not the case. In recent polio eradication campaigns, the number of supplemental immunization activity (SIA) campaigns planned in certain high-risk districts is greater than 6 per year, but poliovirus still persists in the area due to certain groups that are chronically missed. This can be caused by low accessibility, exclusion from SIA microplans, or vaccine refusal. Reff has not been driven below 1 because these susceptible people in the chronically missed groups are still in contact with the rest of population. In some modeling simulations, this assumption has to be included.

In this second example, the same 50% campaign coverage is repeated so that the same amount of vaccine is used. However, 30% of the population is not accessible to any vaccine campaigns. Although the number of vaccine doses used is the same as the previous example, the overall coverage is much lower. Creating campaigns that target interventions is described in more detail in Targeting interventions to nodes or individuals.

Number of campaigns

Covered percentage of total population

Covered percentage of groups with access

Covered percentage of groups without access

1

50%

71.43%

0%

2

59%

91.84%

0%

3

68.37%

97.67%

0%

4

69.53%

99.33%

0%

5

69.87%

99.81%

0%

The following graph shows the same SIR outbreak when 30% of the population is chronically missed by the vaccination campaign, allowing the outbreak to persist.

_images/Vaccination_InsetChart_access.png

Figure 3: Vaccination campaign that misses 30% of the population

Cascade of care

Some diseases, such as HIV, have a complex sequential cascade of care that individuals must navigate. For example, going from testing to diagnosis, receiving medical counseling, taking antiretroviral therapy, and achieving viral suppression. Other life events, such as pregnancy, migration, relationship changes, or diagnostic criteria may trigger different medical interventions.

Health care in EMOD can be applied to individuals, such as through a vaccination campaign, or be sought out by various triggering events including birth, pregnancy, or symptoms. A potential problem created by this structure is that an individual could end up in care multiple times. For example, an individual might have an antenatal care (ANC) visit and, in the same time step, seek health care for AIDS symptoms, both leading to HIV testing and staging.

To avoid this situation, you can configure interventions using the InterventionStatus individual property in the demographics file (see NodeProperties and IndividualProperties for more information). In the demographics file, create as many property values as necessary to describe the care cascade. For example, undiagnosed, positive diagnosis, on therapy, lost to care, etc.

In the campaign file, set up your event coordinator as you typically would, using Target_Demographic, Property_Restrictions_Within_Node, and other available parameters to target the desired individuals. See Targeting interventions to nodes or individuals for more information on targeting interventions and Events and event coordinators for all available event coordinators.

Then, in the intervention itself, you can add any properties that should prevent someone who would otherwise qualify for the intervention from receiving it. For example, someone who has already received a positive diagnosis would be prevented from receiving a diagnostic test if they sought out medical care for symptoms. You can also add the new property that should be assigned to the individual if they receive the intervention.

The following example shows a simplified example with two interventions, a diagnostic event and distribution of medication. The demographics file defines intervention status values for having tested positive and for being on medication.

{
    "Use_Defaults": 1,
    "Events": [{
            "class": "CampaignEvent",
            "Start_Day": 1,
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Target_Demographic": "Everyone",
                "Demographic_Coverage": 1.0,
                "Intervention_Config": {
                    "class": "SimpleDiagnostic",
                    "Disqualifying_Properties": [
                        "InterventionStatus:OnMeds"
                    ],
                    "New_Property_Value": "InterventionStatus:TestPositive",
                    "Base_Sensitivity": 1.0,
                    "Base_Specificity": 1.0,
                    "Cost_To_Consumer": 0,
                    "Days_To_Diagnosis": 5.0,
                    "Dont_Allow_Duplicates": 0,
                    "Event_Or_Config": "Event",
                    "Positive_Diagnosis_Event": "NewInfectionEvent",
                    "Intervention_Name": "Diagnostic_Sample",
                    "Treatment_Fraction": 1.0
                }
            }
        },
        {
            "class": "CampaignEvent",
            "Start_Day": 1,
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Intervention_Config": {
                    "class": "NodeLevelHealthTriggeredIV",
                    "Trigger_Condition_List": [
                        "NewInfectionEvent"
                    ],
                    "Disqualifying_Properties": [
                        "InterventionStatus:OnMeds"
                    ],
                    "New_Property_Value": "InterventionStatus:OnMeds",
                    "Demographic_Coverage": 1.0,
                    "Actual_IndividualIntervention_Config": {
                        "class": "ARTBasic",
                        "Viral_Suppression": 1,
                        "Days_To_Achieve_Viral_Suppression": 1000000
                    }
                }
            }
        }
    ]
}

Vector model overview

The EMOD vector model inherits the generic model functionality and introduces vector transmission and mosquito population dynamics. Interventions can be deployed within simulations for a variety of transmission settings with different transmission intensities, vector behaviors, and seasonally-driven ecologies. Climate data is necessary to simulate the effect of climatalogical impacts on vector biology. To use the vector model, set the configuration parameter Simulation_Type to VECTOR_SIM.

The figure below demonstrates the main components of the vector EMOD simulation type.

_images/malariaSIR.png

Vectors add a level of complexity to the interactions, as pathogens are transmitted via vector - human - vector. For a vector-borne disease, the SEIR model would appear as follows.

_images/DTKSchematic_vectors.png

While EMOD is an agent-based model, both the simulated humans and vectors move through the various infection states analogously to the compartmental model illustrated above. For more information comparing EMOD to compartmental models, see Compartmental models and EMOD.

The core of the simulation consists of solvers for mosquito population dynamics and pathogen transmission to and from the human host population. While the human population is fully individual-based, the mosquito population can be represented by discrete cohorts or individual agent mosquitoes. As an agent-based model, interactions between humans and vectors advance during each time step. The mosquito population is advanced, for example, through discrete 1-day time steps during which mosquitoes may engage in host-seeking or feeding behaviors, mating, maturation through a life-stage, or even death. Successful feeds on humans can result in infection of the human host, or conversely, susceptible mosquitoes may become infected from an infectious human.

Model implementation structure

There are two categories of possible implementations of the basic model, each with different computational efficiencies, resolutions, and flexibilities. The first is an individual model, where it simulates every individual mosquito in the population or can utilize a sampled subset of mosquitoes to represent the population as the whole. The second is a modified cohort simulation, with or without explicit mosquito ages.

Individual mosquito model

This basic model can be implemented through simulation of every individual mosquito, or by simulation of a subset of individual mosquitoes to represent the full population. Each mosquito’s state contains status (susceptible, latently infected, and infectious), timers for transition to adult from immature and infected to infectious, mating status and Wolbachia infection, and age. An oviposition timer to enforce a fixed feeding cycle may be included as well. If mosquitoes are sampled and a subset used to represent the local population, each sampled mosquito will have an associated sampling weight as well. To use this model, set the Vector_Sampling_Type parameter to TRACK_ALL_VECTORS or SAMPLE_IND_VECTORS. See Sampling parameters for more information.

Cohort model

In the modified cohort simulation, rather than representing the entire population by three compartments for susceptible, latently infected, and infectious mosquitoes, the simulation dynamically allocates a cohort for every distinct state, and the cohort maintains the count of all mosquitoes in that state. For the cohort simulation with explicit ages, in order to allow modeling of senescence, mosquito age is part of the state definition, and many more cohorts are required to represent the population. To use this model, set the Vector_Sampling_Type parameter to VECTOR_COMPARTMENTS_NUMBER or VECTOR_COMPARTMENTS_PERCENT. See Sampling parameters for more information.

Spatial-scale dynamics and migration

Extensive multi-node simulations with location-specific climate, intervention deployments, larval habitat, and migration may be configured within the EMOD framework. Nodes can be configured to best represent the spatial scale being simulated: a node can be at the household level, village level, county level, national level, or other desired geographic scale. Migration can be enabled between nodes (for both humans and mosquitoes), such that EMOD can most accurately represent the dynamics of the location. By varying spatial-scale resolution, relevant factors for transmission dynamics can be more clearly elucidated. For information on configuring nodes and migration parameters, see Migration.

The following visualization shows an example of a gridded representation of malaria transmission on the island of Madagascar.

_images/5_Madagascar_Vector_Daily_Bites.gif

The following pages in this section describe the structural components of the vector model.

Climate and demographics spatial data

Climate is especially important in vector models, as it is a key determinant of the geographic distribution and seasonality of malaria. Climate, which includes rainfall, temperature, and humidity, directly influences availability of larval habitat, determines mosquito developmental and survival rates, and impacts human behavior that leads to contact with infectious mosquitoes.

This framework explores the impact of different locations and weather patterns in single node and multi-node geographies. It explores demographics data for births, deaths, age initialization and immunity. Climate and demographics spatial data is configured for a geographic location, such as a rainfall-driven larval habitat, to create a climate-based distribution model of malaria transmission. The seasonality of species abundances are considered, as are rainfall, temperature, humidity and larval dynamics. Mosquito species and seasonality are depicted by habitat preferences that are, for example, temporary, semi-permanent or permanent.

Clearly, climate and habitat type are inextricably linked. However, it is possible to have several types of habitat within a given climate region; for this reason, climate is configured separately from habitat type. You may choose from a variety of options to configure climate, ranging from pre-set classification systems to uploaded user data. Further, it is possible to enable stochasticity in climate as well as in rainfall patterns. For example, rainfall data may be based off of monthly totals averaged per day. In the simulation configuration file, the parameter Enable_Rainfall_Stochasticity will set up daily rainfalls drawn from an exponential distribution with the same daily mean. This preserves approximate monthly totals, but produces a more realistic rainfall pattern for the simulation. For more information on how to configure climate, see Climate files. And for more information on the particular habitat types that are configurable in EMOD see Larval habitat.

_images/Malaria-climate.png

The EMOD vector model utilizes site-specific climate and demographics data to accurately simulate vector transmission in a given geographic location.

For a complete list of demographics parameters, go to Demographics parameters.

Relevant IDM publications on spatial models
Larval habitat

Available larval habitat is a primary driver of local mosquito populations, and different mosquito species can have different habitat preferences. Rainfall and humidity can strongly affect available larval habitat, although this depends on the mosquito species and its particular habitat preference. For example, Anopheles funestus, which prefers more semi-permanent larval habitat, exhibits rainfall dependence, partly due to vegetation on edges of water and the interaction of rainfall with agricultural schedules for crops such as rice. Further, preference can vary within species: An. funestus exhibits differences in population responses to rainfall which are correlated with chromosomal diversity.

In addition to available habitat, mosquito populations depend on larval development and mortality rates. These in turn are affected by a variety of factors, including climate, short term weather, and densities of other larvae.

In the present model, different models for larval habitat are developed for temporary, semi- permanent, permanent, and human-driven habitats. The duration of larval development is a decreasing function of temperature, and the present model utilizes an Arrhenius temperature-dependent rate a1(a2/TK). In some cases, this temperature-dependent rate must be modified by local larval density. Rainfall and temperature then combine through habitat creation and larval development to create varying local patterns of distribution by larval instar, and larval mortality and development duration determine pupal rates.

The creation of the habitat model is described in further detail in the articles Eckhoff, Malaria Journal 2011, 10:303, Eckhoff, Malaria Journal 2012, 11:419, and Eckhoff, Am. J. Trop. Med. Hyg. 2013, 88(5). The following sections provide information regarding specific larval habitat types and how to configure these parameters in EMOD.

See Larval habitat and Vector life cycle parameters for more information on configuring the various vector species.

Modeling mosquito life cycles and larval habitat

The framework facilitates simulating multiple species of Anopheles mosquitoes simultaneously. This allows for a mechanistic description of vector abundances through the effects of climate and weather on different preferred larval habitats. Each species is configured separately according to its ecological and behavioral preferences. For example, the female An. arabiensis deposits eggs primarily in temporary, rainfall-driven habitat and has a higher propensity to feed outdoors or on livestock. In contrast, An. farauti larvae live in brackish lagoons in the Solomon Islands, where lagoons fill with rain, and larvae are washed out into the ocean due to excess rain. The temporary rainfall relationship between larval habitat and rainfall cannot reproduce abrupt (sub-week) changes in biting rates, and an additional rainfall-driven larval mortality term has been implemented to capture the non-linear rain-to-habitat relationship.

_images/Vector_Transmission_abundance.png

Vector abundance depends on larval habitat availability

_images/Vector_Transmission_larval_habitats.png

Larval habitat type determines how rainfall and temperature influence vector populations

Habitats

The parameter Habitats is an array of JSON objects that contains one or more objects that define a habitat. The user indicates the vector species, habitat type(s), and the array of scaling factors used to determine the volume of that habitat needed for larvae to develop. Note that multiple species may utilize the same habitat, with each species existing independently. The scaling factor represents the number of larvae in a 1x1-degree area. The factor multiplicatively scales the resulting weather or population dependent functional form. The following example shows how to use this parameter, using Anopheles funestus as the vector and CONSTANT as the scaled habitat type.

{
    "Vector_Species_Params": [
        {
            "Name" : "funestus",
            "Habitats": [
                {
                    "Habitat_Type" : "CONSTANT",
                    "Max_Larval_Capacity": 10000000.0
                }
            ],
            "Acquire_Modifier": 0.2
        }
    ]
}

The following are possible values for Habitats. They are described in detail below.

  • TEMPORARY_RAINFALL

  • WATER_VEGETATION

  • CONSTANT

  • BRACKISH_SWAMP

  • HUMAN_POPULATION

  • LINEAR_SPLINE

Temporary rainfall

TEMPORARY_RAINFALL habitat corresponds to mosquitoes such as Anopheles arabiensis or Anopheles gambiae which breed primarily in temporary puddles which are replenished by rainfall and drain through evaporation and infiltration. We have developed a series of update equations for the larval carrying capacity which scale the functional form for evaporation rates. This creates a relationship in which evaporation and infiltration are higher when the weather is hot and dry.

The basic update equations appear in the article Eckhoff, Malaria Journal 2011, 10:303 and are as follows: Temporary habitat Htemp in a grid of diameter Dcell increases with rainfall Prain and decays with a rate \(\tau\)temp proportional to the evaporation rate driven by temperature in Kelvin T and humidity RH:

\[H_{temp} += P_{rain}K_{temp}D^2_{cell} - H_{temp}\left(\frac{\Delta t}{\tau_{temp}}\right)\]
\[\frac{1}{\tau_{temp}} = \left(5.1\times 10^{11} Pa\right)\mathrm{e}^{-\frac{5628.1 K}{T}} k_{tempdecay}\sqrt{\frac{0.018kg / mol}{2\pi R T}}(1-RH)\]

in which Ktemprefers to species-specific maximum larval habitat capacity defined in Vector_Species_Params, the exponential results from the Clausius-Clayperon relation, the root is from the expression for vapor evaporation rates due to molecular mass given a partial pressure, and the constant is the Clausius-Clayperon integration constant multiplied by a factor ktempdecay to relate mass evaporation per unity area to habitat loss. Note that the R in the denominator of the square root refers to the noble gas constant, and is neither a variable nor related to the relative humidity term RH.

Examining the two equations more closely, the first is a time step update equation, in which available habitat is increased by a multiple of the rainfall in the past time step, scaled by the size of the node in the simulation. For example, a 5 km square node will have 25 times the habitat of a 1 km square node. Then existing habitat decays with a time constant defined by the second equation. The basic functional form is based on an equation for evaporation rates, which is not fully realistic in that it neglects boundary layer effects. However, we are looking for a rate of habitat loss, not the rate of evaporation, and we also want to take infiltration into account. The parameter ktempdecay converts raw evaporation rates in kg/m^2/s into habitat loss per day.

The value of ktempdecay is initially chosen to set the habitat half-lives near 1 day for hot and dry conditions and 2-3 weeks for more tropical conditions. The parameter ktempdecay in the article Eckhoff, Malaria Journal 2011, 10:303 corresponds to Temporary_Habitat_Decay_Factor in the simulation configuration file, and it applies to all local species using temporary habitat. Each species in a given location can adjust the parameter Ktemp from the paper, or equivalently the scaling factor value in Habitats in the simulation configuration file (nested under the species name), to adjust the overall scaling of the time series. So Ktemp is specific for each species, but ktempdecay is a single value for all temporary habitat species in that location. Basically, the local weather and ktempdecay set the overall time profile for larval habitat which can feed-forward into adult population levels and biting rates. ktempdecay can be adjusted to achieve a best fit for the time profile for a given location. Factors such as how sandy or clay-like the soil is will affect this value. Once the time profile is correct, Ktemp can be adjusted to yield the correct annual entomological inoculation rate (EIR) for that species.

The carrying capacity for TEMPORARY_RAINFALL is the number of larvae per [R * Degree2] where R is accumulated rainfall (in meters).

Semi-permanent water vegetation

The second type of larval habitat, WATER_VEGETATION, is a semi-permanent habitat, which corresponds to developing vegetation on the edges of semi-permanent habitat. This could be seen for swamp-like or rice cultivation settings, in which the development of vegetation lags the rainfall and will be closest to its peak towards the end of the rainy season. The decay is specified as a loss of habitat per day, and this slower decay constant means that species with this habitat type will tend to have a proportionately higher dry-season population relative to the temporary habitat species.

In the model, semi-permanent habitat increases with a constant KsemiDcell2Prain and decays with a longer time constant \(\tau\)semi(Semipermanent_Habitat_Decay_Rate in the simulation configuration file):

\[H_{semi} += P_{rain}K_{semi}D^2_{cell} - H_{semi}\Delta t\tau_{semi}\]

The parameter Ksemi is the maximum larval capacity in Habitats for species with a Habitats of “WATER_VEGETATION”. The values of \(\tau\)semiand Ksemican be fit to local data on vector abundance by species over time or to local data on EIR to tailor a simulation to a specific setting.

The carrying capacity for WATER_VEGETATION is the number of larvae per [R * Degree2] where R is accumulated rainfall (in meters).

Constant habitat

For habitat type CONSTANT, larval carrying capacity is constant throughout the year and does not depend on weather. However, there will be a seasonal signal in adult population levels due to the effects of temperature upon aquatic development times. For a given carrying capacity, a faster development time will allow a local habitat to have a higher larval “through-put” with corresponding impacts on the adult population. The scaling factor value in the Habitats parameter is used to specify the carrying capacity per unit area Dcell2.

The carrying capacity for CONSTANT is the number of larvae per Degree2.

Brackish swamp

The BRACKISH_SWAMP habitat setting deals with the dynamics of how rain fills a brackish swamp, how it decays and the associated parameter for rainfall-driven mortality threshold. This habitat type is observable in the Solomon Islands where an entire larval generation is flushed away as a result of the overflowing lagoon geography. Since this is the case, the TEMPORARY_RAINFALL relationship between larval habitat and rainfall cannot reproduce these abrupt changes (on 0 week timescales) of biting rates. So, an additional rainfall-driven larval mortality term has been implemented that captures the non-linear rain-to- habitat relationship. This habitat uses the same decay rate as the semi-permanent water vegetation habitat.

The carrying capacity for BRACKISH_SWAMP is the number of larvae per Degree2.

_images/Rainfall_Driven_Mortality_Flushing_of_Swamp.png

Rainfall-driven mortality during swamp flushing

Human population

The habitat type HUMAN_POPULATION scales with correlates of urban development, for example, water that is available due to water pots in urban areas. This type is configured by multiplying the number of people in the node’s population times the capacity value set in Habitats. Further, climate is not involved in setting the capacity, as available water is not dependent on rainfall (it is instead dependent on human-provided water sources).

The carrying capacity for HUMAN_POPULATION is the number of larvae per person.

Linear spline

LINEAR_SPLINE is a user-customizable habitat type. Instead of specifying a given habitat type, users may utilize data that tracks the number of larvae measured throughout the year(s) to estimate the daily larval population using linear interpolation. In other words, this option enables the user to use data collected from a specific site, or create data files to match a particular location. This option does not replace the climatological parameters, but it instead adds a more flexible option in which you do not need climate data.

LINEAR_SPLINE has different syntax than the other habitat types, as the user is now required to provide a distribution of the larval population. The following example provides the syntax for configuring this habitat type:

{
    "Vector_Species_Params": [
        {
            "Name" : "funestus",
            "Habitats": [
                {
                    "Habitat_Type" : "LINEAR_SPLINE",
                    "Capacity_Distribution_Number_Of_Years" : 2,
                    "Capacity_Distribution_Over_Time": {
                        "Times": [   0.0,  30.417,  60.833,  91.25, 121.667, 152.083, 182.5, 212.917, 243.333, 273.75, 304.167, 334.583,
                                   365.0, 395.417, 425.833, 456.25, 486.667, 517.083, 547.5, 577.917, 608.333, 638.75, 669.167, 699.583 ],
                        "Values": [ 0.0, 0.0, 0.0, 0.0, 0.2, 1.0, 1.0, 1.0, 0.5, 0.2, 0.0, 0.0,
                                    0.0, 0.0, 0.0, 0.0, 0.2, 1.0, 1.0, 1.0, 0.5, 0.2, 0.0, 0.0 ]
                    },
                    "Max_Larval_Capacity": 10000000000.0
                }
            ]
        }
    ]
}

The LINEAR_SPLINE configuration specifies the day of year (in the Times array), larval value (in the Values array), and larval capacity scaling number (Max_Larval_Capacity). The model linearly interpolates the values to estimate the habitat availability for each vector species without requiring climatological data. It is required that the first value under Times is equal to zero.

The carrying capacity for LINEAR_SPLINE is the number of larvae per Degree2.

Modifying habitat availability

Ultimately, habitat is configured in order to create mosquito populations that realistically emulate observed entomological inoculation rate (EIR). Briefly, available habitat is directly related to mosquito abundance, and mosquito abundance in turn is directly related to biting rate. In order to calibrate the model there are several options for configuring habitat. You can first set habitat parameters and modify them directly using the habitat scalar and decay rate. Then, after those initial parameters are set, you can modify habitat with overall scaling parameters.

Habitat scalars and decay rates

The following two figures demonstrate the effects of varying the habitat scalar (Habitats) and the Temporary_Habitat_Decay_Factor (ktempdecay) for a single species with temporary habitat. Changing the habitat scalar will scale the resulting adult population size and biting rate by a similar factor.

_images/Vary_Habitat_Scalar.png

Effect of varying the habitat scalar, Habitats

Lowering ktempdecay causes the resulting rainfall-driven habitat to decay at a slower rate and thus increases \(\tau\)temp. A slower decay rate will result in higher larval habitat on average, and higher resulting adult population biting rates.

_images/Vary_Temporary_Habitat_Scalar.png

Effect of varying the decay rate, Temporary_Habitat_Decay_Factor (ktempdecay)

These two parameters can be co-varied to produce an appropriate temporal profile. If rainfall is constant, there is only one degree of freedom, as the habitat is always at equilibrium. The two degrees of freedom become important when there is a distinct rainy season. For example, scaling habitat produces the same ratio between biting rates in the wet season and dry season as seen in Effect of varying the habitat scalar, Habitats.

However, when varying the decay rate, the ratio between habitats is different in the wet season versus the dry season. As seen in the graph Effect of varying the decay rate, Temporary_Habitat_Decay_Factor (ktempdecay), a given ratio in rainy season will become exaggerated in the start of dry season (with a higher ratio the drier the season), as the slower decay rate extends habitat longer into the dry season.

Overall scaling

Often, it is desired to study a similar location, with the same species, temporal profile, etc., but with a different annual entomological inoculation rate (EIR). EIR depends not only on climate, but on larval habitat availability as well. So as an alternative for modifying all climate and habitat parameters to change EIR, it is simpler to use the habitat scaling parameter, x_Temporary_Larval_Habitat found in the configuration file. This parameter will scale all habitat parameters without changing the temporal dynamics, so that a new transmission is achieved with the same ratios among the species, and same time profile. For example, setting x_Temporary_Larval_Habitat to 0.1 would simulate low EIR (or a low transmission setting) by reducing available habitat to 10%; a value of 1 could be used to simulate high EIR (or a high transmission setting), and there would be no reduction in available habitat.

An alternative to x_Temporary_Larval_Habitat is LarvalHabitatMultiplier. LarvalHabitatMultiplier is a parameter in the demographics file, and can be applied to all habitat types configured for the simulation, specific habitat types, or individual mosquito species within particular habitat types in the Nodes array of the demographics file (see NodeAttributes in Demographics parameters). The following example shows the syntax:

{
    "Nodes": {
        "NodeID": 340461476,
        "NodeAttributes": {
            "LarvalHabitatMultiplier": [
                {
                    "Factor": 0.5,
                    "Habitat": "ALL_HABITATS",
                    "Species": "ALL_SPECIES"
                }
            ]
        }
    }
}

It should be noted that LarvalHabitatMultiplier enables habitat availability to be modified independently for each species within a shared habitat. This is an upgrade over previous versions of EMOD in which the modifier would be applied equally to all species within a shared habitat.

Basic species configuration

For each species listed in Vector_Species_Params, a “VectorPopulation” object will be added to the simulation at each node. Each species will be defined by parameters in the simulation configuration file for the vector ecology and behavior of the species. This allows for a mechanistic description of vector abundances and behavior through the effects of climate and weather on different preferred larval habitats.

The following example shows the syntax for configuring species parameters for Anopheles arabiensis. Note that you would also need to configure parameters for An. fuenstus and An. gambiae, if you are using a three species model as from the above example.

{
    "Vector_Species_Params": [
        {
            "Name": "arabiensis",
            "Acquire_Modifier": 0.2,
            "Adult_Life_Expectancy": 20,
            "Anthropophily": 0.65,
            "Aquatic_Arrhenius_1": 84200000000,
            "Aquatic_Arrhenius_2": 8328,
            "Aquatic_Mortality_Rate": 0.1,
            "Days_Between_Feeds": 3,
            "Egg_Batch_Size": 100,
            "Habitats": [
                {
                    "Habitat_Type": "TEMPORARY_RAINFALL",
                    "Max_Larval_Capacity": 11250000000.0
                }
            ],
            "Immature_Duration": 2,
            "Indoor_Feeding_Fraction": 1,
            "Infected_Arrhenius_1": 117000000000,
            "Infected_Arrhenius_2": 8336,
            "Infected_Egg_Batch_Factor": 0.8,
            "Infectious_Human_Feed_Mortality_Factor": 1.5,
            "Male_Life_Expectancy": 10,
            "Temperature_Dependent_Feeding_Cycle": "NO_TEMPERATURE_DEPENDENCE",
            "Transmission_Rate": 0.5,
            "Vector_Sugar_Feeding_Frequency": "VECTOR_SUGAR_FEEDING_NONE"
        }
    ]
}

While the above example shows the mosquito residing in one habitat, it is worth noting that mosquitoes can reside in multiple habitat types. This linear-combination of habitat types allows for greater control over local vector ecology (especially during dry seasons as habitat availability may be drastically different). The following example demonstrates the syntax to include several habitat types.

{
    "Vector_Species_Params": [
        {
            "Name": "arabiensis",
            "Acquire_Modifier": 0.2,
            "Adult_Life_Expectancy": 20,
            "Anthropophily": 0.65,
            "Aquatic_Arrhenius_1": 84200000000,
            "Aquatic_Arrhenius_2": 8328,
            "Aquatic_Mortality_Rate": 0.1,
            "Days_Between_Feeds": 3,
            "Egg_Batch_Size": 100,
            "Habitats": [
                {
                    "Habitat_Type": "TEMPORARY_RAINFALL",
                    "Max_Larval_Capacity": 11250000000.0
                },
                {
                    "Habitat_Type": "WATER_VEGETATION",
                    "Max_Larval_Capacity": 6000000000.0
                }
            ],
            "Immature_Duration": 2,
            "Indoor_Feeding_Fraction": 1,
            "Infected_Arrhenius_1": 117000000000,
            "Infected_Arrhenius_2": 8336,
            "Infected_Egg_Batch_Factor": 0.8,
            "Infectious_Human_Feed_Mortality_Factor": 1.5,
            "Male_Life_Expectancy": 10,
            "Temperature_Dependent_Feeding_Cycle": "NO_TEMPERATURE_DEPENDENCE",
            "Transmission_Rate": 0.5,
            "Vector_Sugar_Feeding_Frequency": "VECTOR_SUGAR_FEEDING_NONE"
        }
    ]
}
Vector transmission model

The vector model can be run in one of two modes: as a cohort model or as an individual mosquito model (see Vector model overview for more information). The vector model inherits the same human-infection model structure from the generic simulation type: uninfected, latent incubation, infectious, multiple immune variables, and super-infection. However, the transmission of infections is not between individual humans, but rather via the human-to-vector and vector-to-human pathways. The model is a closed-loop feeding cycle where a successful vector (mosquito) feeds (both indoors and outdoors), mates, then produce eggs which become larva and then adult mosquitoes.

For example, vector blood-feeding branches into various probabilities that are calculated once per time step. These calculations are based on species parameters and aggregated vector interventions (see feeding decision tree).

Vector transmission can be thought of in terms of vector ecology, and in the EMOD framework is comprised of several components:

  1. Vector tracking and the mosquito life-cycle,

  2. Mosquito behavior, which involves feeding (see Modeling feeding cycle outcomes).

  3. Transmission of malaria-causing Plasmodium parasites between humans and mosquitoes (see Transmission between humans and vectors). Each of these components is described in more detail below.

For a complete list of configuration parameters that are used in the malaria model, see the Configuration parameters.

Vector tracking and the mosquito life-cycle

Populations are tracked as cohorts throughout the full mosquito life-cycle:

  • From eggs to larvae with a temperature-dependent development period preceding emergence

  • A brief immature phase including sugar-feeding and mating

  • Repeating multi-day gonotrophic cycles during which mosquitoes may be exposed to infection

  • A temperature-dependent latency for sporogony

Life-cycle progression is modulated through a set of required species-specific parameters.

_images/Vector_Transmission_life_cycle.png

Vector life cycle

Modeling feeding cycle outcomes

Adult vectors enter a cycle of host-seeking, feeding, and egg-laying that continues until death. Successful vector blood-feeding branches into various probabilities that are calculated once per time step. These calculations are based on species parameters (for example, An. arabiensis has a higher propensity to feed outdoors or on livestock) and aggregated vector interventions.

Various feeding cycle outcomes are calculated from branching trees of conditional probabilities, and individual interventions modulate the probability of choosing between branches. Feeding cycle outcomes include:

  • Death before, during, or after feeding

  • Host unavailable

  • Successful human feed

The allocation of mosquitoes to feeding-cycle outcomes is based on end-state probabilities that have been aggregated over the individual humans in the simulation.

Multiple simultaneous interventions can target various branches in the vector feeding tree. For example, when both indoor residual spraying (IRS) and insecticide-treated nets (ITN) are applied against indoor host-seeking mosquitoes, IRS can discourage mosquitoes from entering the house and kill mosquitoes before feeding. The fraction of mosquitoes that survive can be blocked by the ITN, which may also kill a subset of the blocked fraction. Those mosquitoes who survive the feeding attempt may be killed by IRS post-feed. This is how deterrent and toxic effects of multiple interventions can be represented simultaneously.

To interact with these parameters and visualize the workings of this microsolver, see the decision tree visualization below:

To get started, press the play button. You can also pause the visualization at any time. Parameters in blue are vector species parameters, while parameters in green are types of campaign interventions. Information on these parameters can be found in Configuration parameters and Campaign parameters. The two pink points on the tree illustrate when transmission of malaria parasites is possible.

When the simulation starts, the initial mosquito population is set at 100 individuals. The starting population for day two has an initial seeding of 50 mosquitoes, and also includes all mosquitoes that either live without feeding or feed and oviposit. The simulation includes parameters that determine the lifespan of mosquitoes and the time it takes for oviposited eggs to hatch and mature to adulthood. As time progresses, the population will be comprised of only mosquitoes that are generated through the oviposition cycle in the model.

The counters on the right side of the visualization keep track of current and total mosquitoes that have “spawned” (generated in the simulation), died, lived without feeding, and fed and oviposited.

As an example, let’s simulate Anopheles gambiae. Set Life_Expectancy to 10 (most are thought to live approximately 1-2 weeks in nature), Egg_to_Adult to 5 (this is their minimum duration in the aquatic phase), Days_between_Feeds to 3, and Anthropophily and Indoor_Feeding_Fraction to 0.8. These mosquitoes prefer to primarily feed on humans, and preferentially feed indoors. Now, by changing the interventions, you can see how effective interventions (or combinations of interventions) need to be in order to disrupt (and reduce) mosquito feeding and oviposition. Note that the slider bars for interventions range from 0 - 1, with 1 conferring 100% effectiveness. When mosquito ecology is sufficiently disrupted, malaria transmission can be controlled. You can also manipulate the species parameters to see how mosquito ecology impacts the need for particular types of interventions.

If you are interested in simulating other mosquito species, more information on their relevant attributes can be found in the articles Made-to-measure malaria vector control strategies: rational design based on insecticide properties and coverage of blood resources for mosquitoes, by Killeen et al., 2014, Malaria Journal 13:146, and A global bionomic database for the dominant vectors of human malaria, by Massey et al., 2016, Nature.

Transmission between humans and vectors

Transmission between humans and vectors can only occur when mosquitoes successfully feed on humans (see Modeling feeding cycle outcomes).

Relevant IDM publications

Specifically for malaria, infectious humans can infect susceptible mosquitoes when mosquitoes ingest gametocytes during the blood meal. Conversely, infectious mosquitoes can infect susceptible humans by injecting sporozoites into the blood during feeding. Infectiousness of individuals to mosquitoes is proportional to the number of mature gametocytes absorbed in a blood meal, but modulated by a factor that inactivates gametocytes at high cytokine densities. Peak infectiousness typically follows peak parasitemia by about ten days. See the Malaria infection and immune model for more information regarding within-host parasite dynamics, and the figure on the parasite life-cycle in the Malaria disease overview for more information on the parasite life stages.

_images/Malaria_Infection_human_vector_transmission.png

Transmission between humans and vectors

Malaria model

The malaria model inherits the functionality of the vector model and introduces human immunity, within-host parasite dynamics, effects of antimalarial drugs, and other aspects of malaria biology to simulate malaria transmission. For example, individuals can have multiple infections and both innate and adaptive responses to antigens. To use the malaria model, set the configuration parameter Simulation_Type to MALARIA_SIM.

Discrete and continuous processes

The model implements a hybrid of discrete and continuous processes that work together to capture system latencies and discrete events inherent in the population dynamics of humans and mosquitoes, and parasites. Discrete events, such as latencies in the infected hepatocyte stage, the length of the asexual cycle from merozoite invasion to schizont rupture, and gametocyte maturation have particular time durations. State changes occur after the completion of the required amount of time (with specific time durations set for each state). See the figure of the Plasmodium life-cycle in Malaria disease overview for more information regarding the life-cycle stages used in this example.

Other processes, such as the decay of antibodies and the clearance of parasites, are represented by continuous-time processes. These are solved with a one-hour time step Euler method. All parasite quantities, such as the number of hepatocytes, number of merozoites, number of infected red blood cells of each antigenic variant, and gametocytes of each stage are represented as discrete integers. The infection is not cleared until each category is reduced to zero. This allows resolution of model dynamics at subpatent levels.

If using the modified mosquito cohort model introduced with the vector model, it allows temperature-dependent progression through sporogony, even with a different mean temperature each day, with no mosquitoes passing from susceptible to infectious before the full discrete latency.

Model components

The malaria model is complex, with numerous configurable parameters. The following network diagram breaks down the model into various model components, and illustrates how they interact with one another. The components on the network diagram correspond to the structural components listed below. Note that there is not perfect overlap between the labels on the network diagram and the structural components; this is because the network is drawn with increased detail in order to provide clarity in how the model functions and the components interact. The following pages will describe in detail how the structural components function.

_images/malaria_network_schematic.png

Network diagram illustrating the malaria model and its constituent components.

As new technology arises and novel types of interventions become available, it will be relatively seamless to integrate them into the current model structure. For example, as genetic technology improves and testing the utility of utilizing GM mosquitoes (such as a gene drive mosquito) in control efforts becomes important, EMOD will have no difficulty integrating this novel intervention into simulations.

The following pages describe the structure of the model and explore each of the model components. Additionally, the specifics of the model are discussed in detail in the articles Eckhoff, Malaria Journal 2011, 10:303; Eckhoff, Malaria Journal 2012, 11:419; Eckhoff, PLoS ONE 2012, 7(9); and Eckhoff, Am. J. Trop. Med. Hyg. 2013, 88(5).

Malaria infection and immune model

To study aspects of malaria infections and population dynamics that do not fit into the framework of a simple SEIRS model with enhancements, EMOD contains a detailed microsolver using within-host parasite dynamics. In the microsolver, the development of clinical and parasitological immunity is tracked through innate and adaptive immune responses to specific antigens. A detailed parasite count over time is tracked for each infected individual. This permits the simulation of measured prevalence over time by slide microscopy, RDTs (Rapid Diagnostic Tests) or other diagnostics. Gametocyte production and decay is included to study the human infectious reservoir.

Immune variables, such as antibody levels for each antigen, inflammatory cytokine levels, and immunological memory are represented by continuous floating-point variables. Most model parameters have a specific physical interpretation and can be rationally constrained by defined experimental measurements.

For a complete list of configuration parameters that are used in the malaria model, see the Configuration parameters.

Within-host parasite dynamics

Within the microsolver framework, each new infection begins with a hepatic (liver-stage) latency of fixed duration (7 days) and proceeds through cycles of asexual replication (with a fixed duration of 2 days) and gametocyte production (which takes 10 days). The model accounts for several antigenic components to which the immune system may develop immunity: the merozoite surface protein (MSP) variant, the Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) presented on the surface of the infected red-blood cell (IRBC), and less immunogenic minor surface epitopes (nonspecific epitopes).

_images/Malaria_Infection_within_host_parasite_dynamics.png

Within-host dynamics

Gametocyte development

The model advances gametocytes through five stages of development that are characterized by different drug susceptibilities. A fraction of gametocytes die at each developmental stage, and gametocytes can be inactivated by cytokines after uptake in a blood meal. Gametocytes reach maturity after ten days and remain in the bloodstream with a 2.5-day half-life. EMOD does not model acquired immunity to gametocytes.

_images/Malaria_Infection_gametocyte_development.png

Gametocyte development

Modeling a single clonal infection

A single clonal infection is modeled with an antigenic repertoire of 50 unique PfEMP-1 variants where each is associated with one of five repeating minor epitopes. In the first asexual cycle, the first five variants are expressed in equal numbers. At each update, the immune system is stimulated by the Infected Red Blood Cell (IRBC) count of each antigenic variant, and concurrently, the IRBC counts are decremented on account of immune and drug killing effects.

At the end of each asexual cycle, the model calculates the fraction of merozoites (16 for each previous IRBC) killed by specific recognition of the MSP variant, and the fraction that is differentiated into male and female gametocytes. To capture the dynamics of the parasite’s immune evasion strategy, the model imposes a constant per-parasite switching rate on the remaining merozoites to advance to subsequent antigenic variants in the repertoire.

_images/Malaria_Infection_parasite_strain_switching.png

Parasite strain switching

Population-level variants

The number of population-level variants affect the age-pattern of natural immunity acquisition. The full set of population-level antigenic variants (out of which a single infection’s repertoire is randomly drawn), consists of 100 MSP variants, 20 sets of five minor epitopes, and 1000 PfEMP-1 variants. Some parameters drive the asymptotic levels of adult detected parasitemia while others primarily govern the transition between child and adult detected prevalence rates, and provide that the number of PfEMP-1 variants is substantially more than an individual would experience in a year. A multi-year burn-in period ensures immune initialization dynamics approximate long-term asymptotic behavior where individuals in the simulation build antibody responses to a broad repertoire of parasite antigens appropriate for their age.

Immune response to infection

The immune response to infection is characterized by innate inflammatory and specific antibody components that limit maximum parasite density and work to clear infection. As the antibody response increases on a dimensionless scale from 0 to 1, the initial inflammatory response is suppressed. After the antibody response appears, continued antigenic stimulation will drive up the adapted response until that antigenic variant is cleared, at which point both the antibody levels and the capacity to respond will decay over time. The larger the antigenic population, the more infections, and thus time, it takes to acquire broad parasitological immunity.

_images/Malaria_Infection_immunity_effects_on_course_of_infection.png

Innate and adaptive immunity work together to limit asexual parasite numbers

Innate immune response

The innate immune response is modeled to depend on a temporary contribution from a rupturing schizont at the end of each asexual cycle, as well as the concentration of IRBC surface antigens to which an antibody response has not yet been developed. The innate response that is suppressed by the presence of specific antibodies is responsible for driving febrile symptoms and broad-spectrum parasite suppression.

_images/Malaria_Infection_immunity_innate_immune_response.png

Innate immune response

Adaptive immune response

The capacity to generate specific antibodies grows in response to the concentration of each novel antigen. Above a capacity threshold level, antibodies are produced in increasing concentration until the corresponding antigenic variant is cleared. At this time, the capacity will decay to a non-zero memory level. The mechanism by which the antibody capacity evolves captures the time delay of specific antibody response on re-infection.

_images/Malaria_Infection_immunity_adaptive_response_to_variable_epitopes.png

Adaptive immune response to PfEMP1 and minor epitopes

_images/Malaria_Infection_immunity_anti_MSP_immunity.png

Adaptive immune response to PfEMP1 and minor epitopesAdaptive immune response to MSP antigens

_images/Malaria_Infection_immunity_anti_CSP_immunity.png

Adaptive immune response to CSP

Relevant IDM publications
Infection and immunity: Within-host parasite dynamics
Outcrossing of parasite genetics
Interventions
Malaria symptoms and diagnostics

In EMOD, parameters for multiple types of malaria symptoms are included. Symptoms indicate the presence of disease, and one of the most prevalent types of data used to inform models is case data. By leveraging such data, models can help elucidate the processes that cause disease and enable calculations of the likely percent of the population that is infected (or infectious). Similarly, in order to model treatment-seeking behaviors (which will change the transmission dynamics of malaria), it is necessary to have a spectrum of disease severity (and hence, a spectrum of symptom parameters). Finally, disease severity is related to past infection and immune response, so inclusion of symptoms can help to further inform transmission dynamics of the target population.

For a complete list of configuration parameters that are used in the malaria model, see the Configuration parameters.

Fever and clinical cases

Fever is triggered by cytokine production through the innate immune response. A clinical case begins when fever surpasses a certain threshold and the clinical incident continues until fever subsides below another threshold.

_images/Malaria_Symptoms_fever_and_clinical_cases.png

Fever and clinical case definition

Anemia

Rupture of infected red blood cells (RBCs) destroys nearby RBCs and leads to anemia. Erythropoiesis is stimulated in anemic individuals to increase the rate of RBC production.

_images/Malaria_Symptoms_anemia.png

Anemia

Severe disease and mortality

Excessive fever, anemia, or parasite counts can all lead to severe disease and mortality.

_images/Malaria_Symptoms_severe_disease_and_mortality.png

Severe disease and mortality

Diagnostics

Malaria diagnostics test for the presence of asexual parasites in an individual’s blood. A parasite count is drawn from a Poisson distribution centered around the true asexual parasite count.

_images/Malaria_Symptoms_diagnostics.png

Diagnostics

Antimalarial drugs

Antimalarial drugs are a powerful tool for malaria control and elimination. Modeling mass antimalarial campaigns can elucidate how to most effectively deploy drug-based interventions and quantitatively compare the effects of cure, prophylaxis, and transmission-blocking in suppressing parasite prevalence.

Individuals in a simulation can receive antimalarial drugs as treatment for clinical or severe malaria or during a mass drug administration (MDA) campaign. The following information explains how to configure antimalarial drug parameters in a configuration file. For a complete list of configuration parameters that are used in the malaria model, see the Configuration parameters.

To specify where, when, and to whom the antimalarial drugs are administered, you must use an AntiMalarialDrug, AdherentDrug, or MultiComboPackDrug intervention in the campaign file. See Campaign parameters for more information.

Modeling a box profile of antimalaria drug pharmacokinetics

Pharmacokinetics (PK) of antimalarial drugs can be modeled with a box profile or with a double exponential decay. Pharmacodynamics (PD) of antimalarial drugs are modeled as an on/off switch for box PK or with a simple sigmoid for double exponential PK. If multiple drugs are given in a treatment, each drug is considered independently for both PK and PD.

To model a box PK, set the parameter PKPD_Model to FIXED_DURATION_CONSTANT_EFFECT. Drugs will have full killing efficacy for the duration set by Drug_Decay_T1. Dosing multiple times leads to multiple pulses of drug killing if dose separation is longer than Drug_Decay_T1. If dose separation (Drug_Dose_Interval) is shorter than Drug_Decay_T1, the box time is reset by each new dose such that drug killing is switched off after time Drug_Decay_T1 after the last dose.

_images/Antimalarial_Drugs__Box_PK_profile.png

Modeling drug killing effects with a box profile. (A) A single dose of drug kills parasites at maximum efficacy for duration set by Drug_Decay_T1. (B) A drug given in multiple doses results in multiple parasite killing pulses of duration Drug_Decay_T1 if the dose separation Drug_Dose_Interval is longer than Drug_Decay_T1. (C) If a drug’s dosing regimen results in doses taken before time Drug_Decay_T1 has elapsed, the drug kills parasites at maximum efficacy the entire time between the first dose and time Drug_Decay_T1 after the last dose.

The following example provides the syntax:

{
    "Malaria_Drug_Params": [
        {
            "Name": "Artemether_Lumefantrine",
            "PKPD_Model": "FIXED_DURATION_CONSTANT_EFFECT",
            "Drug_Cmax": 1000,
            "Drug_Decay_T1": 1,
            "Drug_Decay_T2": 1,
            "Drug_Dose_Interval": 1,
            "Drug_Fulltreatment_Doses": 3,
            "Drug_Gametocyte02_Killrate": 2.3,
            "Drug_Gametocyte34_Killrate": 2.3,
            "Drug_GametocyteM_Killrate": 0,
            "Drug_Hepatocyte_Killrate": 0,
            "Drug_PKPD_C50": 100,
            "Drug_Vd": 10,
            "Max_Drug_IRBC_Kill": 4.61,
            "Bodyweight_Exponent": 0
        }
    ]
}
Modeling a double exponential PK

To model a double exponential PK, set the parameter PKPD_Model to CONCENTRATION_VERSUS_TIME. The double exponential PK approximates a one- or two-compartment PK model. To model a drug with 1-compartment PK, set Drug_Decay_T2 equal to Drug_Decay_T1 (Drug_Vd becomes irrelevant). For PD, the Drug_PKPD_C50 determines the drug concentrations where parasite killing is effective (drug concentration > C50) and ineffective (drug concentration < C50). EMOD sets the shape of the parasite killing curve based on parasite kill rate and C50. Drug_Cmax, Drug_Decay_T1, Drug_Decay_T2, Drug_Vd, and Drug_PKPD_C50 together determine when the drug is effectively killing parasites. Changing each of these parameters will affect how long the parasites are exposed to a strong killing effect. Adding additional doses will also increase the duration of the parasite- killing window.

Children can be dosed with a fraction of the adult dose according to Fractional_Dose_By_Upper_Age, where each dose fraction is paired with the maximum age of children receiving that dose. Depending on specific PK characteristics of each drug, bodyweight may affect the rate of drug clearance. In the double exponential PK model, bodyweight is directly determined by age, and Drug_Cmax is multiplied by the inverse of the bodyweight raised to the power of Bodyweight_Exponent. See the paper on drug PK and PD ( Mass campaigns with antimalarial drugs: a modelling comparison of artemether-lumefantrine and DHA-piperaquine with and without primaquine as tools for malaria control and elimination) for more details on how to set PK and PD parameter values in EMOD.

_images/Antimalarial_Drugs_Concentration_vs_time_PK_profile.png

Modeling drug killing effects with a double exponential PK and sigmoid PD. (A) Two-compartment PK models include both distribution and elimination of drug. One-compartment models do not have a distribution phase. (B) A double exponential PK approximates the distribution and elimination phases of a two- compartment model. (C) Parasite killing is determined by drug concentration, Drug_PKPD_C50, and parasite stage- specific maximum kill rates. (D) PK and PD together determine the duration over which a dose of drug is effective.

Relevant IDM publications
Antimalarial drugs
Vaccines

Malaria disease overview

IDM is committed to supporting data-driven malaria control and elimination efforts. This page provide information about malaria itself: the biology, symptoms, treatment, and prevention. See Malaria model for information about the Epidemiological MODeling software (EMOD) MALARIA simulation type developed by IDM to aid in malaria elimination.

Malaria biology

Malaria is a mosquito-borne disease caused by plasmodium parasites transmitted through the bite of blood-feeding Anopheles mosquitos. There are roughly 30 species of Anophelenes capable of serving as malaria vectors, and only females bite (i.e. seek blood meals) to gain nutrients necessary for oviposition. After taking blood meals, females lay eggs in water sources, where the emerging larvae hatch and mature. Each species of Anopheles has different aquatic habitat preferences, ranging from small, ephemeral pools to large swampy areas, and in some cases, brackish water.

Transmission of malaria depends on both biotic and abiotic factors. Mosquito lifespan is positively correlated to transmission intensity. Mosquito populations, and therefore transmission, tend to be seasonal with dependence on climatic conditions such as rainfall patterns, humidity, and temperature. Typically, malaria transmission peaks during and after the rainy season, when conditions are prime for mosquito reproduction. Additionally, transmission is dependent on human immunity: people living in areas of high exposure to malaria can develop partial immunity, which reduces the risk of developing severe disease. Unfortunately, such partial immunity may mask symptoms of disease (and obscure infection), which may hinder control or elimination efforts.

Once an infected mosquito bites a human and transmits parasites, the parasites multiply in the host’s liver, then infect and destroy red blood cells (erythrocytes). During the blood stage, the parasites that infect and destroy erythrocytes release merozoites which then infect other erythrocytes. The gametocyte form of the blood-stage parasite are ingested by female Anophelenes during future blood meals, where the mosquito-based life cycle continues.

In the mosquito, the ingested gametocytes generate zygotes in the mosquito’s gut. Those zygotes undergo development, embed into the mosquito’s midgut wall, and mature into oocytes. Oocytes develop and in turn rupture, releasing the sporozoite form of the parasite. The sporozoites travel to and reside in the mosquito’s salivary glands. Then, when the female takes a blood meal, the sporozoites are injected into the host, starting another infection cycle. The mosquito is the vector that transmits pathogens between human hosts. See the below figure and figure caption for an illustrated description of this process.

_images/malaria_lifecycle.gif

Plasmodium life cycle, adapted from the CDC, Malaria Biology. During a blood meal, a malaria-infected female Anopheles mosquito inoculates sporozoites into the human host (1) . Sporozoites infect liver cells (2) and mature into schizonts (3), which rupture and release merozoites (4). After this initial replication in the liver (exo-erythrocytic schizogony, “A”), the parasites undergo asexual multiplication in the erythrocytes (erythrocytic schizogony, “B”). Merozoites infect red blood cells (5). Some parasites differentiate into sexual erythrocytic stages (gametocytes) (7). The gametocytes are ingested by an Anopheles mosquito during a blood meal (8). The parasites’ multiplication in the mosquito is known as the sporogonic cycle, “C”. While in the mosquito’s stomach, the gametocytes generate zygotes (9). The zygotes in turn become motile and elongated (ookinetes) (10) which invade the midgut wall of the mosquito where they develop into oocysts (11). The oocysts grow, rupture, and release sporozoites (12), which make their way to the mosquito’s salivary glands. Inoculation of the sporozoites into a new human host (1) perpetuates the malaria life cycle.

Symptoms

All the clinical symptoms associated with malaria are caused by the asexual erythrocytic (or blood stage) parasites. Malaria has a long incubation period, so symptoms do not develop immediately after a person has been bitten by an infectious mosquito; they can occur 7 days after infection, but this may vary up to 30 days.

Symptoms of malaria range in severity, but include fever, headache, body-ache, chills, and vomiting. Severe malaria can develop when the infection is not treated, and may result in organ failure or even death. Examples of severe malaria include cerebral malaria, severe anemia, respiratory distress (due to accumulated fluid in the lungs), kidney failure, metabolic acidosis, hypoglycemia, and other maladies. Death can occur from anemia, hypoglycemia, or cerebral malaria (where capillaries leading to the brain are blocked; typically resulting in coma, brain damage or learning disabilities).

Confirmed diagnosis of malaria is done through observation of parasites in the blood, as seen on the below microscopic image. In highly endemic areas, treatment typically occurs prior to diagnostic confirmation, as malaria is more easily cured with prompt treatment.

_images/malaria-blood-smear.jpg

Human red blood cells infected with Plasmodium falciparum (the parasite is shown in dark purple). Photo credit Le Roch lab, UC Riverside.

Prevention & control efforts

There are numerous types of strategies used to control malaria. As a vector-borne disease, there are multiple stages at which the transmission cycle can be broken.

  • Vector control: strategies that take into account vector ecology. These include:

    • Chemical control, such as insecticide spraying or use of larvicides.

    • Reduction of or elimination of mosquito larval habitat, through drainage or use of biological controls.

    • Potential use of genetic modification (with tools such as CRISPR) to create mosquitoes that are resistant to infection from Plasmodium parasites.

    • Potential use of the bacterium Wolbachia to prevent mosquitoes from becoming infectious.

  • Personal protection: strategies that avoid infection (by avoiding bites by infectious mosquitoes), or by preventing disease. These include:

    • The use of insecticide-treated nets (ITN)

    • Administration of antimalarial drugs to particularly vulnerable groups, such as children or pregnant women

Global malaria burden

While progress towards reducing the malaria burden has been largely successful, malaria nevertheless remains a major health problem and target of focused, global efforts for elimination and eradication. According to the CDC, 3.2 billion people worldwide are at risk of malaria. In 2015, there were 214 million cases with 483,000 deaths. Malaria is especially harmful to children: more than 70% of all malaria deaths occur in children under the age of 5. To put that in perspective, a child dies from malaria roughly every 2 minutes.

While malaria is a global problem, the burden is not distributed equally across the globe. Sub-Saharan Africa experiences a disproportionately high burden, with about 76% of all cases and 75% of all deaths. South East Asia, Latin America, and the Middle East are also at high risk for malaria.

Benefits of mathematical models in malaria control and eradication

Control and eventual eradication of malaria will require multifaceted and geographically-specific intervention efforts. Heterogeneity in transmission, and transmission potential, creates a need for combinations of interventions that can adapt to the particular malaria epidemiology of the target area.

For malaria, mathematical modeling and simulations are key to achieving eradication. Species- specific vector ecology is a fundamental driver of transmission, and transmission is also impacted by the interactions of climate, human behavior, and land usage, across varying spatial scales. Modeling these factors enables accurate representations of baseline transmission, which in turn provides a platform to test various interventions (such as insecticide-treated nets (ITN), indoor residual spraying (IRS), or mass drug administration (MDA)) and combinations of interventions. These simulation results can inform policy to develop effective–and cost effective–strategies by exploring the many possible dimensions of coverage, frequency of distribution, and combinations of interventions targeted to particular locations.

It should be noted that such a multifaceted and integrated approach to vector control and health management is the most likely path towards elimination, as heavy reliance on singular approaches can be problematic. For example, vector control has been widely implemented and quite successful; however, mosquitoes are developing resistance to pyrethroids, one of the most common classes of insecticides currently recommended for ITN or IRS to control mosquito populations. Through modeling approaches, researchers will be able to develop strategies that lessen dependence on particular insecticides while maintaining successful control efforts.

A brief history of malaria modeling

Malaria has a long history of posing risks to public health, and as such, also has long been the target of mathematical models tasked with providing solutions to ease the burden. For a more detailed history of malaria models, see Smith et al. PLOS Pathogens 2012 8(4), and Smith et al. Trans R Soc Trop Med Hyg 2014 108(4). The EMOD malaria model builds upon this rich history of disease modeling to provide a novel and rigorous approach to help guide efforts towards malaria elimination and eradication. To fully understand the strengths of EMOD, it is helpful to understand the modeling background from which EMOD developed.

Arguably, the quantitative analysis of mosquito-borne diseases, specifically malaria, began with Ronald Ross. In 1897 Ross confirmed that mosquitoes serve as the vector for malaria parasites, and embarked on a career focused on disease prevention through vector control. Further, Ross developed the modeling framework that serves as the basis for studying malaria transmission dynamics.

Ross’s work inspired researchers focused on controlling mosquito-borne diseases. His emphasis on the development of metrics useful for measuring the intensity of transmission led to the development of :term:` entomological inoculation rate (EIR)` and motivated research aimed at understanding mosquito movement and the relevant spatial scales for mosquito ecology. Later, in the 1950s, George MacDonald formalized Ross’s models, and introduced the concepts of vectorial capacity and reproductive number. This framework is now known as the Ross-Macdonald model, and is still widely implemented in current modeling work. In fact, the vast majority of models created in the last 40 years share most of their assumptions with the Ross-Macdonald model (see Reiner et al. 2013, Journal of the Royal Society Interface.)

The Garki project

The Garki project was a major milestone in malaria research. From 1969 to 1976 this study was conducted by the World Health Organization (WHO) and the local government of the Jigawa State, Nigeria, to understand the impacts of indoor residual spraying (IRS) and mass drug administration (MDA) on malaria transmission, as well as to evaluate the utility of mathematical modeling. While the interventions used did not interrupt transmission at the desired level, the model proved to be a success. The epidemiology of the Plasmodium parasite was realistically replicated, even with a simplistic model, and as a result understanding of the parasite’s epidemiology was greatly increased.

While the project was largely considered a failure in terms of malaria control, much was gained from the project. Basic tenets of malaria control were learned– namely, that malaria ecology needs to be fundamentally altered: either by modification of mosquito ecology, or by changing human ecology such as by improving housing conditions. A superinfection, a fundamental malaria concept modeled by Macdonald in his first malaria model publication [Macdonald-1950], was more correctly described by Dietz [Dietz-1974]. Further, the data set from the project is publicly available, and has become a fundamental tool for use in malaria modeling. For more information on this project, see The Garki Project.

Moving forward

The Ross-Macdonald model, while extremely useful and influential, has some shortcomings. The model assumes homogeneous transmission within a well-mixed population. Host-vector ratios and numerous aspects of mosquito feeding and development biology is assumed to remain constant. Further, all hosts are assumed to be identical, with equal exposure to pathogens, and the probability of transmission is proportional to host and vector densities. Interestingly, despite work demonstrating that these assumptions are not realistic, many models still utilize them.

To improve on the Ross-Macdonald model, it is necessary to understand and implement increased complexity into the transmission dynamics of the model. Transmission dynamics are not linear, and instead depend on fine-scale heterogeneities; ecological as well as social contexts are extremely relevant for potential host-vector interactions, and both human and mosquito movement across relevant spatial scales also impact malaria transmission.

Modern research has continued to improve our understanding of both parasite biology (and genetics) as well as within-host immune system interactions. To fully understand the complex nature of malaria epidemiology and how to control it, it is important to include these aspects into models.

The malaria EMOD leverages the history of malaria modeling by staring with these important fundamentals and building upon them. EMOD combines detailed vector population dynamics and interactions with human populations, and includes microsimulations for human immunity and within- host parasite dynamics. The model builds on the work of Ross and MacDonald, leverages the Garki model, and incorporates current modeling efforts to model multiple vector species simultaneously interacting with a human population.

References
Macdonald-1950

Macdonald G (1950) The analysis of infection rates in diseases in which superinfection occurs. Trop Dis Bull 47: 907–915.

Dietz-1974

Dietz K, Molineaux L, Thomas A (1974) A malaria model tested in the African savannah. Bull World Health Organ 50: 347–357

EMOD parameter reference

The configurable parameters described in this reference section determine the behavior of a particular simulation and interventions. The parameter descriptions are intended to guide you in correctly configuring simulations to match your disease scenario of interest.

The reference section contains only the parameters supported for use with the malaria simulation type. To see the parameters for a different simulation type, see the corresponding documentation.

These parameters include three major groups: parameters for the demographics file, parameters for the configuration file, and parameters for the campaign file.

If you would rather use a text file for parameter reference than this web documentation, you can also generate a schema with the EMOD executable (Eradication.exe) that defines all configuration and campaign parameters that can be used in a simulation, including names, data types, defaults, ranges, and short descriptions.

JSON formatting overview

All of these parameters are contained in JSON (JavaScript Object Notation) formatted files. JSON is a data format that is human-readable and easy for software to read and generate. It can be created and edited using a simple text editor.

JSON has two basic structures: a collection of key-value pairs or an ordered list of values (an array). These structures are within a JSON object, which is enclosed in a set of braces. You will notice that each of these files begins with a left brace ({) and ends with a right brace (}).

A value can be a string, number, Boolean, array, or an object. The campaign and data input files often use nested objects and arrays. See www.json.org for more information on JSON.

A few important details to call out when creating JSON files:

  • Add a comma after every key-value pair or array except for the last key-value pair in an array or object.

  • The keys (parameters) are case-sensitive. For example, “NodeID” is not the same as “NodeId”.

  • Decimals require a 0 (zero) before the decimal point.

  • EMOD does not support Booleans (“True”, “False”). Instead, EMOD use the integer 1 for “True” and 0 for “False”.

  • Every opening curly brace or square bracket ({ or [) requires a corresponding closing brace or bracket (} or ]).

The following is an example of a JSON formatted file.

{
    "A_Complex_Key": {
        "An_Array_with_a_Nested_Object_Value": [
            {
                "A_Simple_Key": "Value",
                "A_Simple_Array": [ "Value1", "Value2" ],
                "An_Array_with_Number_Values": [ 0.1, 0.2 ],
                "A_Nested_Object": {
                    "Another_Simple_Key": "Value",
                    "Nested_Arrays": [
                        [ 10, 0.1 ],
                        [ 0.1, 1 ]
                    ]
                }
            }
        ]
    }
}

JSON resources

The website https://jsonlint.com provides validation of JSON formatting. This can be very helpful in identifying missing commas, unbalanced curly braces, missing quotation marks, and other common JSON syntax errors. Another helpful site is http://jsondiff.com/, which highlights differences between two uploaded JSON files.

Demographics parameters

The parameters described in this reference section can be added to the JSON (JavaScript Object Notation) formatted demographics file to determine the demographics of the population within each geographic node in a simulation. For example, the number of individuals and the distribution for age, gender, immunity, risk, and mortality. These parameters work closely with the Population dynamics parameters in the configuration file, which are simulation-wide and generally control whether certain events, such as births or deaths, are enabled in a simulation.

Generally, you will download a demographics file and modify it to meet the needs of your simulation. Demographics files for several locations are available on the Institute for Disease Modeling (IDM) GitHub EMOD-InputData repository or you can use COmputational Modeling Platform Service (COMPS) to generate demographics and climate files for a particular region. By convention, these are named using the name of the region appended with “_demographics.json”, but you may name the file anything you like.

Additionally, you can use more than one demographics file, with one serving as the base layer and the one or more others acting as overlays that override the values in the base layer. This can be helpful if you want to experiment with different values in the overlay without modifying your base file. For more information, see Demographics file.

At least one demographics file is required for every simulation unless you set the parameter Enable_Demographics_Builtin to 1 (one) in the configuration file. This setting does not represent a real location and is generally only used for testing and validating code pathways rather than actual modeling of disease.

Demographics files are organized into four main sections: Metadata, NodeProperties, Defaults, and Nodes. The following example shows the skeletal format of a demographics file.

{
     "Metadata": {
          "DateCreated": "dateTime",
          "Tool": "scriptUsedToGenerate",
          "Author": "author",
          "IdReference": "Gridded world grump2.5arcmin",
          "NodeCount": 2
     },
     "NodeProperties": [
          {}
     ],
     "Defaults": {
          "NodeAttributes": {},
          "IndividualAttributes": {},
          "IndividualProperties": {},
          "Society": {}
     },
     "Nodes": [{
          "NodeID": 1,
          "NodeAttributes": {},
          "IndividualAttributes": {},
          "IndividualProperties": {},
          "Society": {}
     }, {
          "NodeID": 2,
          "NodeAttributes": {},
          "IndividualAttributes": {},
          "IndividualProperties": {},
          "Society": {}
     }]
}

All parameters except those in the Metadata and NodeProperties sections below can appear in either the Defaults section or the Nodes section of the demographics file. Parameters under Defaults will be applied to all nodes in the simulation. Parameters under Nodes will be applied to specific nodes, overriding the values in Defaults if they appear in both. Each node in the Nodes section is identified using a unique NodeID.

The tables below contain only parameters available when using the malaria simulation type.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

Metadata

Metadata provides information about data provenance. IdReference is the only parameter used by EMOD, but you are encouraged to include information for your own reference. For example, author, date created, tool used, NodeCount and more are commonly included in the Metadata section. You can include any information you like here provided it is in valid JSON format. IDReference is used to connect the files together; the climate, migration, and demographics files all have IdReference so that there is some way to know that they go together (i.e. know about the same nodes).

If you generate input files using COMPS, the following IdReference values are possible and indicate how the NodeID values are generated:

Gridded world grump30arcsec

Nodes are approximately square regions defined by a 30-arc second grid and the NodeID values are generated from the latitude and longitude of the northwest corner.

Gridded world grump2.5arcmin

Nodes are approximately square regions defined by a 2.5-arc minute grid and the NodeID values are generated from the latitude and longitude of the northwest corner.

Gridded world grump1degree

Nodes are approximately square regions defined by a 1-degree grid and the NodeID values are generated from the latitude and longitude of the northwest corner.

The algorithm for encoding latitude and longitude into a NodeID is as follows:

unsigned int xpix = math.floor((lon + 180.0) / resolution)
unsigned int ypix = math.floor((lat + 90.0) / resolution)
unsigned int NodeID = (xpix << 16) + ypix + 1

This generates a NodeID that is a 4-byte unsigned integer; the first two bytes represent the longitude of the node and the second two bytes represent the latitude. To reserve 0 to be used as a null value, 1 is added to the NodeID as part of the final calculation.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Author

string

NA

NA

NA

The person who created the demographics file. Files generated by COMPS will include this value, but it is not used by EMOD simulations.

 
{
    "Metadata": {
        "DateCreated": "Sun Sep 25 23:19:55 2011",
        "Tool": "convertdemog.py",
        "Author": "jdoe",
        "IdReference": "Gridded world grump2.5arcmin",
        "NodeCount": 1
    }
}

DateCreated

string

NA

NA

NA

The date the demographics file was created. Files generated by COMPS will include this value, but it is not used by EMOD simulations.

 
{
    "Metadata": {
        "DateCreated": "09212017",
        "IdReference": "Gridded world grump2.5arcmin",
        "NodeCount": 23
    }
}

IdReference

string

NA

NA

NA

The identifier for a simulation; all input files (except configuration and campaign files) used in a simulation must have the same IdReference value. The value must be greater than 0. If the input files are generated using COMPS, this indicates the method used for generating the NodeID, the identifier used for each node in the simulation.

 
{
    "Metadata": {
        "IdReference": "Gridded world grump30arcsec"
    }
}

Metadata

JSON object

NA

NA

NA

The structure that contains the metadata for the demographics file.

 
{
    "Metadata": {
        "IdReference": "Gridded world grump30arcsec",
        "NodeCount": 20
    }
}

NodeCount

integer

1

Depends on available memory

NA

The number of nodes to expect in the input files. This parameter is required.

 
{
    "Metadata": {
        "NodeCount": 2
    }
}

Resolution

integer

NA

NA

NA

The spatial resolution of the demographics file. Files generated by COMPS will include this value, but it is not used by EMOD simulations.

 
{
    "Metadata": {
        "Resolution": 150
    }
}

Tool

string

NA

NA

NA

The software tool used to create the demographics file. Files generated by COMPS will include this value, but it is not used by EMOD simulations.

 
{
    "Metadata": {
        "Tool": "convertdemog.py",
        "Author": "jdoe",
        "IdReference": "Gridded world grump2.5arcmin",
        "Resolution": 150,
        "NodeCount": 1
    }
}
NodeProperties and IndividualProperties

Node properties and individual properties are set similarly and share many of the same parameters. Properties can be thought of as tags that are assigned to nodes or individuals and can then be used to either target interventions to nodes or individuals with certain properties (or prevent them from being targeted). For example, you could define individual properties for disease risk and then target an intervention to only those at high risk. Similarly, you could define properties for node accessibility and set lower intervention coverage for nodes that are difficult to access.

Individual properties are also used to simulate health care cascades. For example, you can disqualify an individual who would otherwise receive an intervention; such as treating a segment of the population with a second-line treatment but disqualifying those who haven’t already received the first-line treatment. Then you can change the property value after the treatment has been received.

The NodeProperties section is a top-level section at the same level as Defaults and Nodes that contains parameters that assign properties to nodes in a simulation. The IndividualProperties section is under either Defaults or Nodes and contains parameters that assign properties to individuals in a simulation.

Individual and node properties provides more guidance and Generic model scenarios provides some example scenarios that use properties.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Age_Bin_Edges_In_Years

array

NA

NA

NA

An array of integers that represents the ages, in years, at which to demarcate the age groups for individuals. Used only with the Age_Bin property type. The first number must be 0, the last must be -1, and they must be listed in ascending order. Cannot be used with NodeProperties.

EMOD automatically create the individual property Age_Bin with values based on the bin edges using the format Age_Bin_Property_From_X_To_Y. These appear in the property reports and can be used to target campaign interventions using Property_Restrictions_Within_Node. See Targeting interventions to nodes or individuals for more information.

The following example creates three age groups: 0 to 5, older than 5 to 13, and older than 13 to the maximum age.

{
    "Defaults": {
        "IndividualProperties": [
            {
                "Property": "Age_Bin",
                "Age_Bin_Edges_In_Years": [
                    0,
                    5,
                    13,
                    -1
                ]
            }
        ]
    }
}

IndividualProperties

array of objects

NA

NA

NA

An array that contains parameters that add properties to individuals in a simulation. For example, you can define values for accessibility, age, geography, risk, and other properties and assign values to different individuals. alues.

 
{
    "Defaults": {
        "IndividualProperties": [
            {
                "Property": "InterventionStatus",
                "Values": [
                    "None",
                    "ARTStaging"
                ],
                "Initial_Distribution": [
                    1,
                    0
                ]
            },
            {
                "Property": "Risk",
                "Values": [
                    "High",
                    "Medium",
                    "Low"
                ],
                "Initial_Distribution": [
                    0.2,
                    0.5,
                    0.3
                ]
            }
        ]
    }
}

Initial_Distribution

array of floats

0

1

1

An array of floats that define the proportion of property values to assign to individuals or nodes at the beginning of the simulation and when new individuals are born. Their sum must equal 1 and the number of members in this array must match the number of members in Values. For Age_Bin property types, omit this parameter as the demographics file controls the age distribution.

 
{
    "NodeProperties": [
        {
            "Property": "InterventionStatus",
            "Values": [
                "NONE",
                "RECENT_SPRAY"
            ],
            "Initial_Distribution": [
                1.0,
                0.0
            ]
        }
    ]
}

{
    "Nodes": [
        {
            "NodeID": 25,
            "IndividualProperties": [
                {
                    "Initial_Distribution": [
                        0.2,
                        0.4,
                        0.4
                    ]
                }
            ]
        }
    ]
}

NodeProperties

array of objects

NA

NA

NA

An array that contains parameters that add properties to nodes in a simulation. Defined in the demographics file at the same level as Nodes and Defaults. For example, you can define values for intervention status, risk, and other properties and assign values to different nodes.

 
{
    "NodeProperties": [
        {
            "Property": "Place",
            "Values": [
                "RURAL",
                "URBAN"
            ],
            "Initial_Distribution": [
                0.85,
                0.15
            ]
        },
        {
            "Property": "InterventionStatus",
            "Values": [
                "NONE",
                "SPRAYED_A",
                "SPRAYED_B",
                "FENCE_AND_TRAP"
            ],
            "Initial_Distribution": [
                1,
                0,
                0,
                0
            ]
        }
    ],
    "Nodes": [
        {
            "NodeID": 1,
            "NodeAttributes": {
                "Latitude": 0,
                "Longitude": 0,
                "Altitude": 0,
                "Airport": 0,
                "Region": 1,
                "Seaport": 0,
                "InitialPopulation": 10000,
                "BirthRate": 5.48e-05,
                "NodePropertyValues": [
                    "Place:URBAN"
                ]
            }
        }
    ]
}

Property

enum

NA

NA

NA

The individual or node property type for which you will assign values to create groups. You can then update the property values assigned to individuals or nodes or target interventions to particular groups. Note that these types, with the exception of Age_Bin, are merely labels that do not affect the simulation unless specified to do so. Possible values are:

Age_Bin

Assign individuals to age bins. Use with Age_Bin_Edges_In_Years. Cannot be used with NodeProperties.

Accessibility

Tag individuals or nodes based on their accessibility.

Geographic

Tag individuals or nodes based on geographic characteristics.

HasActiveTB

Tag individuals or nodes based on active TB status. Typically used only with HIV ART staging interventions.

InterventionStatus

Tag individuals or nodes based on intervention status, so that receiving an intervention can affect how other interventions are distributed. Use with Disqualifying_Properties and New_Property_Value in the campaign file.

Place

Tag individuals or nodes based on place.

Risk

Tag individuals or nodes based on disease risk.

QualityofCare

Tag individuals or nodes based on the quality of medical care.

 
{
    "NodeProperties": [
        {
            "Property": "InterventionStatus",
            "Values": [
                "NONE",
                "RECENT_SPRAY"
            ],
            "Initial_Distribution": [
                1.0,
                0.0
            ]
        }
    ]
}

{
    "Defaults": {
        "IndividualProperties": [
            {
                "Property": "Age_Bin",
                "Age_Bin_Edges_In_Years": [
                    0,
                    6,
                    10,
                    20,
                    -1
                ]
            }
        ]
    }
}

Values

array of strings

NA

NA

NA

An array of the user-defined values that can be assigned to individuals or nodes for this property. The order of the values corresponds to the order of the Initial_Distribution array.

You can have up to 125 values for the Geographic and InterventionStatus property types and up to 5 values for all other types. For Age_Bin property types, omit this parameter and use Age_Bin_Edges_In_Years instead.

 
{
    "NodeProperties": [
        {
            "Property": "InterventionStatus",
            "Values": [
                "NONE",
                "RECENT_SPRAY"
            ],
            "Initial_Distribution": [
                1.0,
                0.0
            ]
        }
    ]
}

{
    "Defaults": {
        "IndividualProperties": [
            {
                "Values": [
                    "Low",
                    "Medium",
                    "High"
                ]
            }
        ]
    }
}
NodeAttributes

The NodeAttributes section contains parameters that add or modify information regarding the location, migration, habitat, and population of node. Some NodeAttributes depend on values set in the configuration parameters.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Airport

boolean

0

1

0

Indicates whether or not the node has an airport for air migration from (not to) the node. If set to 1, Enable_Air_Migration in the configuration file must be set to 1 or migration will not occur (see Migration parameters). Primarily used to turn off migration in a particular node.

 
{
    "Defaults": {
        "NodeAttributes": {
            "Airport": 0
        }
    }
}

Altitude

float

-3.40282e+038

3.40282e+038

0

The altitude, in meters, for the node. Required, but only used when Climate_Model is set to CLIMATE_KOPPEN.

 
{
    "Defaults": {
        "NodeAttributes": {
            "Altitude": 250
        }
    }
}

BirthRate

double

0

1

0.00008715

The birth rate, in births per person per day. In the configuration file, Enable_Birth must be set to 1 and Birth_Rate_Dependence will affect how this rate is used (see Population dynamics parameters).

 
{
    "Nodes": [
        {
            "NodeID": 21,
            "NodeAttributes": {
                "BirthRate": 0.0001
            }
        }
    ]
}

InfectivityReservoirEndTime

float

InfectivityReservoirStartTime

3.40282e+038

3.40282e+038

The ending of the exogeneous reservoir of infectivity. This parameter is conditional upon the configuration parameter, Enable_Infectivity_Reservoir, being enabled (set to 1).

 
{
    "NodeAttributes": {
        "InfectivityReservoirSize": 0.1,
        "InfectivityReservoirStartTime": 90,
        "InfectivityReservoirEndTime": 365
    }
}

InfectivityReservoirSize

float

0

3.40282e+038

0

The quantity-per-timestep added to the total infectivity present in a node; it is equivalent to the expected number of additional infections in a node, per timestep. For example, if timestep is equal to a day, then setting InfectivityReservoirSize to a value of 0.1 would introduce an infection every 10 days from the exogenous reservoir. This parameter is conditional upon the configuration parameter, Enable_Infectivity_Reservoir, being enabled (set to 1).

 
{
    "NodeAttributes": {
        "InfectivityReservoirSize": 0.1,
        "InfectivityReservoirStartTime": 90,
        "InfectivityReservoirEndTime": 365
    }
}

InfectivityReservoirStartTime

float

0

3.40282e+038

0

The beginning of the exogeneous reservoir of infectivity. This parameter is conditional upon the configuration parameter, Enable_Infectivity_Reservoir, being enabled (set to 1).

 
{
    "NodeAttributes": {
        "InfectivityReservoirSize": 0.1,
        "InfectivityReservoirStartTime": 90,
        "InfectivityReservoirEndTime": 365
    }
}

InitialPopulation

integer

0

2147480000.0

1000

The number of people that will be populated into the node at the beginning of the simulation. You can scale this number using Base_Population_Scale_Factor in the configuration file (see Population dynamics parameters).

 
{
    "Nodes": [
        {
            "NodeID": 25,
            "NodeAttributes": {
                "InitialPopulation": 1000
            }
        }
    ]
}

InitialVectorsPerSpecies

JSON object

0

2.15e+09

10,000

The number of vectors per species that will be populated into the node at the beginning of the simulation. Population responds to habitat availability that can be scaled by LarvalHabitatMultiplier. Vector_Sampling_Type in the configuration file must be set to TRACK_ALL_VECTORS or SAMPLE_IND_VECTORS.

 
{
    "Nodes": [
        {
            "NodeID": 340461476,
            "NodeAttributes": {
                "InitialVectorsPerSpecies": {
                    "aegypti": 100,
                    "funestus": 0,
                    "gambiae": 0
                }
            }
        }
    ]
}

LarvalHabitatMultiplier

float or nested JSON object

NA

NA

NA

The value by which to scale the larval habitat availability specified in the configuration file with Habitats across all habitat types, for specific habitat types, or for specific mosquito species within each habitat type.

The following example scales the larval habitat equally across all habitat types and mosquito species.

{
    "LarvalHabitatMultiplier": [
        {
            "Habitat": "ALL_HABITATS",
            "Species": "ALL_SPECIES",
            "Factor": 0.2
        }
    ]
}

The following example scales the larval habitat only in the temporary rainfall habitat for all mosquito species.

{
    "LarvalHabitatMultiplier": [
        {
            "Habitat": "TEMPORARY_RAINFALL",
            "Species": "ALL_SPECIES",
            "Factor": 0.2
        }
    ]
}

The following example scales the larval habitat independently for A. gambiae in the temporary rainfall habitat and for A. arabiensis in the brackish swamp habitat.

{
    "LarvalHabitatMultiplier": [
        {
            "Habitat": "TEMPORARY_RAINFALL",
            "Species": "gambiae",
            "Factor": 0.1
        },
        {
            "Habitat": "BRACKISH_SWAMP",
            "Species": "arabiensis",
            "Factor": 0.5
        }
    ]
}

Latitude

float

3.40282e+038

-3.40282e+038

-1

Latitude of the node in decimal degrees. This can be used for several things, including determining infectiousness by latitude and defining the size of grid cells.

 
{
    "Nodes": [
        {
            "NodeID": 25,
            "NodeAttributes": {
                "Latitude": 12.4,
                "Longitude": 9.35
            }
        }
    ]
}

Longitude

float

-3.40282e+38

3.40282e+38

-1

Longitude of the node in decimal degrees. This can be used for several things, including defining the size of grid cells.

 
{
    "Nodes": [
        {
            "NodeID": 254,
            "NodeAttributes": {
                "Latitude": 25.4,
                "Longitude": 9.1
            }
        }
    ]
}

NodeAttributes

JSON object

NA

NA

NA

The structure that contains parameters that add or modify information regarding the location, migration, habitat, and population of a simulation. Some NodeAttributes depend on values set in the configuration parameters.

 
{
    "Nodes": [
        {
            "NodeID": 1487548419,
            "NodeAttributes": {
                "Latitude": 12.4208,
                "Longitude": 9.15417
            }
        }
    ]
}

PercentageVectorsBySpecies

JSON object

NA

NA

NA

A list of key-value pairs of the vector species and percentage of the total they each make up.

 
{
    "Defaults": {
        "NodeAttributes": {
            "PercentageVectorsBySpecies": {
                "arabiensis": 0.3,
                "funestus": 0.3,
                "gambiae": 0.4
            }
        }
    }
}

Region

boolean

0

1

0

Indicates whether or not the node has a road network for regional migration from (not to) the node. If set to 1, Enable_Regional_Migration in the configuration file must be set to 1 or migration will not occur (see Migration parameters). Primarily used to turn off migration in particular nodes.

 
{
    "Nodes": [
        {
            "NodeID": 12,
            "NodeAttributes": {
                "Region": 1
            }
        }
    ]
}

Seaport

boolean

0

1

0

Indicates whether or not the node is connected by sea migration from (not to) the node. If set to 1, Enable_Sea_Migration in the configuration file must be set to 1 or migration will not occur (see Migration parameters). Primarily used to turn off migration in particular nodes.

 
{
    "Nodes": [
        {
            "NodeID": 43,
            "NodeAttributes": {
                "Seaport": 1
            }
        }
    ]
}
IndividualAttributes

The IndividualAttributes section contains parameters that initialize the distribution of attributes across individuals, such as the age or immunity. An initial value for an individual is a randomly selected value from a given distribution. These distributions can be configured using a simple flag system of three parameters or a complex system of many more parameters. The following table contains the parameters that can be used with either distribution system.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

IndividualAttributes

JSON object

NA

NA

NA

The structure that contains parameters that add or modify the distribution of attributes across individuals in a simulation. For example, the age or immunity distribution. An initial value for an individual is a randomly selected value from a distribution. For example, if you use a uniform distribution to initialize age, the initial ages of individuals in the simulation will be evenly distributed between some minimum and maximum value. These distributions can be set using Simple distributions or Complex distributions.

 
{
    "Defaults": {
        "IndividualAttributes": {
            "AgeDistributionFlag": 0,
            "AgeDistribution1": 25550,
            "AgeDistribution2": 0
        }
    }
}

PercentageChildren

float

0

1

NA

The percentage of individuals in the node that are children. Set Minimum_Adult_Age_Years to determine the age at which individuals transition to adults.

 
{
    "Nodes": {
        "NodeID": 45,
        "IndividualAttributes": {
            "PercentageChildren": 0.7
        }
    }
}
Simple distributions

Simple distributions are defined by three parameters where one is a flag for the distribution type and the other two are used to further define the distribution. For example, if you set the age flag to a uniform distribution, the initial ages of individuals in the simulation will be evenly distributed between some minimum and maximum value as defined by the other two parameters.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

AgeDistribution1

float

-3.40282e+038

3.40282e+038

0.000118

The first value in the age distribution, the meaning of which depends upon the value set in AgeDistributionFlag. The table below shows the flag value and corresponding distribution value.

AgeDistributionFlag

AgeDistribution1

0

Age, in days, to assign to all individuals.

1

Minimum age, in days, for a uniform distribution.

2

Mean age, in days, for a Gaussian distribution.

3

Exponential decay rate.

4

Mean age, in days, for a Poisson distribution.

5

Mu (the mean of the natural log) for a log normal distribution.

6

Proportion of individuals in the second, user-defined age bin vs. the first age bin (1 day) for a bimodal distribution. Must be between 0 and 1.

7

Scale parameter for a Weibull distribution.

Age_Initialization_Distribution_Type in the configuration file must be set to DISTRIBUTION_SIMPLE (see Population dynamics parameters).

 
{
    "IndividualAttributes": {
        "AgeDistributionFlag": 0,
        "AgeDistribution1": 25550,
        "AgeDistribution2": 0
    }
}

AgeDistribution2

float

-3.40282e+038

3.40282e+038

0

The second value in the age distribution, the meaning of which depends upon the value set in AgeDistributionFlag. The table below shows the flag value and corresponding distribution value.

AgeDistributionFlag

AgeDistribution2

0

NA, set to 0.

1

Maximum age, in days, for a uniform distribution.

2

Standard deviation in age, in days, for a Gaussian distribution.

3

NA, set to 0.

4

NA, set to 0.

5

Sigma (the standard deviation of the natural log) for a log normal distribution.

6

The age, in days, of individuals in the second age bin for a bimodal distribution.

7

Shape parameter for a Weibull distribution.

Age_Initialization_Distribution_Type in the configuration file must be set to DISTRIBUTION_SIMPLE (see Population dynamics parameters).

 
{
    "IndividualAttributes": {
        "AgeDistributionFlag": 0,
        "AgeDistribution1": 25550,
        "AgeDistribution2": 0
    }
}

AgeDistributionFlag

integer

0

7

3

The type of distribution to use for age. Possible values are:

  • 0 (Constant, everyone in the population is the same age.)

  • 1 (Uniform, ages are randomly drawn between a minimum and maximum value.)

  • 2 (Gaussian)

  • 3 (Exponential)

  • 4 (Poisson)

  • 5 (Log normal)

  • 6 (Bimodal, non-continuous with some individuals 1 day old and others a user-defined age.)

  • 7 (Weibull)

Age_Initialization_Distribution_Type in the configuration file must be set to DISTRIBUTION_SIMPLE (see Population dynamics parameters).

 
{
    "IndividualAttributes": {
        "AgeDistributionFlag": 0,
        "AgeDistribution1": 25550,
        "AgeDistribution2": 0
    }
}

MigrationHeterogeneityDistribution1

float

-3.40282e+38

3.40282e+38

1

The first value in the migration heterogeneity distribution, the meaning of which depends upon the value set in MigrationHeterogeneityFlag. The table below shows the flag value and corresponding distribution value.

MigrationHeterogeneityDistributionFlag

MigrationHeterogeneityDistribution1

0

Migration heterogeneity value to assign.

1

Minimum migration heterogeneity for a uniform distribution.

2

Mean migration heterogeneity for a Gaussian distribution.

3

Exponential decay rate.

4

Mean migration heterogeneity for a Poisson distribution.

5

Mu (the mean of the natural log) for a log normal distribution.

6

Proportion of individuals in the second, user-defined migration heterogeneity bin vs. the first migration heterogeneity bin (value of 1 ) for a bimodal distribution. Must be between 0 and 1.

7

Scale parameter for a Weibull distribution.

Enable_Migration_Heterogeneity in the configuration file must be set to 1 (see Migration parameters).

 
{
    "IndividualAttributes": {
        "MigrationHeterogeneityDistributionFlag": 0,
        "MigrationHeterogeneityDistribution1": 1,
        "MigrationHeterogeneityDistribution2": 0
    }
}

MigrationHeterogeneityDistribution2

float

-3.40282e+038

3.40282e+038

0

The second value in the distribution, the meaning of which depends upon the value set in MigrationHeterogeneityDistributionFlag. The table below shows the flag value and corresponding distribution value.

MigrationHeterogeneityDistributionFlag

MigrationHeterogeneityDistribution2

0

NA, set to 0.

1

Maximum migration heterogeneity for a uniform distribution.

2

Standard deviation in migration heterogeneity for a Gaussian distribution.

3

NA, set to 0.

4

NA, set to 0.

5

Sigma (the standard deviation of the natural log) for a log normal distribution.

6

The migration heterogeniety of individuals in the second migration heterogeniety bin for a bimodal distribution.

7

Shape parameter for a Weibull distribution.

Enable_Migration_Heterogeneity in the configuration file must be set to 1 (see Migration parameters).

 
{
    "IndividualAttributes": {
        "MigrationHeterogeneityDistributionFlag": 0,
        "MigrationHeterogeneityDistribution1": 1,
        "MigrationHeterogeneityDistribution2": 0
    }
}

MigrationHeterogeneityDistributionFlag

integer

0

7

0

The type of distribution to use for migration heterogeneity. Possible values are:

  • 0 (Constant, everyone in the population has the same migration value.)

  • 1 (Uniform, migration values are randomly drawn between a minimum and maximum value.)

  • 2 (Gaussian)

  • 3 (Exponential)

  • 4 (Poisson)

  • 5 (Log normal)

  • 6 (Bimodal, non-continuous with some individuals with a migration heterogeniety of 1 and others a user-defined value.)

  • 7 (Weibull)

Enable_Migration_Heterogeneity in the configuration file must be set to 1 (see Migration parameters).

 
{
    "IndividualAttributes": {
        "MigrationHeterogeneityDistributionFlag": 0,
        "MigrationHeterogeneityDistribution1": 1,
        "MigrationHeterogeneityDistribution2": 0
    }
}

PrevalenceDistribution1

float

-3.40282e+038

3.40282e+038

1

The first value in the prevalence distribution, the meaning of which depends upon the value set in PrevalenceDistributionFlag. The table below shows the flag value and corresponding distribution value.

PrevalenceDistributionFlag

PrevalenceDistribution1

0

Prevalence value to assign.

1

Minimum prevalence for a uniform distribution.

2

Mean prevalence for a Gaussian distribution.

3

Exponential decay rate.

4

Mean prevalence for a Poisson distribution.

5

Mu (the mean of the natural log) for a log normal distribution.

6

Proportion of individuals in the second, user-defined prevalence bin vs. the first prevalence bin (value of 1) for a bimodal distribution. Must be between 0 and 1.

7

Scale parameter for a Weibull distribution.

 
{
    "IndividualAttributes": {
        "PrevalenceDistributionFlag": 0,
        "PrevalenceDistribution1": 0.0,
        "PrevalenceDistribution2": 0.0
    }
}

PrevalenceDistribution2

float

-3.40282e+038

3.40282e+038

0

The second value in the distribution, the meaning of which depends upon the value set in PrevalenceDistributionFlag. The table below shows the flag value and corresponding distribution value.

PrevalenceDistributionFlag

PrevalenceDistribution2

0

NA, set to 0.

1

Maximum prevalence for a uniform distribution.

2

Standard deviation in prevalence for a Gaussian distribution.

3

NA, set to 0.

4

NA, set to 0.

5

Sigma (the standard deviation of the natural log) for a log normal distribution.

6

The prevalence for individuals in the second prevalence bin for a bimodal distribution.

7

Shape parameter for a Weibull distribution.

 
{
    "IndividualAttributes": {
        "PrevalenceDistributionFlag": 0,
        "PrevalenceDistribution1": 0.0,
        "PrevalenceDistribution2": 0.0
    }
}

PrevalenceDistributionFlag

integer

0

7

0

The type of distribution to use for prevalence. Possible values are:

  • 0 (Constant, everyone in the population has the same prevalence.)

  • 1 (Uniform, prevalence is randomly drawn between a minimum and maximum value.)

  • 2 (Gaussian)

  • 3 (Exponential)

  • 4 (Poisson)

  • 5 (Log normal)

  • 6 (Bimodal, non-continuous with some individuals having a prevalence of 1 and others a user-defined prevalence.)

  • 7 (Weibull)

 
{
    "IndividualAttributes": {
        "PrevalenceDistributionFlag": 0,
        "PrevalenceDistribution1": 0.0,
        "PrevalenceDistribution2": 0.0
    }
}

RiskDistribution1

float

-3.40282e+038

3.40282e+038

0

The first value in the risk distribution, the meaning of which depends upon the value set in RiskDistributionFlag. The table below shows the flag value and corresponding distribution value.

RiskDistributionFlag

RiskDistribution1

0

Risk value to assign.

1

Minimum risk for a uniform distribution.

2

Mean risk for a Gaussian distribution.

3

Exponential decay rate.

4

Mean risk for a Poisson distribution.

5

Mu (the mean of the natural log) for a log normal distribution.

6

Proportion of individuals in the second, user-defined risk bin vs. the first risk bin (value of 1) for a bimodal distribution. Must be between 0 and 1.

7

Scale parameter for a Weibull distribution.

 
{
    "IndividualAttributes": {
        "RiskDistributionFlag": 0,
        "RiskDistribution1": 1,
        "RiskDistribution2": 0
    }
}

RiskDistribution2

float

-3.40282e+038

3.40282e+038

0

The second value in the distribution, the meaning of which depends upon the value set in RiskDistributionFlag. The table below shows the flag value and corresponding distribution value.

:header: RiskDistributionFlag, RiskDistribution2 :widths: 1, 3

0

NA, set to 0.

1

Maximum risk for a uniform distribution.

2

Standard deviation in risk for a Gaussian distribution.

3

NA, set to 0.

4

NA, set to 0.

5

Sigma (the standard deviation of the natural log) for a log normal distribution.

6

The risk of individuals in the second risk bin for a bimodal distribution.

7

Shape parameter for a Weibull distribution.

 
{
    "IndividualAttributes": {
        "RiskDistributionFlag": 0,
        "RiskDistribution1": 1,
        "RiskDistribution2": 0
    }
}

RiskDistributionFlag

integer

0

7

0

The type of distribution to use for risk. Possible values are:

  • 0 (Constant, everyone in the population has the same risk.)

  • 1 (Uniform, risk is randomly drawn between a minimum and maximum value.)

  • 2 (Gaussian)

  • 3 (Exponential)

  • 4 (Poisson)

  • 5 (Log normal)

  • 6 (Bimodal, non-continuous with some individuals with a risk of 1 and others a user-defined risk.)

  • 7 (Weibull)

Enable_Demographics_Risk must be set to 1 (see Population dynamics parameters).

 
{
    "IndividualAttributes": {
        "RiskDistributionFlag": 0,
        "RiskDistribution1": 1,
        "RiskDistribution2": 0
    }
}

SusceptibilityDistribution1

float

-3.40282e+038

3.40282e+038

0

The first value in the susceptibility distribution, the meaning of which depends upon the value set in SusceptibilityDistributionFlag. The table below shows the flag value and corresponding distribution value.

SusceptibilityDistributionFlag

SusceptibilityDistribution1

0

Probability of full susceptibility.

1

Minimum probability of susceptibility for a uniform distribution.

6

Proportion of individuals in the second, user-defined susceptibility bin vs. the first susceptibility bin (value of 1) for a bimodal distribution. Must be between 0 and 1.

In the configuration file, Enable_Immunity must be set to 1 and Susceptibility_Initialization_Distribution_Type must be set to DISTRIBUTION_SIMPLE (see Immunity parameters).

 
{
    "IndividualAttributes": {
        "SusceptibilityDistributionFlag": 0,
        "SusceptibilityDistribution1": 1,
        "SusceptibilityDistribution2": 0
    }
}

SusceptibilityDistribution2

float

-3.40282e+038

3.40282e+038

0

The second value in the susceptibility distribution, the meaning of which depends upon the value set in SusceptibilityDistributionFlag. The table below shows the flag value and corresponding distribution value.

SusceptibilityDistributionFlag

SusceptibilityDistribution2

0

NA, set to 0.

1

Maximum susceptibility for a uniform distribution.

6

The susceptibility values of individuals in the second susceptibility bin for a bimodal distribution.

In the configuration file, Enable_Immunity must be set to 1 and Susceptibility_Initialization_Distribution_Type must be set to DISTRIBUTION_SIMPLE (see Immunity parameters).

 
{
    "IndividualAttributes": {
        "SusceptibilityDistributionFlag": 0,
        "SusceptibilityDistribution1": 1,
        "SusceptibilityDistribution2": 0
    }
}

SusceptibilityDistributionFlag

integer

0

7

0

The type of distribution to use for determining an individual’s probability of full susceptibility. Possible values are:

  • 0 (Constant, everyone in the population has the same probability of susceptibility.)

  • 1 (Uniform, the probability of susceptibility is randomly drawn between a minimum and maximum value.)

  • 6 (Bimodal, non-continuous with some individuals’ probability of susceptibility at 1 and others with a user-defined probability.)

In the configuration file, Enable_Immunity must be set to 1 and Susceptibility_Initialization_Distribution_Type must be set to DISTRIBUTION_SIMPLE (see Immunity parameters).

 
{
    "IndividualAttributes": {
        "SusceptibilityDistributionFlag": 0,
        "SusceptibilityDistribution1": 1,
        "SusceptibilityDistribution2": 0
    }
}
Complex distributions

Complex distributions are more effort to configure, but are useful for representing real-world data where the distribution does not fit a standard. Individual attribute values are drawn from a piecewise linear distribution. The distribution is configured using arrays of axes (such as gender or age) and values at points along each of these axes. This allows you to have different distributions for different groups in the population.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

AgeDistribution

JSON object

NA

NA

NA

The structure defining a complex age distribution. Age_Initialization_Distribution_Type in the configuration file must be set to DISTRIBUTION_COMPLEX.

The following example shows at age distribution in which 25% of individuals are under age 5, 50% are between 5 and 20, and 25% are between 20 and 35.

{
    "IndividualAttributes": {
        "AgeDistribution": {
            "ResultUnits": "years",
            "ResultScaleFactor": 365,
            "ResultValues": [
                0,
                0.25,
                0.75,
                1
            ],
            "DistributionValues": [
                0,
                5,
                20,
                35
            ]
        }
    }
}

AxisNames

array of strings

NA

NA

NA

An array of the names used for each axis of a complex distribution. The list below shows the axis names to use (in the order given) for each of the distribution types:

MortalityDistribution

[“gender”, “age”] Death_Rate_Dependence in the configuration file must be set to NONDISEASE_MORTALITY_BY_AGE_AND_GENDER (see Mortality and survival parameters).

MortalityDistributionMale

[“age”, “year”] Death_Rate_Dependence must be set to NONDISEASE_MORTALITY_BY_YEAR_AND_AGE_FOR_EACH_GENDER (see Mortality and survival parameters).

MortalityDistributionFemale

[“age”, “year”] Death_Rate_Dependence must be set to NONDISEASE_MORTALITY_BY_YEAR_AND_AGE_FOR_EACH_GENDER (see Mortality and survival parameters).

FertilityDistribution

Two options are available:

  • [“urban”, “age”] Birth_Rate_Dependence in the configuratIon file must be set to INDIVIDUAL_PREGNANCIES_BY_URBAN_AND_AGE (see Population dynamics parameters).

  • [“age”, “year”] Birth_Rate_Dependence must be set to INDIVIDUAL_PREGNANCIES_BY_AGE_AND_YEAR (see Population dynamics parameters).

ImmunityDistribution

[“age”]

AgeDistribution

No axes.

[“age”] is the only value accepted for all malaria-specific distributions:

  • MSP_mean_antibody_distribution

  • MSP_variance_antibody_distribution

  • nonspec_mean_antibody_distribution

  • nonspec_variance_antibody_distribution

  • PfEMP1_mean_antibody_distribution

  • PfEMP1_variance_antibody_distribution

 
{
    "IndividualAttributes": {
        "MortalityDistribution": {
            "AxisNames": [
                "gender",
                "age"
            ],
            "AxisUnits": [
                "male=0,female=1",
                "years"
            ],
            "AxisScaleFactors": [
                1,
                365
            ],
            "NumPopulationGroups": [
                2,
                1
            ],
            "PopulationGroups": [
                [
                    0,
                    1
                ],
                [
                    0
                ]
            ],
            "ResultUnits": "annual deaths per 1000 individuals",
            "ResultScaleFactor": 2.739726027397e-06,
            "ResultValues": [
                [
                    0
                ],
                [
                    0
                ]
            ]
        }
    }
}

AxisScaleFactors

array of floats

3.40282e+038

-3.40282e+038

1

A list of the scale factors used to convert axis units to data measurements in a complex distribution. For example, 365 to convert daily mortality to annual mortality. The array must contain one factor for each axis.

 
{
    "IndividualAttributes": {
        "MortalityDistribution": {
            "AxisNames": [
                "gender",
                "age"
            ],
            "AxisUnits": [
                "male=0,female=1",
                "years"
            ],
            "AxisScaleFactors": [
                1,
                365
            ],
            "NumPopulationGroups": [
                2,
                1
            ],
            "PopulationGroups": [
                [
                    0,
                    1
                ],
                [
                    0
                ]
            ],
            "ResultUnits": "annual deaths per 1000 individuals",
            "ResultScaleFactor": 2.739726027397e-06,
            "ResultValues": [
                [
                    0
                ],
                [
                    0
                ]
            ]
        }
    }
}

AxisUnits

array of strings

NA

NA

NA

An array that describes the scale factors used to convert the units for the axes into the units expected by EMOD. For example, when age is provided in years but must be scaled to days. EMOD does not use this value; it is only informational.

 
{
    "IndividualAttributes": {
        "MortalityDistribution": {
            "AxisNames": [
                "gender",
                "age"
            ],
            "AxisUnits": [
                "male=0,female=1",
                "years"
            ],
            "AxisScaleFactors": [
                1,
                365
            ]
        }
    }
}

DistributionValues

array of floats

0

1

1

An array of values between 0 and 1 listed in ascending order that defines a complex age distribution. Each value represents the proportion of the population below that age and the difference between two successive values is the proportion of the population in the age bin defined in ResultValues. Age_Initialization_Distribution_Type in the configuration file must be set to DISTRIBUTION_COMPLEX (see Population dynamics parameters).

The following example shows at age distribution in which 25% of individuals are under age 5, 50% are between 5 and 20, and 25% are between 20 and 35.

{
    "IndividualAttributes": {
        "AgeDistribution": {
            "ResultUnits": "years",
            "ResultScaleFactor": 365,
            "AxisScaleFactors": 1,
            "DistributionValues": [
                0,
                0.25,
                0.75,
                1
            ],
            "ResultValues": [
                0,
                5,
                20,
                35
            ]
        }
    }
}

FertilityDistribution

JSON object

NA

NA

NA

The distribution of the fertility rate in the population. Enable_Birth in the configuration file must be set to 1 (see Population dynamics parameters).

 
{
    "IndividualAttributes": {
        "FertilityDistribution": {
            "NumDistributionAxes": 2,
            "AxisNames": [
                "urban",
                "XXX"
            ],
            "AxisUnits": [
                "rural=0, urban=1",
                "years"
            ],
            "AxisScaleFactors": [
                1,
                365
            ],
            "NumPopulationGroups": [
                2,
                9
            ],
            "PopulationGroups": [
                [
                    0,
                    1
                ],
                [
                    0,
                    15,
                    20,
                    25,
                    30,
                    35,
                    40,
                    45,
                    49
                ]
            ],
            "ResultUnits": "annual births per 1000 individuals",
            "ResultScaleFactor": 2.739726027397e-06,
            "ResultValues": [
                [
                    0,
                    28.4,
                    190.3,
                    222.4,
                    155.4,
                    68,
                    21.9,
                    3.6,
                    0
                ],
                [
                    0,
                    28.4,
                    190.3,
                    222.4,
                    155.4,
                    68,
                    21.9,
                    3.6,
                    0
                ]
            ]
        }
    }
}

ImmunityDistribution

JSON object

NA

NA

NA

The structure defining a complex immunity distribution. Immunity_Initialization_Distribution_Type in the configuration file must be set to DISTRIBUTION_COMPLEX (see Immunity parameters).

 
{
    "IndividualAttributes": {
        "ImmunityDistribution": {
            "AxisNames": [
                "age"
            ],
            "AxisUnits": [
                "years"
            ],
            "AxisScaleFactors": [
                365
            ],
            "NumPopulationGroups": [
                1
            ],
            "PopulationGroups": [
                [
                    0
                ]
            ],
            "ResultScaleFactor": 3.6952,
            "ResultValues": [
                [
                    0
                ]
            ]
        }
    }
}

MortalityDistribution

JSON object

NA

NA

NA

The distribution of non-disease mortality for a population. Death_Rate_Dependence in the configuration file must be set to NONDISEASE_MORTALITY_BY_AGE_AND_GENDER or NONDISEASE_MORTALITY_BY_YEAR_AND_AGE_FOR_EACH_GENDER (see Mortality and survival parameters).

Warning

Mortality is sampled every 30 days. To correctly attribute neonatal deaths to days 0-30, you must indicate that the threshold for the first age group in PopulationGroups is less than 30 days.

 
{
    "IndividualAttributes": {
        "MortalityDistribution": {
            "AxisNames": [
                "gender",
                "age"
            ],
            "AxisScaleFactors": [
                1,
                1
            ],
            "NumDistributionAxes": 2,
            "NumPopulationGroups": [
                2,
                4
            ],
            "PopulationGroups": [
                [
                    0,
                    1
                ],
                [
                    0.0,
                    29.99,
                    365,
                    1826
                ]
            ],
            "ResultScaleFactor": 1,
            "ResultValues": [
                [
                    0.0016,
                    0.000107,
                    6.3e-05,
                    100.0
                ],
                [
                    0.0016,
                    0.000107,
                    6.3e-05,
                    100.0
                ]
            ]
        }
    }
}

MSP_mean_antibody_distribution

JSON object

NA

NA

NA

The mean of the fraction of the antigenic variants of the anti-MSP antibody that the immune system has been exposed to, binned by age using PopulationGroups. ResultValues are bounded between 0 and 1, typically increasing with age. Immunity_Initialization_Distribution_Type in the configuration file must be set to DISTRIBUTION_COMPLEX.

 
{
    "IndividualAttributes": {
        "MSP_mean_antibody_distribution": {
            "AxisNames": [
                "age"
            ],
            "AxisUnits": [
                "years"
            ],
            "AxisScaleFactors": [
                365
            ],
            "NumPopulationGroups": [
                11
            ],
            "PopulationGroups": [
                [
                    0,
                    0.5,
                    1.5,
                    2.5,
                    3.5,
                    4.5,
                    7.5,
                    15,
                    25,
                    35,
                    60
                ]
            ],
            "ResultUnits": "mean fraction of antibody variants",
            "ResultScaleFactor": 1,
            "ResultValues": [
                0,
                0.0668,
                0.1499,
                0.2279,
                0.2988,
                0.3743,
                0.5576,
                0.8248,
                0.8866,
                0.8748,
                0.8723
            ]
        }
    }
}

MSP_variance_antibody_distribution

JSON object

NA

NA

NA

The variance of the fraction of the antigenic variants of the anti-MSP antibody that the immune system has been exposed to, binned by age using PopulationGroups. ResultValues are bounded between 0 and 1, typically increasing with age. Immunity_Initialization_Distribution_Type in the configuration file must be set to DISTRIBUTION_COMPLEX.

 
{
    "IndividualAttributes": {
        "MSP_variance_antibody_distribution": {
            "AxisNames": [
                "age"
            ],
            "AxisUnits": [
                "years"
            ],
            "AxisScaleFactors": [
                365
            ],
            "NumPopulationGroups": [
                11
            ],
            "PopulationGroups": [
                [
                    0,
                    0.5,
                    1.5,
                    2.5,
                    3.5,
                    4.5,
                    7.5,
                    15,
                    25,
                    35,
                    60
                ]
            ],
            "ResultUnits": "mean fraction of antibody variants",
            "ResultScaleFactor": 1,
            "ResultValues": [
                0,
                0.027,
                0.029,
                0.0271,
                0.0309,
                0.0323,
                0.0899,
                0.0726,
                0.0285,
                0.0267,
                0.0279
            ]
        }
    }
}

nonspec_mean_antibody_distribution

JSON object

NA

NA

NA

The mean of the fraction of the antigenic variants of non-specific malaria antibodies that the immune system has been exposed to, binned by age using PopulationGroups. ResultValues are bounded between 0 and 1, typically increasing with age. Immunity_Initialization_Distribution_Type in the configuration file must be set to DISTRIBUTION_COMPLEX.

 
{
    "IndividualAttributes": {
        "nonspec_mean_antibody_distribution": {
            "AxisNames": [
                "age"
            ],
            "AxisUnits": [
                "years"
            ],
            "AxisScaleFactors": [
                365
            ],
            "NumPopulationGroups": [
                11
            ],
            "PopulationGroups": [
                [
                    0,
                    0.5,
                    1.5,
                    2.5,
                    3.5,
                    4.5,
                    7.5,
                    15,
                    25,
                    35,
                    60
                ]
            ],
            "ResultUnits": "mean fraction of antibody variants",
            "ResultScaleFactor": 1,
            "ResultValues": [
                0,
                0.272,
                0.5547,
                0.7226,
                0.8429,
                0.9186,
                0.9782,
                0.9991,
                1.0,
                1.0,
                1.0
            ]
        }
    }
}

nonspec_variance_antibody_distribution

JSON object

NA

NA

NA

The variance of the fraction of the antigenic variants of non-specific malaria antibodies that the immune system has been exposed to, binned by age using PopulationGroups. ResultValues are bounded between 0 and 1, typically increasing with age. Immunity_Initialization_Distribution_Type in the configuration file must be set to DISTRIBUTION_COMPLEX.

 
{
    "IndividualAttributes": {
        "nonspec_variance_antibody_distribution": {
            "AxisNames": [
                "age"
            ],
            "AxisUnits": [
                "years"
            ],
            "AxisScaleFactors": [
                365
            ],
            "NumPopulationGroups": [
                11
            ],
            "PopulationGroups": [
                [
                    0,
                    0.5,
                    1.5,
                    2.5,
                    3.5,
                    4.5,
                    7.5,
                    15,
                    25,
                    35,
                    60
                ]
            ],
            "ResultUnits": "mean fraction of antibody variants",
            "ResultScaleFactor": 1,
            "ResultValues": [
                0,
                0.1146,
                0.1008,
                0.0795,
                0.0781,
                0.055,
                0.0337,
                0.0067,
                0,
                0,
                0
            ]
        }
    }
}

NumDistributionAxes

integer

1

NA

NA

The number of axes to use for a complex distribution. EMOD does not use this value; it is only informational.

 
{
    "IndividualAttributes": {
        "MortalityDistribution": {
            "NumDistributionAxes": 2,
            "AxisNames": [
                "gender",
                "age"
            ],
            "AxisScaleFactors": [
                1,
                365
            ]
        }
    }
}

NumPopulationGroups

array of integers

NA

NA

NA

An array of population groupings for each independent variable for a complex distribution. This variable defines the number of columns for each row in the population group table. The number of values in the array is often two, representing the values for gender and number of age bins. EMOD does not use this value; it is only informational.

 
{
    "IndividualAttributes": {
        "MortalityDistribution": {
            "AxisNames": [
                "gender",
                "age"
            ],
            "AxisUnits": [
                "male=0,female=1",
                "years"
            ],
            "AxisScaleFactors": [
                1,
                365
            ],
            "NumPopulationGroups": [
                2,
                1
            ],
            "PopulationGroups": [
                [
                    0,
                    1
                ],
                [
                    0
                ]
            ],
            "ResultUnits": "annual deaths per 1000 individuals",
            "ResultScaleFactor": 2.739726027397e-06,
            "ResultValues": [
                [
                    0
                ],
                [
                    0
                ]
            ]
        }
    }
}

PfEMP1_mean_antibody_distribution

JSON object

NA

NA

NA

The mean of the fraction of the antigenic variants of the PfEMP1 antibody that the immune system has been exposed to, binned by age using PopulationGroups. ResultValues are bounded between 0 and 1, typically increasing with age. Immunity_Initialization_Distribution_Type in the configuration file must be set to DISTRIBUTION_COMPLEX.

 
{
    "IndividualAttributes": {
        "PfEMP1_mean_antibody_distribution": {
            "NumDistributionAxes": 1,
            "AxisNames": [
                "age"
            ],
            "AxisUnits": [
                "years"
            ],
            "AxisScaleFactors": [
                365
            ],
            "NumPopulationGroups": [
                11
            ],
            "PopulationGroups": [
                [
                    0,
                    0.5,
                    1.5,
                    2.5,
                    3.5,
                    4.5,
                    7.5,
                    15,
                    25,
                    35,
                    60
                ]
            ],
            "ResultUnits": "mean fraction of antibody variants",
            "ResultScaleFactor": 1,
            "ResultValues": [
                0,
                0.1265,
                0.3071,
                0.4547,
                0.568,
                0.6627,
                0.809,
                0.9405,
                0.9772,
                0.9796,
                0.9801
            ]
        }
    }
}

PfEMP1_variance_antibody_distribution

JSON object

NA

NA

NA

The variance of the fraction of the antigenic variants of the PfEMP1 antibody that the immune system has been exposed to, binned by age using PopulationGroups. ResultValues are bounded between 0 and 1, typically increasing with age. Immunity_Initialization_Distribution_Type in the configuration file must be set to DISTRIBUTION_COMPLEX.

 
{
    "IndividualAttributes": {
        "PfEMP1_variance_antibody_distribution": {
            "AxisNames": [
                "age"
            ],
            "AxisUnits": [
                "years"
            ],
            "AxisScaleFactors": [
                365
            ],
            "NumPopulationGroups": [
                11
            ],
            "PopulationGroups": [
                [
                    0,
                    0.5,
                    1.5,
                    2.5,
                    3.5,
                    4.5,
                    7.5,
                    15,
                    25,
                    35,
                    60
                ]
            ],
            "ResultUnits": "mean fraction of antibody variants",
            "ResultScaleFactor": 1,
            "ResultValues": [
                0,
                0.0606,
                0.0509,
                0.0386,
                0.0334,
                0.0274,
                0.0575,
                0.0261,
                0.005,
                0.0045,
                0.0044
            ]
        }
    }
}

PopulationGroups

matrix of integers

NA

NA

NA

An array in which each row represents one of the distribution axes and contains the values that the independent variable can take. The values must be listed in ascending order and each defines the left edge of the bin.

Warning

Mortality is sampled every 30 days. To correctly attribute neonatal deaths to days 0-30, you must indicate that the threshold for the first age group in PopulationGroups is less than 30 days.

The following example configures relatively high infant mortality and lower mortality at ages 10 and 40, with everyone dead by age 120.

{
    "IndividualAttributes": {
        "MortalityDistribution": {
            "AxisNames": [
                "gender",
                "age"
            ],
            "AxisUnits": [
                "male=0,female=1",
                "years"
            ],
            "AxisScaleFactors": [
                1,
                365
            ],
            "NumPopulationGroups": [
                2,
                1
            ],
            "PopulationGroups": [
                [
                    0,
                    1
                ],
                [
                    0,
                    10,
                    40,
                    120
                ]
            ],
            "ResultUnits": "annual deaths per 1000 individuals",
            "ResultScaleFactor": 2.739726027397e-06,
            "ResultValues": [
                [
                    51.6,
                    3.7,
                    5.3,
                    1000
                ],
                [
                    60.1,
                    4.1,
                    4.8,
                    1000
                ]
            ]
        }
    }
}

ResultScaleFactor

float

-3.40282e+038

3.40282e+038

1

The scale factor used to convert ResultUnits to number of births, deaths, or another variable per individual per day.

 
{
    "IndividualAttributes": {
        "AgeDistribution": {
            "AxisScaleFactors": 1,
            "DistributionValues": [
                0.99,
                1.0
            ],
            "ResultScaleFactor": 365,
            "ResultUnits": "years",
            "ResultValues": [
                0.0027,
                0.0027
            ]
        }
    }
}

ResultUnits

string

NA

NA

NA

A string that indicates the units used for the ResultValues parameter of a complex distribution. EMOD does not use this value; it is only informational. The values here are scaled by the value in ResultScaleFactor before being passed to EMOD as a daily rate.

 
{
    "IndividualAttributes": {
        "MortalityDistribution": {
            "NumPopulationGroups": [
                2,
                1
            ],
            "PopulationGroups": [
                [
                    0,
                    1
                ],
                [
                    0
                ]
            ],
            "ResultUnits": "annual deaths per 1000 individuals",
            "ResultScaleFactor": 2.739726027397e-06,
            "ResultValues": [
                [
                    0
                ],
                [
                    0
                ]
            ]
        }
    }
}

ResultValues

array of floats

NA

NA

NA

An array in which each row represents one of the distribution axes and contains the dependent variable values. The units are configurable; the values are scaled by the value in ResultScaleFactor before being passed to EMOD in units of days.

For age distributions, it lists in ascending order the ages at which to bin the population. The corresponding values in DistributionValues represent the proportion of the population that is below that age. If the first member of the array is non-zero, the first bin is defined as those with that exact value (EMOD does not assume the bins start at zero).

For all other distributions, an array in which each row represents the values for a combination of axes. For example, a mortality distribution that includes both gender and age axes will have a row for males and a row for females that each contain the mortality rate at various ages set in PopulationGroups.

The following example shows an age distribution in which 10% of individuals are newborn, 25% are under age 5, 50% are between 5 and 20, and 25% are between 20 and 35.

{
    "IndividualAttributes": {
        "AgeDistribution": {
            "DistributionValues": [
                0.1,
                0.25,
                0.75,
                1
            ],
            "ResultValues": [
                0,
                5,
                20,
                35
            ]
        }
    }
}

The following example configures relatively high infant mortality and lower mortality at ages 10 and 40, with everyone dead by age 120.

{
    "IndividualAttributes": {
        "MortalityDistribution": {
            "AxisNames": [
                "gender",
                "age"
            ],
            "AxisUnits": [
                "male=0,female=1",
                "years"
            ],
            "AxisScaleFactors": [
                1,
                365
            ],
            "NumPopulationGroups": [
                2,
                1
            ],
            "PopulationGroups": [
                [
                    0,
                    1
                ],
                [
                    0,
                    10,
                    40,
                    120
                ]
            ],
            "ResultUnits": "annual deaths per 1000 individuals",
            "ResultScaleFactor": 2.739726027397e-06,
            "ResultValues": [
                [
                    51.6,
                    3.7,
                    5.3,
                    1000
                ],
                [
                    60.1,
                    4.1,
                    4.8,
                    1000
                ]
            ]
        }
    }
}

SusceptibilityDistribution

JSON object

NA

NA

NA

The structure defining a complex immunity/susceptibility distribution. Susceptibility_Initialization_Distribution_Type in the configuration file must be set to DISTRIBUTION_COMPLEX (see Immunity parameters).

 
{
    "IndividualAttributes": {
        "SusceptibilityDistribution": {
            "AxisNames": [
                "age"
            ],
            "AxisScaleFactors": [
                365
            ],
            "AxisUnits": [
                "years"
            ],
            "NumPopulationGroups": [
                1
            ],
            "PopulationGroups": [
                [
                    0
                ]
            ],
            "ResultScaleFactor": 3.6952,
            "ResultValues": [
                [
                    0
                ]
            ]
        }
    }
}
Configuration parameters

The parameters described in this reference section can be added to the JSON (JavaScript Object Notation) formatted configuration file to determine the core behavior of a simulation including the computing environment, functionality to enable, additional files to use, and characteristics of the disease being modeled. This file contains mostly a flat list of JSON key:value pairs.

For more information on the structure of these files, see Configuration file.

The topics in this section contain only parameters available when using the malaria simulation type. Some parameters may appear in multiple categories.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

Drugs and treatments

The following parameters determine the efficacy of drugs and other treatments.

For more information on the drugs used to treat malaria, see Antimalarial drugs.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Bodyweight_Exponent

float

0

100000

0

The effect of bodyweight on maximum drug concentration in an individual patient. Drug_Cmax is divided by patient bodyweight raised to the power of Bodyweight_Exponent to account for the influence of body size on volume of distribution.

 
{
    "Malaria_Drug_Params": [
        {
        "Name": "Artemether",
        "Bodyweight_Exponent": 2
        }
    ]
}

Drug_Cmax

float

0

100000

1000

The maximum drug concentration used.

Note

This parameter and the Drug_PKPD_C50 parameter must use the same units.

 
{
    "Malaria_Drug_Params": [
        {
            "Name": "Artemether",
            "Drug_Cmax": 1000,
            "Drug_PKPD_C50": 100
        }
    ]
}

Drug_Decay_T1

float

0

100000

1

The primary drug decay rate, in days.

 
{
    "Malaria_Drug_Params": [
        {
            "Name": "Artemether",
            "Drug_Decay_T1": 1,
            "Drug_Decay_T2": 1
        }
    ]
}

Drug_Decay_T2

float

0

100000

1

The secondary drug decay rate, in days.

 
{
    "Malaria_Drug_Params": [
        {
            "Name": "Artemether",
            "Drug_Decay_T1": 1,
            "Drug_Decay_T2": 1
        }
    ]
}

Drug_Dose_Interval

float

0

100000

1

The interval between doses of drugs, in days.

 
{
    "Malaria_Drug_Params": [
        {
            "Name": "Artemether",
            "Drug_Dose_Interval": 1
        }
    ]
}

Drug_Fulltreatment_Doses

float

1

100000

3

The number of doses for a full treatment of a drug.

 
{
    "Malaria_Drug_Params": [
        {
            "Name": "Artemether",
            "Drug_Fulltreatment_Doses": 3
        }
    ]
}

Drug_Gametocyte02_Killrate

float

0

100000

0

The log reduction per day in early-stage gametocytes.

 
{
    "Malaria_Drug_Params": [
        {
            "Name": "Chloroquine",
            "Drug_Gametocyte02_Killrate": 0,
            "Drug_Gametocyte34_Killrate": 0,
            "Drug_GametocyteM_Killrate": 0
        }
    ]
}

Drug_Gametocyte34_Killrate

float

0

100000

0

The log reduction per day in late-stage gametocytes.

 
{
    "Malaria_Drug_Params": [
        {
            "Name": "Chloroquine",
            "Drug_Gametocyte02_Killrate": 0,
            "Drug_Gametocyte34_Killrate": 0,
            "Drug_GametocyteM_Killrate": 0
        }
    ]
}

Drug_GametocyteM_Killrate

float

0

100000

0

The log reduction per day in mature gametocyte numbers at saturated drug concentrations.

 
{
    "Malaria_Drug_Params": [
        {
            "Name": "Chloroquine",
            "Drug_Gametocyte02_Killrate": 0,
            "Drug_Gametocyte34_Killrate": 0,
            "Drug_GametocyteM_Killrate": 0
        }
    ]
}

Drug_Hepatocyte_Killrate

float

0

100000

0

The log reduction in hepatocyte numbers per day.

 
{
    "Malaria_Drug_Params": [
        {
            "Name": "Chloroquine",
            "Drug_Hepatocyte_Killrate": 0
        }
    ]
}

Drug_PKPD_C50

float

0

100000

100

The concentration at which drug killing rates are half of the maximum.

Note

This parameter and the Drug_Cmax parameter must use the same units.

 
{
    "Malaria_Drug_Params": [
        {
            "Name": "Artemether_Lumefantrine",
            "Drug_Cmax": 1000,
            "Drug_PKPD_C50": 100
        }
    ]
}

Drug_Vd

float

0

100000

10

The volume of drug distribution in a pharmacokinetic two compartment model. The first compartment comprises central organs and tissues and the second compartment comprises peripheral tissues. This value is the ratio of the volume of the second compartment to that of the first.

 
{
    "Malaria_Drug_Params": [
        {
            "Name": "Artemether_Lumefantrine",
            "Drug_Vd": 3
        }
    ]
}

Fractional_Dose_By_Upper_Age

array of JSON objects

NA

NA

NA

An array to specify the fraction of the adult dose to use for children at various ages. Contains Upper_Age_In_Years and Fraction_Of_Adult_Dose values.

 
{
    "Malaria_Drug_Params": [
        {
            "Name": "Artemether_Lumefantrine",
            "Fractional_Dose_By_Upper_Age": [
                {
                    "Fraction_Of_Adult_Dose": 0.25,
                    "Upper_Age_In_Years": 3
                },
                {
                    "Fraction_Of_Adult_Dose": 0.5,
                    "Upper_Age_In_Years": 6
                },
                {
                    "Fraction_Of_Adult_Dose": 0.75,
                    "Upper_Age_In_Years": 10
                }
            ]
        }
    ]
}

Fraction_Of_Adult_Dose

float

0

1

NA

The fraction of the adult drug dose given to children below the age defined in Upper_Age_In_Years. Set in the Fractional_Dose_By_Upper_Age array.

 
{
    "Malaria_Drug_Params": [
        {
            "Name": "Artemether_Lumefantrine",
            "Fractional_Dose_By_Upper_Age": [
                {
                    "Fraction_Of_Adult_Dose": 0.25,
                    "Upper_Age_In_Years": 3
                },
                {
                    "Fraction_Of_Adult_Dose": 0.5,
                    "Upper_Age_In_Years": 6
                },
                {
                    "Fraction_Of_Adult_Dose": 0.75,
                    "Upper_Age_In_Years": 10
                }
            ]
        }
    ]
}

Malaria_Drug_Params

array of JSON objects

NA

NA

NA

This is an array of JSON objects where each object in the array contains the parameters for an anti-malarial drug.

 
{
    "Malaria_Drug_Params": [
        {
            "Name": "Artemether_Lumefantrine",
            "PKPD_Model": "FIXED_DURATION_CONSTANT_EFFECT",
            "Bodyweight_Exponent": 0,
            "Drug_Cmax": 1000,
            "Drug_Decay_T1": 1,
            "Drug_Decay_T2": 1,
            "Drug_Dose_Interval": 1,
            "Drug_Fulltreatment_Doses": 3,
            "Drug_Gametocyte02_Killrate": 2.3,
            "Drug_Gametocyte34_Killrate": 2.3,
            "Drug_GametocyteM_Killrate": 0,
            "Drug_Hepatocyte_Killrate": 0,
            "Drug_PKPD_C50": 100,
            "Drug_Vd": 10,
            "Max_Drug_IRBC_Kill": 4.6
        },
        {
            "Name": "Artemisinin",
            "PKPD_Model": "FIXED_DURATION_CONSTANT_EFFECT",
            "Bodyweight_Exponent": 0,
            "Drug_Cmax": 1000,
            "Drug_Decay_T1": 1,
            "Drug_Decay_T2": 1,
            "Drug_Dose_Interval": 1,
            "Drug_Fulltreatment_Doses": 3,
            "Drug_Gametocyte02_Killrate": 2.3,
            "Drug_Gametocyte34_Killrate": 2.3,
            "Drug_GametocyteM_Killrate": 0,
            "Drug_Hepatocyte_Killrate": 0,
            "Drug_PKPD_C50": 100,
            "Drug_Vd": 10,
            "Max_Drug_IRBC_Kill": 4.61
        }
    ]
}

Max_Drug_IRBC_Kill

float

5

100000

5

The maximum log reduction in IRBCs per day due to treatment.

 
{
    "Malaria_Drug_Params": [
        {
            "Name": "Artemether_Lumefantrine",
            "Max_Drug_IRBC_Kill": 4.6
        }
    ]
}

PKPD_Model

enum

NA

NA

FIXED_DURATION_CONSTANT_EFFECT

Determines which pharmacokinetic pharmacodynamic model to use. Possible values are:

FIXED_DURATION_CONSTANT_EFFECT

This is the simplified model of drug action. Each dose of the drug has a constant effect for a fixed duration of time, after which the effect is zero.

CONCENTRATION_VERSUS_TIME

This model of drug action uses continuous pharmacokinetics with two-compartment decay. Effects of each drug depend on the concentration at that point in time.

 
{
    "Malaria_Drug_Params": [
        {
            "Name": "Artemether_Lumefantrine",
            "PKPD_Model": "FIXED_DURATION_CONSTANT_EFFECT"
        }
    ]
}

Upper_Age_In_Years

float

0

125

NA

The age, in years, below which children are given a fraction of the adult drug dose, as defined in Fraction_Of_Adult_Dose. Note that this parameter can be specified for each drug included in the configuration file, and different fractional doses may be used for different age groups.

 
{
    "Malaria_Drug_Params": [
        {
            "Name": "Artemether_Lumefantrine",
            "Fractional_Dose_By_Upper_Age": [
                {
                    "Fraction_Of_Adult_Dose": 0.25,
                    "Upper_Age_In_Years": 3
                },
                {
                    "Fraction_Of_Adult_Dose": 0.5,
                    "Upper_Age_In_Years": 6
                },
                {
                    "Fraction_Of_Adult_Dose": 0.75,
                    "Upper_Age_In_Years": 10
                }
            ]
        }
    ]
}
Enable or disable features

The following parameters enable or disable features of the model, such as allowing births, deaths, or aging. Set to false (0) to disable; set to true (1) to enable.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Enable_Aging

boolean

0

1

1

Controls whether or not individuals in a population age during the simulation. Enable_Vital_Dynamics must be set to true (1).

 
{
    "Enable_Vital_Dynamics": 1,
    "Enable_Aging": 1
}

Enable_Air_Migration

boolean

0

1

0

Controls whether or not migration by air travel will occur. Migration_Model must be set to FIXED_RATE_MIGRATION.

 
{
    "Migration_Model": "FIXED_RATE_MIGRATION",
    "Enable_Air_Migration": 1,
    "Air_Migration_Filename": "../inputs/air_migration.bin"
}

Enable_Birth

boolean

0

1

1

Controls whether or not individuals will be added to the simulation by birth. Enable_Vital_Dynamics must be set to true (1). If you want new individuals to have the same intervention coverage as existing individuals, you must add a BirthTriggeredIV to the campaign file.

 
{
    "Enable_Vital_Dynamics": 1,
    "Enable_Birth": 1
}

Enable_Climate_Stochasticity

boolean

0

1

0

Controls whether or not the climate has stochasticity. Climate_Model must be set to CLIMATE_CONSTANT or CLIMATE_BY_DATA. Set the variance using the parameters Air_Temperature_Variance, Land_Temperature_Variance, Enable_Rainfall_Stochasticity, and Relative_Humidity_Variance.

 
{
    "Climate_Model": "CLIMATE_BY_DATA",
    "Enable_Climate_Stochasticity": 1,
    "Air_Temperature_Variance": 2,
    "Enable_Rainfall_Stochasticity": 1,
    "Land_Temperature_Variance": 2,
    "Relative_Humidity_Variance": 0.05
}

Enable_Default_Reporting

boolean

0

1

1

Controls whether or not the default InsetChart.json report is created.

 
{
    "Enable_Default_Reporting": 1
}

Enable_Demographics_Birth

boolean

0

1

0

Controls whether or not newborns have disease risk drawn from a distribution; uniform disease risk if false. Enable_Birth, Enable_Demographics_Risk, and Enable_Vital_Dynamics must be set to true (1).

 
{
    "Enable_Birth": 1,
    "Enable_Demographics_Birth": 1,
    "Enable_Vital_Dynamics": 1
}

Enable_Demographics_Builtin

boolean

0

1

0

Controls whether or not built-in demographics for default geography will be used. Note that the built-in demographics feature does not represent a real geographical location and is mostly used for testing. Set to true (1) to define the initial population and number of nodes using Default_Geography_Initial_Node_Population and Default_Geography_Torus_Size. Set to false (0) to use demographics input files defined in Demographics_Filenames.

 
{
    "Enable_Demographics_Builtin": 1,
    "Default_Geography_Initial_Node_Population": 1000,
    "Default_Geography_Torus_Size": 3
}

Enable_Demographics_Reporting

boolean

0

1

1

Controls whether or not demographic summary data and age-binned reports are outputted to file.

 
{
    "Enable_Demographics_Reporting": 1
}

Enable_Demographics_Risk

boolean

0

1

0

Controls whether or not the simulation includes the impact of disease risk in demographics.

 
{
    "Enable_Demographics_Risk": 1
}

Enable_Disease_Mortality

boolean

0

1

1

Controls whether or not individuals are removed from the simulation due to disease deaths.

 
{
    "Enable_Disease_Mortality": 1
}

Enable_Egg_Mortality

boolean

0

1

0

Controls whether or not to include a daily mortality rate on the egg population, which is independent of climatic factors.

 
{
    "Enable_Egg_Mortality": 1
}

Enable_Family_Migration

boolean

0

1

0

Controls whether or not all members of a household can migrate together when a MigrateFamily event occurs. All residents must be home before they can leave on the trip. Migration_Model must be set to FIXED_RATE_MIGRATION.

 
{
    "Enable_Migration": "FIXED_RATE_MIGRATION",
    "Enable_Family_Migration": 1,
    "Family_Migration_Filename": "../inputs/family_migration.bin"
}

Enable_Heterogeneous_Intranode_Transmission

boolean

0

1

0

Controls whether or not individuals experience heterogeneous disease transmission within a node. When set to true (1), individual property definitions and the \(\beta\) matrix must be specified in the demographics file (see NodeProperties and IndividualProperties parameters). The \(\beta\) values are multiplied with the \(\beta\) 0 value configured by Base_Infectivity.

This is used only in generic, environmental, and typhoid simulations, but must be set to false (0) for all other simulation types. Heterogeneous transmission for other diseases uses other mechanistic parameters included with the simulation type.

 
{
    "Enable_Heterogeneous_Intranode_Transmission": 1
}

Enable_Immunity

boolean

0

1

1

Controls whether or not an individual has protective immunity after an infection clears.

 
{
    "Enable_Immunity": 1
}

Enable_Infectivity_Reservoir

boolean

0

1

0

Controls whether or not an exogeneous reservoir of infectivity will be included in the simulation and allows for the infectivity in a node to be increased additively. When set to 1 (true), the demographics parameter InfectivityReservoirSize is expected in NodeAtttributes for each node.

Warning

Do not set both Enable_Infectivity_Reservoir and Enable_Strain_Tracking to true (1) - as this combination will cause an exception error.

 
{
    "Enable_Infectivity_Reservoir": 1
}

Enable_Initial_Prevalence

boolean

0

1

0

Controls whether or not parameters in the demographics file are used to define a distribution for the number of infected people per node at the beginning of the simulation. Set the distribution under NodeAttributes using PrevalenceDistributionFlag, PrevalenceDistribution1, and PrevalenceDistribution2.

 
{
    "Enable_Initial_Prevalence": 1
}

Enable_Initial_Susceptibility_Distribution

boolean

0

1

0

Controls whether or not individuals in the population have immunity at the beginning of the simulation. If set to 0, individuals are not initialized with immunity but may acquire immunity. If set to 1, you must indicate the type of distribution to use for immunity in the configuration parameter Immunity_Initialization_Distribution_Type and the distribution values in the demographics file. Enable_Immunity must be set to 1.

 
{
    "Enable_Immunity": 1,
    "Enable_Initial_Susceptibility_Distribution": 1,
    "Immunity_Initialization_Distribution_Type": "DISTRIBUTION_SIMPLE"
}

Enable_Interventions

boolean

0

1

0

Controls whether or not campaign interventions will be used in the simulation. Set Campaign_Filename to the path of the file that contains the campaign interventions.

 
{
    "Enable_Interventions": 1,
    "Campaign_Filename": "campaign.json"
}

Enable_Local_Migration

boolean

0

1

0

Controls whether or not local migration (the diffusion of people in and out of nearby nodes by foot travel) occurs. Migration_Model must be set to FIXED_RATE_MIGRATION.

 
{
    "Migration_Model": "FIXED_RATE_MIGRATION",
    "Enable_Local_Migration": 1,
    "Local_Migration_Filename": "../inputs/local_migration.bin"
}

Enable_Maternal_Protection

boolean

0

1

0

Controls whether or not mothers pass immunity on to their infants. Setting to 1 (true) enables maternal protection as defined by Maternal_Protection_Type. Enable_Birth must be set to 1 (true).

 
{
    "Enable_Maternal_Protection": 1,
    "Maternal_Protection_Type": "LINEAR_FRACTIONAL"
}

Enable_Migration_Heterogeneity

boolean

0

1

1

Controls whether or not migration rate is heterogeneous among individuals within each group that has a migration rate setting. Set to true (1) to apply a migration rate distribution (see NodeAttributes demographics parameters); set to false (0) to use the same migration rate applied to all individuals in the group. For example, if you are using a migration file that sets different migration rates for each age group in a node, you could apply an Gaussian distribution around a mean value in each age group or you could assign the same value to each individual in each age group.

Migration_Model must be set to FIXED_RATE_MIGRATION.

 
{
    "Migration_Model": "FIXED_RATE_MIGRATION",
    "Enable_Migration_Heterogeneity": 1
}

Enable_Natural_Mortality

boolean

0

1

0

Controls whether or not individuals are removed from the simulation due to natural (non-disease) deaths. Enable_Vital_Dynamics must be set to 1 (true). Use Death_Rate_Dependence to set the natural death rate.

 
{
    "Enable_Natural_Mortality": 1,
    "Enable_Vital_Dynamics": 1
}

Enable_Property_Output

boolean

0

1

0

Controls whether or not to create property output reports, which detail groups as defined in IndividualProperties in the demographics file (see NodeProperties and IndividualProperties parameters). When there is more than one property type, the report will display the channel information for all combinations of the property type groups.

 
{
    "Enable_Property_Output": 1
}

Enable_Rainfall_Stochasticity

boolean

0

1

1

Controls whether or not there is stochastic variation in rainfall; set to true (1) for stochastic variation of rainfall that is drawn from an exponential distribution (with a mean value as the daily rainfall from the Climate_Model values CLIMATE_CONSTANT or CLIMATE_BY_DATA), or set to false (0) to disable rainfall stochasticity.

 
{
    "Enable_Climate_Stochasticity": 1,
    "Air_Temperature_Variance": 2,
    "Enable_Rainfall_Stochasticity": 1,
    "Land_Temperature_Variance": 2,
    "Relative_Humidity_Variance": 0.05
}

Enable_Regional_Migration

boolean

0

1

0

Controls whether or not there is migration by road or rail network into and out of nodes in the simulation. Migration_Model must be set to FIXED_RATE_MIGRATION.

 
{
    "Migration_Model": "FIXED_RATE_MIGRATION",
    "Enable_Regional_Migration": 1,
    "Regional_Migration_Filename": "../inputs/regional_migration.bin"
}

Enable_Sea_Migration

boolean

0

1

0

Controls whether or not there is migration on ships into and out of coastal cities with seaports. Migration_Model must be set to FIXED_RATE_MIGRATION.

 
{
    "Migration_Model": "FIXED_RATE_MIGRATION",
    "Enable_Sea_Migration": 1,
    "Sea_Migration_Filename": "../inputs/sea_migration.bin"
}

Enable_Spatial_Output

boolean

0

1

0

Controls whether or not spatial output reports are created. If set to true (1), spatial output reports include all channels listed in the parameter Spatial_Output_Channels.

Note

Spatial output files require significant processing time and disk space.

 
{
    "Enable_Spatial_Output": 1,
    "Spatial_Output_Channels": [
        "Prevalence",
        "New_Infections"
    ]
}

Enable_Strain_Tracking

boolean

0

1

0

Enable an association of an infection with a specified strain, defined by unique integers for a clade and a genome.

For infections distributed through interventions, you specify clade and genome values using the Clade and Genome parameters with the Outbreak and OutbreakIndividual intervention classes in the campaign file.

When using an initial prevalence distribution, you specify clade and genome values using the InitialPrevalenceStrains parameter as part of IndividualAttributes in the demographics file.

When Enable_Strain_Tracking is set to false (0) or absent, all infections are associated with a single strain that has clade and genome identity (0, 0).

When Enable_Strain_Tracking is set to true (1) then both Number_of_Clades and Log2_Number_of_Genomes_per_Clade parameters must be specified.

Warning

Do not set both Enable_Strain_Tracking and Enable_Infectivity_Reservoir to true (1) or Enable_Strain_Tracking and Enable_Maternal_Infection_Transmission to true (1) - as these combinations will cause an exception error.

 
{
    "Enable_Strain_Tracking": 1,
    "Log2_Number_of_Genomes_per_Clade": 8,
    "Number_of_Clades": 2
}

Enable_Superinfection

boolean

0

1

0

Controls whether or not an individual can have multiple infections simultaneously. Set to true (1) to allow for multiple simultaneous infections; set to false (0) if multiple infections are not possible. Set the Max_Individual_Infections parameter.

 
{
    "Enable_Superinfection": 1,
    "Max_Individual_Infections": 2
}

Enable_Vector_Aging

boolean

0

1

0

Controls whether or not vectors senesce as they get older. When enabled, the daily mortality rate (1/Adult_Life_Expectancy) is modified by environmental conditions such as dry heat, feeding on humans, etc. With vector aging enabled, Adult_Life_Expectancy equal to 20 days corresponds to a mean adult female mosquito lifespan of 9-9.5 days in calibrated settings. The senescence formula that EMOD uses was described by Styer, et al <https://www.ncbi.nlm.nih.gov/pubmed/17255238>.

 
{
    "Enable_Vector_Aging": 1,
    "Adult_Life_Expectancy": 20
}

Enable_Vector_Migration

boolean

0

1

0

Controls whether or not vectors can migrate. Vector_Sampling_Type must be set to TRACK_ALL_VECTORS or SAMPLE_IND_VECTORS. Specific migration types must be enabled or disabled.

 
{
    "Vector_Sampling_Type": "TRACK_ALL_VECTORS",
    "Enable_Vector_Migration": 1,
    "Enable_Vector_Migration_Local": 1,
    "Vector_Migration_Filename_Local": "../inputs/vector_local.bin"
}

Enable_Vector_Migration_Local

boolean

0

1

0

Controls whether or not vectors may migrate to adjacent nodes. Enable_Vector_Migration must be set to true (1).

 
{
    "Vector_Sampling_Type": "TRACK_ALL_VECTORS",
    "Enable_Vector_Migration": 1,
    "Enable_Vector_Migration_Local": 1,
    "Vector_Migration_Filename_Local": "../inputs/vector_local.bin"
}

Enable_Vector_Migration_Regional

boolean

0

1

0

Controls whether or not vectors can migration to non-adjacent nodes. Enable_Vector_Migration must be set to true (1).

 
{
    "Vector_Sampling_Type": "TRACK_ALL_VECTORS",
    "Enable_Vector_Migration": 1,
    "Enable_Vector_Migration_Regional": 1,
    "Vector_Migration_Filename_Regional": "../inputs/vector_regional.bin"
}

Enable_Vector_Mortality

boolean

0

1

1

Controls whether or not vectors can die. Vector_Sampling_Type must be set to TRACK_ALL_VECTORS.

 
{
    "Vector_Sampling_Type": "TRACK_ALL_VECTORS",
    "Enable_Vector_Mortality": 1
}

Enable_Vital_Dynamics

boolean

0

1

1

Controls whether or not births and deaths occur in the simulation. Births and deaths must be individually enabled and set.

 
{
    "Enable_Vital_Dynamics": 1,
    "Enable_Birth": 1,
    "Death_Rate_Dependence": "NOT_INITIALIZED",
    "Base_Mortality": 0.002
}
General disease

The following parameters determine general disease characteristics.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Enable_Strain_Tracking

boolean

0

1

0

Enable an association of an infection with a specified strain, defined by unique integers for a clade and a genome.

For infections distributed through interventions, you specify clade and genome values using the Clade and Genome parameters with the Outbreak and OutbreakIndividual intervention classes in the campaign file.

When using an initial prevalence distribution, you specify clade and genome values using the InitialPrevalenceStrains parameter as part of IndividualAttributes in the demographics file.

When Enable_Strain_Tracking is set to false (0) or absent, all infections are associated with a single strain that has clade and genome identity (0, 0).

When Enable_Strain_Tracking is set to true (1) then both Number_of_Clades and Log2_Number_of_Genomes_per_Clade parameters must be specified.

Warning

Do not set both Enable_Strain_Tracking and Enable_Infectivity_Reservoir to true (1) or Enable_Strain_Tracking and Enable_Maternal_Infection_Transmission to true (1) - as these combinations will cause an exception error.

 
{
    "Enable_Strain_Tracking": 1,
    "Log2_Number_of_Genomes_per_Clade": 8,
    "Number_of_Clades": 2
}

Log2_Number_of_Genomes_per_Clade

integer

0

24

0

The maximum number of genome combinations per clades. Used with Number_of_Clades and dependent upon Enable_Strain_Tracking enabled, set to 1.

Note

Log2_Number_of_Genomes_per_Clade is internally fixed to 0 when using Typhoid and Malaria simulations.

 
{
    "Enable_Strain_Tracking": 1,
    "Log2_Number_of_Genomes_per_Clade": 8,
    "Number_of_Clades": 2
}

Number_of_Clades

integer

1

10

1

The maximum number of distinct clades in the simulation. Used with Log2_Number_of_Genomes_per_Clade to define the strain associated with an infection. Dependent upon Enable_Strain_Tracking enabled, set to 1.

 
{
    "Enable_Strain_Tracking": 1,
    "Log2_Number_of_Genomes_per_Clade": 8,
    "Number_of_Clades": 2
}
Geography and environment

The following parameters determine characteristics of the geography and environment of the simulation. For example, how to use the temperature or rainfall data in the climate files and the size of the nodes in the simulation.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Air_Temperature_Filename

string

NA

NA

_add-your-air-temperature-file_.bin

The path to the input file that defines air temperature data measured two meters above ground. Climate_Model must be set to CLIMATE_BY_DATA. The file must be in .bin format.

 
{
    "Climate_Model": "CLIMATE_BY_DATA",
    "Air_Temperature_Filename": "Namawala_single_node_air_temperature_daily.bin"
}

Air_Temperature_Offset

float

-20

20

0

The linear shift of air temperature in degrees Celsius. Climate_Model must be set to CLIMATE_BY_DATA.

 
{
    "Air_Temperature_Offset": 1
}

Air_Temperature_Variance

float

0

-16

2

The standard deviation (in degrees Celsius) for normally distributed noise applied to the daily air temperature values when Climate_Model is configured as CLIMATE_CONSTANT or CLIMATE_BY_DATA. Enable_Climate_Stochasticity must be set to true (1).

 
{
    "Enable_Climate_Stochasticity": 1,
    "Air_Temperature_Variance": 2
}

Base_Air_Temperature

float

-55

45

22

The air temperature, in degrees Celsius, where Climate_Model is set to CLIMATE_CONSTANT.

 
{
    "Climate_Model": "CLIMATE_CONSTANT",
    "Base_Air_Temperature": 30
}

Base_Land_Temperature

float

-55

60

26

The land temperature, in degrees Celsius, where Climate_Model is set to CLIMATE_CONSTANT.

 
{
    "Climate_Model": "CLIMATE_CONSTANT",
    "Base_Land_Temperature": 20
}

Base_Rainfall

float

0

150

10

The value of rainfall per day in millimeters when Climate_Model is set to CLIMATE_CONSTANT.

 
{
    "Climate_Model": "CLIMATE_CONSTANT",
    "Base_Rainfall": 20
}

Base_Relative_Humidity

float

0

1

0.75

The value of humidity where Climate_Model is set to CLIMATE_CONSTANT.

 
{
    "Base_Relative_Humidity": 0.1
}

Climate_Model

enum

NA

NA

CLIMATE_OFF

How and from what files the climate of a simulation is configured. The possible values are:

CLIMATE_OFF

No climate files used.

CLIMATE_CONSTANT

Uses the conditional parameters that give the fixed values of temperature or rain for land temperature, air temperature, rainfall, and humidity.

CLIMATE_KOPPEN

Uses an input file that decodes Koppen codes by geographic location.

CLIMATE_BY_DATA

Reads everything out of several input files with additional parameters that allow the addition of stochasticity or scale offsets.

 
{
    "Climate_Model": "CLIMATE_CONSTANT"
}

Climate_Update_Resolution

enum

NA

NA

CLIMATE_UPDATE_YEAR

The resolution of data in climate files. Climate_Model must be set to CLIMATE_CONSTANT, CLIMATE_BY_DATA, or CLIMATE_KOPPEN. Possible values are:

CLIMATE_UPDATE_YEAR CLIMATE_UPDATE_MONTH CLIMATE_UPDATE_WEEK CLIMATE_UPDATE_DAY CLIMATE_UPDATE_HOUR

 
{
    "Climate_Update_Resolution": "CLIMATE_UPDATE_DAY"
}

Default_Geography_Initial_Node_Population

integer

0

1000000

1000

When using the built-in demographics for default geography, the initial number of individuals in each node. Note that the built-in demographics feature does not represent a real geographical location and is mostly used for testing. Enable_Demographics_Builtin must be set to true (1).

 
{
    "Enable_Demographics_Builtin": 1,
    "Default_Geography_Initial_Node_Population": 1000,
    "Default_Geography_Torus_Size": 3
}

Default_Geography_Torus_Size

integer

3

100

10

When using the built-in demographics for default geography, the square root of the number of nodes in the simulation. The simulation uses an N x N square grid of nodes with N specified by this parameter. If migration is enabled, the N x N nodes are assumed to be a torus and individuals can migrate from any node to all four adjacent nodes.

To enable migration, set Migration_Model to FIXED_RATE_MIGRATION. Built-in migration is a form of “local” migration where individuals only migrate to the adjacent nodes. You can use the x_Local_Migration parameter to control the rate of migration. The other migration parameters are ignored. Note that the built-in demographics feature does not represent a real geographical location and is mostly used for testing.

Enable_Demographics_Builtin must be set to true (1).

 
{
    "Enable_Demographics_Builtin": 1,
    "Default_Geography_Initial_Node_Population": 1000,
    "Default_Geography_Torus_Size": 3
}

Enable_Climate_Stochasticity

boolean

0

1

0

Controls whether or not the climate has stochasticity. Climate_Model must be set to CLIMATE_CONSTANT or CLIMATE_BY_DATA. Set the variance using the parameters Air_Temperature_Variance, Land_Temperature_Variance, Enable_Rainfall_Stochasticity, and Relative_Humidity_Variance.

 
{
    "Climate_Model": "CLIMATE_BY_DATA",
    "Enable_Climate_Stochasticity": 1,
    "Air_Temperature_Variance": 2,
    "Enable_Rainfall_Stochasticity": 1,
    "Land_Temperature_Variance": 2,
    "Relative_Humidity_Variance": 0.05
}

Enable_Rainfall_Stochasticity

boolean

0

1

1

Controls whether or not there is stochastic variation in rainfall; set to true (1) for stochastic variation of rainfall that is drawn from an exponential distribution (with a mean value as the daily rainfall from the Climate_Model values CLIMATE_CONSTANT or CLIMATE_BY_DATA), or set to false (0) to disable rainfall stochasticity.

 
{
    "Enable_Climate_Stochasticity": 1,
    "Air_Temperature_Variance": 2,
    "Enable_Rainfall_Stochasticity": 1,
    "Land_Temperature_Variance": 2,
    "Relative_Humidity_Variance": 0.05
}

Koppen_Filename

string

NA

NA

_add-your-koppen-climate-file_.json

The path to the input file used to specify Koppen climate classifications. Climate_Model must be set to CLIMATE_KOPPEN.

 
{
    "Koppen_Filename": "Mad_2_5arcminute_koppen.dat"
}

Land_Temperature_Filename

string

NA

NA

_add-your-land-temp-file_.bin

The path of the input file defining temperature data measured at land surface; used only when Climate_Model is set to CLIMATE_BY_DATA. The file must be in .bin format.

 
{
    "Land_Temperature_Filename": "Namawala_single_node_land_temperature_daily.bin"
}

Land_Temperature_Offset

float

-20

20

0

The linear shift of land surface temperature in degrees Celsius; only used when Climate_Model is set to CLIMATE_BY_DATA.

 
{
    "Land_Temperature_Offset": 0
}

Land_Temperature_Variance

float

0

7

2

The standard deviation (in degrees Celsius) for normally distributed noise applied to the daily land temperature values when Climate_Model is configured to CLIMATE_CONSTANT or CLIMATE_BY_DATA; only used if the Enable_Climate_Stochasticity is set to true (1).

 
{
    "Land_Temperature_Variance": 1.5
}

Node_Grid_Size

float

0.00416

90

0.004167

The spatial resolution indicating the node grid size for a simulation in degrees.

 
{
    "Node_Grid_Size": 0.042
}

Rainfall_Filename

string

NA

NA

_add-your-rainfall-file_.bin

The path of the input file which defines rainfall data. Climate_Model must be set to CLIMATE_BY_DATA. The file must be in .bin format.

 
{
    "Rainfall_Filename": "Namawala_single_node_rainfall_daily.bin"
}

Rainfall_In_mm_To_Fill_Swamp

float

1

10000

1000

Millimeters of rain to fill larval habitat to capacity. This is only used for vector species with Habitats set to BRACKISH_SWAMP.

 
{
    "Rainfall_In_mm_To_Fill_Swamp": 1000.0
}

Rainfall_Scale_Factor

float

0.1

10

1

The scale factor used in multiplying rainfall value(s). Climate_Model must be set to CLIMATE_BY_DATA.

 
{
    "Rainfall_Scale_Factor": 1
}

Relative_Humidity_Filename

string

NA

NA

_add-your-relative-humidity-file__hum.bin

The path of the input file which defines relative humidity data measured 2 meters above ground. Climate_Model must be set to CLIMATE_BY_DATA. The file must be in .bin format.

 
{
    "Relative_Humidity_Filename": "Namawala_single_node_relative_humidity_daily.bin"
}

Relative_Humidity_Scale_Factor

float

0.1

10

1

The scale factor used in multiplying relative humidity values. Climate_Model must be set to CLIMATE_BY_DATA.

 
{
    "Relative_Humidity_Scale_Factor": 1
}

Relative_Humidity_Variance

float

0

0.12

0.05

The standard deviation (in percentage) for normally distributed noise applied to the daily relative humidity values when Climate_Model is configured as CLIMATE_CONSTANT or CLIMATE_BY_DATA. Enable_Climate_Stochasticity must be set to true (1).

 
{
    "Relative_Humidity_Variance": 0.05
}
Immunity

The following parameters determine the immune system response for the disease being modeled, including waning immunity after an infection clears.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Acquisition_Blocking_Immunity_Decay_Rate

float

0

1

0.001

The rate at which acquisition-blocking immunity decays after the initial period indicated by the base acquisition-blocking immunity offset. Only used when Enable_Immunity and Enable_Immune_Decay parameters are set to true (1).

 
{
    "Acquisition_Blocking_Immunity_Decay_Rate": 0.05
}

Acquisition_Blocking_Immunity_Duration_Before_Decay

float

0

45000

0

The number of days after infection until acquisition-blocking immunity begins to decay. Enable_Immunity and Enable_Immune_Decay must be set to true (1).

 
{
    "Acquisition_Blocking_Immunity_Duration_Before_Decay": 10
}

Antibody_Capacity_Growth_Rate

float

0

1

0.1

The maximum daily rate of specific antibody capacity increase. Antibody production begins at a capacity of 0.3., and hyperimmunity results at 0.4. The actual growth rate tends to be lower and is a function of antigen concentrations.

 
{
    "Antibody_Capacity_Growth_Rate": 0.09
}

Antibody_CSP_Decay_Days

float

1

3.40E+38

90

Exponential decay time for the circumsporozoite protein (CSP) antibody concentration (in days) when boosted above natural maximum concentrations.

 
{
    "Antibody_CSP_Decay_Days": 80
}

Antibody_CSP_Killing_Inverse_Width

float

1.00E-0

1000000

1.5

The inverse width of the sigmoidal sporozoite killing function of circumsporozoite protein (CSP) antibody concentration.

 
{
    "Antibody_CSP_Killing_Inverse_Width": 1.7
}

Antibody_CSP_Killing_Threshold

float

1.00E-0

1000000

10

The threshold value on circumsporozoite protein (CSP) antibody concentration for sporozoite killing.

 
{
    "Antibody_CSP_Killing_Threshold": 20
}

Antibody_IRBC_Kill_Rate

double

NA

NA

2

The scale factor multiplied by antibody level to produce the rate of clearance of the infected red blood cell (IRBC) population.

 
{
    "Antibody_IRBC_Kill_Rate": 1.595
}

Antibody_Memory_Level

float

0

0.35

0.2

The limiting level of antibody capacity that remains after a prolonged absence of a specific antigen. The antibody capacity decays to this level gradually after infection is cleared. The decay rate in antibody capacity is set so that hyperimmunity is lost within 4 months, and capacity continues to decay to this level. The antibody memory level is relevant for year-scale dynamics, but not for long-term dynamics (10-20 years).

 
{
    "Antibody_Memory_Level": 0.3
}

Antibody_Stimulation_C50

float

0.1

10000

10

The concentration of an antigen, measured in IRBC/ul at which growth in antibody capacity against the antigen increases at half the maximum rate specified by Antibody_Capacity_Growth_Rate.

 
{
    "Antibody_Stimulation_C50": 30,
    "Antibody_Capacity_Growth_Rate": 0.09
}

Antigen_Switch_Rate

double

NA

NA

2.00E-09

The antigenic switching rate per infected red blood cell per asexual cycle. See Parasite_Switch_Type for different switching patterns.

 
{
    "Antigen_Switch_Rate": 5e-09
}

Cytokine_Gametocyte_Inactivation

float

0

1

0.02

The strength of inflammatory response inactivation of gametocytes.

 
{
    "Cytokine_Gametocyte_Inactivation": 0.0167
}

Enable_Immunity

boolean

0

1

1

Controls whether or not an individual has protective immunity after an infection clears.

 
{
    "Enable_Immunity": 1
}

Enable_Initial_Susceptibility_Distribution

boolean

0

1

0

Controls whether or not individuals in the population have immunity at the beginning of the simulation. If set to 0, individuals are not initialized with immunity but may acquire immunity. If set to 1, you must indicate the type of distribution to use for immunity in the configuration parameter Immunity_Initialization_Distribution_Type and the distribution values in the demographics file. Enable_Immunity must be set to 1.

 
{
    "Enable_Immunity": 1,
    "Enable_Initial_Susceptibility_Distribution": 1,
    "Immunity_Initialization_Distribution_Type": "DISTRIBUTION_SIMPLE"
}

Enable_Maternal_Antibodies_Transmission

boolean

0

1

0

Controls whether or not mothers pass antibodies to their infants. When enabled, you must set Maternal_Antibodies_Type,**Maternal_Antibody_Protection**, and Maternal_Antibody_Decay_Rate. Enable_Birth must be set to 1.

 
{
    "Enable_Maternal_Antibodies_Transmission": 1,
    "Maternal_Antibodies_Type": "SIMPLE_WANING",
    "Maternal_Antibody_Protection": 0.23,
    "Maternal_Antibody_Decay_Rate": 0.03
}

Enable_Maternal_Protection

boolean

0

1

0

Controls whether or not mothers pass immunity on to their infants. Setting to 1 (true) enables maternal protection as defined by Maternal_Protection_Type. Enable_Birth must be set to 1 (true).

 
{
    "Enable_Maternal_Protection": 1,
    "Maternal_Protection_Type": "LINEAR_FRACTIONAL"
}

Erythropoiesis_Anemia_Effect

float

0

1000

3.5

The exponential rate of increased red-blood-cell production from reduced red-blood-cell availability.

 
{
    "Erythropoiesis_Anemia_Effect": 3
}

Falciparum_MSP_Variants

integer

0

1000

100

The number of distinct merozoite surface protein (MSP) variants for P. falciparum malaria in the overall parasite population in the simulation, not necessarily in an individual.

 
{
    "Falciparum_MSP_Variants": 4
}

Falciparum_Nonspecific_Types

integer

0

1000

20

The number of distinct non-specific types of P. falciparum malaria.

 
{
    "Falciparum_Nonspecific_Types": 92
}

Falciparum_PfEMP1_Variants

integer

0

100000

1000

The number of distinct Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) variants for P. falciparum malaria in the overall parasite population in the simulation.

 
{
    "Falciparum_PfEMP1_Variants": 300
}

Fever_IRBC_Kill_Rate

float

0

1000

0.15

The maximum kill rate for infected red blood cells due to the inflammatory innate immune response. As fever increases above 38.5 degrees Celsius, the kill rate becomes successively higher along a sigmoidal curve approaching this rate.

 
{
    "Fever_IRBC_Kill_Rate": 1.4
}

Immune_Threshold_For_Downsampling

float

0

1

0

The threshold on acquisition immunity at which to apply immunity-dependent downsampling; individuals who are more immune than this this threshold are downsampled. A value of 1 is equivalent to full susceptibility and 0 is equivalent to full immunity. If the acquisition immunity modifier is larger than the threshold, no downsampling occurs. Individual_Sampling_Type must set to ADAPTED_SAMPLING_BY_IMMUNE_STATE.

 
{
    "Relative_Sample_Rate_Immune": 0.1,
    "Immune_Threshold_For_Downsampling": 0.5,
    "Individual_Sampling_Type": "ADAPTED_SAMPLING_BY_IMMUNE_STATE"
}

Innate_Immune_Variation_Type

enum

NA

NA

NONE

The type of innate immunity on which to apply individual-level variation. Possible values are:

NONE

No variation in innate immunity between individuals.

PYROGENIC_THRESHOLD

Applies individual variation using the susceptibility distribution values set in SusceptibilityDistributionFlag, SusceptibilityDistribution1, and SusceptibilityDistribution2 in the demographics file to the average threshold for infected red blood cells resulting in fever, as set in Pyrogenic_Threshold.

PYROGENIC_THRESHOLD_VS_AGE

Ignores the demographic file settings and applies an age-dependent threshold for infected red blood cells resulting in fever.

CYTOKINE_KILLING

Applies individual variation using the susceptibility distribution values set in SusceptibilityDistributionFlag, SusceptibilityDistribution1, and SusceptibilityDistribution2 in the demographics file to the effectiveness of cytokines in killing infected red blood cells, as set in Fever_IRBC_Kill_Rate.

 
{
    "Innate_Immune_Variation_Type": "PYROGENIC_THRESHOLD"
}

Maternal_Antibodies_Type

enum

NA

NA

OFF

The type of maternal antibody protection. Enable_Maternal_Antibodies_Transmission must be set to 1 (true). Possible values are:

OFF

No maternal antibody protection provided.

SIMPLE_WANING

The protection from maternal antibodies is a function of the configured maximum fraction protection (Maternal_Antibody_Protection) and the age of the child (through Maternal_Antibody_Decay_Rate), but also of the transmission-intensity in the simulation. In particular, the protection is modified by a factor corresponding to the fraction of PfEMP1 antigenic variants to which possible mothers (14 to 45-year-old females) have been exposed in their lifetimes.

CONSTANT_INITIAL_IMMUNITY

Protection is independent of acquired immunity in possible mothers.

 
{
    "Maternal_Antibodies_Type": "CONSTANT_INITIAL_IMMUNITY"
}

Maternal_Antibody_Decay_Rate

float

0

3.40E+38

0.01

The decay rate per day in protection from maternal antibodies. Maternal_Antibodies_Type must be set to SIMPLE_WANING or CONSTANT_INITIAL_IMMUNITY.

 
{
    "Maternal_Antibodies_Type": "SIMPLE_WANING",
    "Maternal_Antibody_Decay_Rate": 0.01,
    "Maternal_Antibody_Protection": 0.1327
}

Maternal_Antibody_Protection

float

0

1

0.1

The strength of protection from maternal antibodies as a multiple of full antibody killing effect. Maternal_Antibodies_Type must be set to SIMPLE_WANING or CONSTANT_INITIAL_IMMUNITY.

 
{
    "Maternal_Antibodies_Type": "SIMPLE_WANING",
    "Maternal_Antibody_Decay_Rate": 0.01,
    "Maternal_Antibody_Protection": 0.1327
}

Maternal_Linear_Slope

float

0.0001

1

0.01

Slope parameter describing the rate of waning for maternal protection, must be positive. The per-day increase in susceptibility. Maternal_Protection_Type must be set to LINEAR_FRACTIONAL or LINEAR_BINARY.

 
{
    "Maternal_Protection_Type": "LINEAR_FRACTIONAL",
    "Maternal_Linear_SusZero": 0.45,
    "Maternal_Linear_Slope": 0.02
}

Maternal_Linear_SusZero

float

0

1

0.2

The initial level of maternal protection at age 0, given as susceptibility. A value of 0.0 implies total protection, a value of 1.0 implies no protection. Maternal_Protection_Type must be set to LINEAR_FRACTIONAL or LINEAR_BINARY.

 
{
    "Maternal_Protection_Type": "LINEAR_FRACTIONAL",
    "Maternal_Linear_SusZero": 0.45,
    "Maternal_Linear_Slope": 0.02
}

Maternal_Protection_Type

enum

NA

NA

NONE

The type of maternal protection afforded to infants. Enable_Maternal_Protection must be set to 1 (true). Possible values are:

NONE

No immune protection is passed on to infants.

LINEAR

Susceptibility is a function of age and governed by a linear equation. Susceptibility = Maternal_Linear_Slope * age + Maternal_Linear_SusZero

SIGMOID

Susceptibility is a function of age and governed by a sigmoidal equation. Susceptibility = Maternal_Sigmoid_SusInit + (1.0 - Maternal_Sigmoid_SusInit) / * (1.0 + EXP(( Maternal_Sigmoid_HalfMaxAge - age) / Maternal_Sigmoid_SteepFac))

You must set Susceptibility_Type to determine if susceptibility at a particular age is interpreted as a fractional value or the probability of complete immunity or susceptibility.

 
{
    "Enable_Maternal_Protection": 1,
    "Maternal_Protection_Type": "LINEAR_FRACTIONAL"
}

Maternal_Sigmoid_HalfMaxAge

float

-270

3650

180

The age in days that the level of maternal protection is half of its initial value. Maternal_Protection_Type must be set to SIGMOID_FRACTIONAL or SIGMOID_BINARY.

 
{
    "Maternal_Protection_Type": "SIGMOID_BINARY",
    "Maternal_Sigmoid_SteepFac": 30,
    "Maternal_Sigmoid_HalfMaxAge": 365,
    "Maternal_Sigmoid_SusInit": 0.0002
}

Maternal_Sigmoid_SteepFac

float

0.1

1000

30

The steepness factor describing the rate of waning for maternal protection, must be positive. Small values imply rapid waning.**Maternal_Protection_Type** must be set to SIGMOID_FRACTIONAL or SIGMOID_BINARY.

 
{
    "Maternal_Protection_Type": "SIGMOID_BINARY",
    "Maternal_Sigmoid_SteepFac": 30,
    "Maternal_Sigmoid_HalfMaxAge": 365,
    "Maternal_Sigmoid_SusInit": 0.0002
}

Maternal_Sigmoid_SusInit

float

0

1

0

The initial level of maternal protection at age -INF, given as susceptibility. A value of 0.0 implies total protection, a value of 1.0 implies no protection. Maternal_Protection_Type must be set to SIGMOID_FRACTIONAL or SIGMOID_BINARY.

 
{
    "Maternal_Protection_Type": "SIGMOID_BINARY",
    "Maternal_Sigmoid_SteepFac": 30,
    "Maternal_Sigmoid_HalfMaxAge": 365,
    "Maternal_Sigmoid_SusInit": 0.0002
}

Max_MSP1_Antibody_Growthrate

float

0

1

0.02

The maximum increase in MSP1 antibody capacity during each asexual cycle. The higher this value, the sooner early clearances are observed and the earlier the parasite density envelope decreases.

 
{
    "Max_MSP1_Antibody_Growthrate": 0.045
}

Min_Adapted_Response

float

0

1

0.02

The minimum level of antibody stimulation to a novel antigen. The value sets the low-range asymptote for antibody capacity growth, which is calculated from Antibody_Capacity_Growth_Rate and antigen density, in the presence of any nonzero antigen level.

 
{
    "Min_Adapted_Response": 0.01
}

Mortality_Blocking_Immunity_Decay_Rate

float

0

1

0.001

The rate at which mortality-blocking immunity decays after the mortality-blocking immunity offset period. Enable_Immune_Decay must be set to 1.

 
{
    "Mortality_Blocking_Immunity_Decay_Rate": 0.1
}

Mortality_Blocking_Immunity_Duration_Before_Decay

float

0

45000

0

The number of days after infection until mortality-blocking immunity begins to decay. Enable_Immunity and Enable_Immune_Decay must be set to 1.

 
{
    "Mortality_Blocking_Immunity_Duration_Before_Decay": 270
}

MSP1_Merozoite_Kill_Fraction

double

NA

NA

0.5

The fraction of merozoites inhibited from invading new erythrocytes when MSP1-specific antibody level is 1.

 
{
    "MSP1_Merozoite_Kill_Fraction": 0.4364623975644405
}

Nonspecific_Antibody_Growth_Rate_Factor

float

0

1000

0.5

The factor that adjusts Antibody_Capacity_Growth_Rate for less immunogenic surface proteins, called minor epitopes.

 
{
    "Nonspecific_Antibody_Growth_Rate_Factor": 0.5
}

Nonspecific_Antigenicity_Factor

double

NA

NA

0.2

The nonspecific antigenicity factor that adjusts antibody IRBC kill rate to account for IRBCs caused by antibody responses to antigenically weak surface proteins.

 
{
    "Nonspecific_Antigenicity_Factor": 0.39192559432597257
}

Post_Infection_Acquisition_Multiplier

float

0

1

0

The multiplicative reduction in the probability of reacquiring disease. At the time of recovery, the immunity against acquisition is multiplied by Acquisition_Blocking_Immunity_Decay_Rate x (1 - Post_Infection_Acquisition_Multiplier). Enable_Immunity must be set to 1 (true).

 
{
    "Enable_Immunity": 1,
    "Enable_Immune_Decay": 1,
    "Post_Infection_Acquisition_Multiplier": 0.9
}

Post_Infection_Mortality_Multiplier

float

0

1

0

The multiplicative reduction in the probability of dying from infection after getting reinfected. At the time of recovery, the immunity against mortality is multiplied by Mortality_Blocking_Immunity_Decay_Rate x (1 - Post_Infection_Mortality_Multiplier). Enable_Immunity must be set to 1 (true).

 
{
    "Enable_Immunity": 1,
    "Enable_Immune_Decay": 1,
    "Post_Infection_Mortality_Multiplier": 0.5
}

Post_Infection_Transmission_Multiplier

float

0

1

0

The multiplicative reduction in the probability of transmitting infection after getting reinfected. At the time of recovery, the immunity against transmission is multiplied by Transmission_Blocking_Immunity_Decay_Rate x (1 - Post_Infection_Transmission_Multiplier). Enable_Immunity must be set to 1 (true).

 
{
    "Enable_Immunity": 1,
    "Enable_Immunity_Decay": 1,
    "Post_Infection_Transmission_Multiplier": 0.9
}

RBC_Destruction_Multiplier

double

NA

NA

9.5

The number of total red blood cells destroyed per infected rupturing schizont.

 
{
    "RBC_Destruction_Multiplier": 3.291048711
}

Susceptibility_Initialization_Distribution_Type

enum

NA

NA

DISTRIBUTION_OFF

The method for initializing the susceptibility distribution in the simulated population. Enable_Initial_Susceptibility_Distribution must be set to 1 (true). Possible values are:

DISTRIBUTION_OFF

All individuals default to no susceptibility.

DISTRIBUTION_SIMPLE

Individual susceptibilities are drawn from a distribution whose functional form and parameters are specified in the demographics file in IndividualAttributes using SusceptibilityDistributionFlag, SusceptibilityDistribution1, and SusceptibilityDistribution2 (see Simple distributions parameters).

DISTRIBUTION_COMPLEX

Individual susceptibilities are drawn from an age-dependent piecewise linear function for each specific antibody in the demographics file (see Complex distributions parameters).

 
{
    "Enable_Immunity": 1,
    "Enable_Initial_Susceptibility_Distribution": 1,
    "Susceptibility_Initialization_Distribution_Type": "DISTRIBUTION_COMPLEX"
}

Susceptibility_Type

enum

NA

NA

FRACTIONAL

Controls implementation of an individual’s susceptibility. Currently only relevant to Maternal_Protection_Type parameter. Possible values are:

FRACTIONAL

All agents are assigned equal values between 0 and 1 according to a governing equation as specified by Maternal_Protection_Type.

BINARY

Agents receive a value of either 0 or 1 (complete immunity or susceptibility) with the probability determined by a governing equation as specified by Maternal_Protection_Type.

 
{
    "Susceptibility_Type": "FRACTIONAL",
    "Enable_Maternal_Protection": 1,
    "Maternal_Protection_Type": "LINEAR_FRACTIONAL"
}

Transmission_Blocking_Immunity_Decay_Rate

float

0

1000

0.001

The rate at which transmission-blocking immunity decays after the base transmission-blocking immunity offset period. Used only when Enable_Immunity and Enable_Immune_Decay parameters are set to true (1).

 
{
    "Transmission_Blocking_Immunity_Decay_Rate": 0.01
}

Transmission_Blocking_Immunity_Duration_Before_Decay

float

0

45000

0

The number of days after infection until transmission-blocking immunity begins to decay. Only used when Enable_Immunity and Enable_Immune_Decay parameters are set to true (1).

 
{
    "Transmission_Blocking_Immunity_Duration_Before_Decay": 90
}
Incubation

The following parameters determine the characteristics of the incubation period.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Incubation_Period_Constant

float

0

3.40282E+38

-1

The incubation period, in days, to use for all individuals when Incubation_Period_Distribution is set to CONSTANT_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "CONSTANT_DISTRIBUTION",
    "Incubation_Period_Constant": 8
}

Incubation_Period_Distribution

enum

NA

NA

NOT_INITIALIZED

The distribution type to use for assigning the incubation period to each individual in the population. Each individual’s value is a random draw from the distribution.

Possible values are:

NOT_INITIALIZED

No distribution set.

CONSTANT_DISTRIBUTION

Use the same value for each individual. Set Incubation_Period_Constant.

UNIFORM_DISTRIBUTION

Use a uniform distribution with a given minimum and maximum. Set Incubation_Period_Max and Incubation_Period_Min.

GAUSSIAN_DISTRIBUTION

The distribution is Gaussian (or normal). Values are resampled to ensure >= 0. Set Incubation_Period_Gaussian_Mean and Incubation_Period_Gaussian_Std_Dev.

EXPONENTIAL_DISTRIBUTION

The distribution is exponential with a given mean. Set Incubation_Period_Exponential.

WEIBULL_DISTRIBUTION

Use a Weibull distribution with a given shape and scale. Set Incubation_Period_Kappa and Incubation_Period_Lambda.

LOG_NORMAL_DISTRIBUTION

Use a log-normal distribution with a given mean and standard deviation of the natural log. Set Incubation_Period_Log_Normal_Mu and Incubation_Period_Log_Normal_Sigma.

POISSON_DISTRIBUTION

Use a Poisson distribution with a given mean. Set Incubation_Period_Poisson_Mean.

DUAL_CONSTANT_DISTRIBUTION

Use a distribution where some individuals are set to a value of zero and the rest to a given value. Set Incubation_Period_Proportion_0 and Peak_2_Value. This distribution does not use the parameters set for CONSTANT_DISTRIBUTION.

DUAL_EXPONENTIAL_DISTRIBUTION

Use two exponential distributions with given means. Set Incubation_Period_Mean_1, Incubation_Period_Mean_2, and Incubation_Period_Proportion_1. This distribution does not use the parameters set for DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Incubation_Period_Gaussian_Mean": 8,
    "Incubation_Period_Gaussian_Std_Dev": 1.5
}

Incubation_Period_Exponential

float

0

3.40282E+38

-1

The mean incubation period, in days, when Incubation_Period_Distribution is set to EXPONENTIAL_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "EXPONENTIAL_DISTRIBUTION",
    "Incubation_Period_Exponential": 4.25
}

Incubation_Period_Gaussian_Mean

float

0

3.40282E+38

-1

The mean of the incubation period, in days, when Incubation_Period_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Incubation_Period_Gaussian_Mean": 8,
    "Incubation_Period_Gaussian_Std_Dev": 1.5
}

Incubation_Period_Gaussian_Std_Dev

float

1.17549E-38

3.40282E+38

-1

The standard deviation of the incubation period, in days, when Incubation_Period_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Incubation_Period_Gaussian_Mean": 8,
    "Incubation_Period_Gaussian_Std_Dev": 1.5
}

Incubation_Period_Kappa

float

1.17549E-38

3.40282E+38

-1

The shape value for the incubation period, in days, when Incubation_Period_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "WEIBULL_DISTRIBUTION",
    "Incubation_Period_Kappa": 0.9,
    "Incubation_Period_Lambda": 1.5
}

Incubation_Period_Lambda

float

1.17549E-38

3.40282E+38

-1

The scale value for the incubation period, in days, when Incubation_Period_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "WEIBULL_DISTRIBUTION",
    "Incubation_Period_Kappa": 0.9,
    "Incubation_Period_Lambda": 1.5
}

Incubation_Period_Log_Normal_Mu

float

-3.40282e+38

1.70141e+38

3.40282e+38

The mean of the natural log of the incubation period, in days, when Incubation_Period_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Incubation_Period_Log_Normal_Mu": 4,
    "Incubation_Period_Log_Normal_Sigma": 1
}

Incubation_Period_Log_Normal_Sigma

float

1.17549E-38

3.40282E+38

1

The standard deviation of the natural log of the incubation period, in days, when Incubation_Period_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Incubation_Period_Log_Normal_Mu": 4,
    "Incubation_Period_Log_Normal_Sigma": 1
}

Incubation_Period_Max

float

0

3.40282E+38

-1

The maximum incubation period, in days, when Incubation_Period_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "UNIFORM_DISTRIBUTION",
    "Incubation_Period_Min": 2,
    "Incubation_Period_Max": 7
}

Incubation_Period_Mean_1

float

1.17549E-38

3.40282E+38

-1

The mean of the first exponential distribution, in days, when Incubation_Period_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Incubation_Period_Mean_1": 4,
    "Incubation_Period_Mean_2": 12,
    "Incubation_Period_Proportion_1": 0.2
}

Incubation_Period_Mean_2

float

1.17549E-38

3.40282E+38

-1

The mean of the second exponential distribution, in days, when Incubation_Period_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Incubation_Period_Mean_1": 4,
    "Incubation_Period_Mean_2": 12,
    "Incubation_Period_Proportion_1": 0.2
}

Incubation_Period_Min

float

0

3.40282E+38

-1

The minimum incubation period, in days, when Incubation_Period_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "UNIFORM_DISTRIBUTION",
    "Incubation_Period_Min": 2,
    "Incubation_Period_Max": 7
}

Incubation_Period_Peak_2_Value

float

0

3.40282E+38

-1

The incubation period value, in days, to assign to the remaining individuals when Incubation_Period_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Incubation_Period_Proportion_0": 0.25,
    "Incubation_Period_Peak_2_Value": 5
}

Incubation_Period_Poisson_Mean

float

0

3.40282E+38

-1

The mean of the incubation period, in days, when Incubation_Period_Distribution is set to POISSON_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "POISSON_DISTRIBUTION",
    "Incubation_Period_Poisson_Mean": 5
}

Incubation_Period_Proportion_0

float

0

1

-1

The proportion of individuals to assign a value of zero days incubation when Incubation_Period_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Incubation_Period_Proportion_0": 0.25,
    "Incubation_Period_Peak_2_Value": 5
}

Incubation_Period_Proportion_1

float

0

1

-1

The proportion of individuals in the first exponential distribution when Incubation_Period_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Incubation_Period_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Incubation_Period_Mean_1": 4,
    "Incubation_Period_Mean_2": 12,
    "Incubation_Period_Proportion_1": 0.2
}

Symptomatic_Infectious_Offset

float

-3.40282e+38

3.40282e+38

3.40282e+38

Amount of time, in days, after the infectious period starts that symptoms appear. Negative values imply an individual is symptomatic before infectious. If this offset is greater than the infectious duration, the infection will not be symptomatic. For example, if Incubation_Period_Constant is set to 10 and Symptomatic_Infectious_Offset is set to 4, then an infected person would become symptomatic 14 days after transmission.

 
{
    "Infectious_Period_Distribution": "CONSTANT_DISTRIBUTION",
    "Symptomatic_Infectious_Offset": 4,
    "Incubation_Period_Constant": 10
}
Infectivity and transmission

The following parameters determine aspects of infectivity and disease transmission. For example, how infectious individuals are and the length of time for which they remain infectious, whether the disease can be maternally transmitted, and how population density affects infectivity.

The malaria transmission model does not use many of the parameters provided by the generic simulation type. Instead, gametocyte abundances and cytokine mediated infectiousness are modeled explicitly. See Vector transmission model for more information.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Acquire_Modifier

float

0

1

1

Modifier of the probability of successful infection of a mosquito by an infected individual, given the individual’s infectiousness.

 
{
    "Vector_Species_Params": [
        {
            "Name": "aegypti",
            "Acquire_Modifier": 1
        }
    ]
}

Age_Dependent_Biting_Risk_Type

enum

NA

NA

OFF

The type of functional form for age-dependent biting risk. Possible values are:

OFF

This is the default value.

LINEAR

The biting risk is 20% of the adult exposure rising linearly until age 20.

SURFACE_AREA_DEPENDENT

The biting risk rises linearly from 7% to 23% over the first two years of life and then rises with a shallower linear slope to the adult value at age 20.

 
{
    "Age_Dependent_Biting_Risk_Type": "SURFACE_AREA_DEPENDENT"
}

Base_Infectivity

float

0

1000

0.3

The base infectiousness of individuals before accounting for transmission-blocking effects of acquired immunity and/or campaign interventions.

For vector and malaria simulations, this is the probability of infecting a mosquito during a successful blood meal (modulated by the vector parameter Acquire_Modifier). The sum infectiousness of an individual is not allowed to exceed 100%.

 
{
    "Base_Infectivity": 0.5
}

Enable_Heterogeneous_Intranode_Transmission

boolean

0

1

0

Controls whether or not individuals experience heterogeneous disease transmission within a node. When set to true (1), individual property definitions and the \(\beta\) matrix must be specified in the demographics file (see NodeProperties and IndividualProperties parameters). The \(\beta\) values are multiplied with the \(\beta\) 0 value configured by Base_Infectivity.

This is used only in generic, environmental, and typhoid simulations, but must be set to false (0) for all other simulation types. Heterogeneous transmission for other diseases uses other mechanistic parameters included with the simulation type.

 
{
    "Enable_Heterogeneous_Intranode_Transmission": 1
}

Enable_Infectivity_Reservoir

boolean

0

1

0

Controls whether or not an exogeneous reservoir of infectivity will be included in the simulation and allows for the infectivity in a node to be increased additively. When set to 1 (true), the demographics parameter InfectivityReservoirSize is expected in NodeAtttributes for each node.

Warning

Do not set both Enable_Infectivity_Reservoir and Enable_Strain_Tracking to true (1) - as this combination will cause an exception error.

 
{
    "Enable_Infectivity_Reservoir": 1
}

Enable_Initial_Prevalence

boolean

0

1

0

Controls whether or not parameters in the demographics file are used to define a distribution for the number of infected people per node at the beginning of the simulation. Set the distribution under NodeAttributes using PrevalenceDistributionFlag, PrevalenceDistribution1, and PrevalenceDistribution2.

 
{
    "Enable_Initial_Prevalence": 1
}

Enable_Skipping

boolean

0

1

0

Controls whether or not the simulation uses an optimization that can increase performance by up to 50% in some cases by probabilistically exposing individuals rather than exposing every single person. Useful in low-prevalence, high-population scenarios.

 
{
    "Enable_Skipping": 0
}

Enable_Superinfection

boolean

0

1

0

Controls whether or not an individual can have multiple infections simultaneously. Set to true (1) to allow for multiple simultaneous infections; set to false (0) if multiple infections are not possible. Set the Max_Individual_Infections parameter.

 
{
    "Enable_Superinfection": 1,
    "Max_Individual_Infections": 2
}

Enable_Termination_On_Zero_Total_Infectivity

boolean

0

1

0

Controls whether or not the simulation should be ended when total infectivity falls to zero. Supported only in single-node simulations.

 
{
    "Enable_Termination_On_Zero_Total_Infectivity": 1,
    "Minimum_End_Time": 3650
}

Infected_Arrhenius_1

float

0

1.00E+15

1.17E+11

The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the daily rate of fractional progression of infected mosquitoes to an infectious state. The duration of sporogony is a decreasing function of temperature. The variable a1 is a temperature-independent scale factor on the progression rate to infectiousness.

 
{
    "Vector_Species_Params": [
        {
            "Name": "arabiensis",
            "Acquire_Modifier": 0.2,
            "Adult_Life_Expectancy": 20,
            "Anthropophily": 0.65,
            "Aquatic_Arrhenius_1": 84200000000,
            "Aquatic_Arrhenius_2": 8328,
            "Aquatic_Mortality_Rate": 0.1,
            "Days_Between_Feeds": 3,
            "Egg_Batch_Size": 100,
            "Habitats": [
                {
                    "Habitat_Type": "TEMPORARY_RAINFALL",
                    "Max_Larval_Capacity": 11250000000.0
                }
            ],
            "Immature_Duration": 2,
            "Indoor_Feeding_Fraction": 1,
            "Infected_Arrhenius_1": 117000000000,
            "Infected_Arrhenius_2": 8336,
            "Infected_Egg_Batch_Factor": 0.8,
            "Infectious_Human_Feed_Mortality_Factor": 1.5,
            "Male_Life_Expectancy": 10,
            "Temperature_Dependent_Feeding_Cycle": "NO_TEMPERATURE_DEPENDENCE",
            "Transmission_Rate": 0.5,
            "Vector_Sugar_Feeding_Frequency": "VECTOR_SUGAR_FEEDING_NONE"
        }
    ]
}

Infected_Arrhenius_2

float

0

1.00E+15

8340

The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the daily rate of fractional progression of infected mosquitoes to an infectious state. The duration of sporogony is a decreasing function of temperature. The variable a2 is a temperature-dependent scale factor on the progression rate to infectiousness.

 
{
    "Vector_Species_Params": [
        {
            "Name": "arabiensis",
            "Acquire_Modifier": 0.2,
            "Adult_Life_Expectancy": 20,
            "Anthropophily": 0.65,
            "Aquatic_Arrhenius_1": 84200000000,
            "Aquatic_Arrhenius_2": 8328,
            "Aquatic_Mortality_Rate": 0.1,
            "Days_Between_Feeds": 3,
            "Egg_Batch_Size": 100,
            "Habitats": [
                {
                    "Habitat_Type": "TEMPORARY_RAINFALL",
                    "Max_Larval_Capacity": 11250000000.0
                }
            ],
            "Immature_Duration": 2,
            "Indoor_Feeding_Fraction": 1,
            "Infected_Arrhenius_1": 117000000000,
            "Infected_Arrhenius_2": 8336,
            "Infected_Egg_Batch_Factor": 0.8,
            "Infectious_Human_Feed_Mortality_Factor": 1.5,
            "Male_Life_Expectancy": 10,
            "Temperature_Dependent_Feeding_Cycle": "NO_TEMPERATURE_DEPENDENCE",
            "Transmission_Rate": 0.5,
            "Vector_Sugar_Feeding_Frequency": "VECTOR_SUGAR_FEEDING_NONE"
        }
    ]
}

Infected_Egg_Batch_Factor

float

0

10

0.8

The dimensionless factor used to modify mosquito egg batch size in order to account for reduced fertility effects arising due to infection (e.g. when females undergo sporogony).

 
{
    "Vector_Species_Params": [
        {
            "Name": "arabiensis",
            "Acquire_Modifier": 0.2,
            "Adult_Life_Expectancy": 20,
            "Anthropophily": 0.65,
            "Aquatic_Arrhenius_1": 84200000000,
            "Aquatic_Arrhenius_2": 8328,
            "Aquatic_Mortality_Rate": 0.1,
            "Days_Between_Feeds": 3,
            "Egg_Batch_Size": 100,
            "Habitats": [
                {
                    "Habitat_Type": "TEMPORARY_RAINFALL",
                    "Max_Larval_Capacity": 11250000000.0
                }
            ],
            "Immature_Duration": 2,
            "Indoor_Feeding_Fraction": 1,
            "Infected_Arrhenius_1": 117000000000,
            "Infected_Arrhenius_2": 8336,
            "Infected_Egg_Batch_Factor": 0.8,
            "Infectious_Human_Feed_Mortality_Factor": 1.5,
            "Male_Life_Expectancy": 10,
            "Temperature_Dependent_Feeding_Cycle": "NO_TEMPERATURE_DEPENDENCE",
            "Transmission_Rate": 0.5,
            "Vector_Sugar_Feeding_Frequency": "VECTOR_SUGAR_FEEDING_NONE"
        }
    ]
}

Infection_Updates_Per_Timestep

integer

0

144

1

The number of infection updates executed during each timestep; note that a timestep defaults to one day.

 
{
    "Infection_Updates_Per_Timestep": 1
}

Infectious_Human_Feed_Mortality_Factor

float

0

1000

1.5

The (dimensionless) factor used to modify the death rate of mosquitoes when feeding on humans, to account for the higher mortality rate infected mosquitoes experience during human feeds versus uninfected mosquitoes.

 
{
    "Vector_Species_Params": [
        {
            "Name": "arabiensis",
            "Acquire_Modifier": 0.2,
            "Adult_Life_Expectancy": 20,
            "Anthropophily": 0.65,
            "Aquatic_Arrhenius_1": 84200000000,
            "Aquatic_Arrhenius_2": 8328,
            "Aquatic_Mortality_Rate": 0.1,
            "Days_Between_Feeds": 3,
            "Egg_Batch_Size": 100,
            "Habitats": [
                {
                    "Habitat_Type": "TEMPORARY_RAINFALL",
                    "Max_Larval_Capacity": 11250000000.0
                }
            ],
            "Immature_Duration": 2,
            "Indoor_Feeding_Fraction": 1,
            "Infected_Arrhenius_1": 117000000000,
            "Infected_Arrhenius_2": 8336,
            "Infected_Egg_Batch_Factor": 0.8,
            "Infectious_Human_Feed_Mortality_Factor": 1.5,
            "Male_Life_Expectancy": 10,
            "Temperature_Dependent_Feeding_Cycle": "NO_TEMPERATURE_DEPENDENCE",
            "Transmission_Rate": 0.5,
            "Vector_Sugar_Feeding_Frequency": "VECTOR_SUGAR_FEEDING_NONE"
        }
    ]
}

Infectious_Period_Constant

float

0

3.40282E+38

-1

The infectious period to use for all individuals, in days, when Infectious_Period_Distribution is set to CONSTANT_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "CONSTANT_DISTRIBUTION",
    "Infectious_Period_Constant": 8
}

Infectious_Period_Distribution

enum

NA

NA

NOT_INITIALIZED

The distribution type to use for assigning the infectious period to each individual in the population. Each individual’s value is a random draw from the distribution.

Possible values are:

NOT_INITIALIZED

No distribution set.

CONSTANT_DISTRIBUTION

Use the same value for each individual. Set Infectious_Period_Constant.

UNIFORM_DISTRIBUTION

Use a uniform distribution with a given minimum and maximum. Set Infectious_Period_Max and Infectious_Period_Min.

GAUSSIAN_DISTRIBUTION

The distribution is Gaussian (or normal). Values are resampled to ensure >= 0. Set Infectious_Period_Gaussian_Mean and Infectious_Period_Gaussian_Std_Dev.

EXPONENTIAL_DISTRIBUTION

The distribution is exponential with a given mean. Set Infectious_Period_Exponential.

WEIBULL_DISTRIBUTION

Use a Weibull distribution with a given shape and scale. Set Infectious_Period_Kappa and Infectious_Period_Lambda.

LOG_NORMAL_DISTRIBUTION

Use a log-normal distribution with a given mean and standard deviation of the natural log. Set Infectious_Period_Log_Normal_Mu and Infectious_Period_Log_Normal_Sigma.

POISSON_DISTRIBUTION

Use a Poisson distribution with a given mean. Set Infectious_Period_Poisson_Mean.

DUAL_CONSTANT_DISTRIBUTION

Use a distribution where some individuals are set to a value of zero and the rest to a given value. Set Infectious_Period_Proportion_0 and Peak_2_Value. This distribution does not use the parameters set for CONSTANT_DISTRIBUTION.

DUAL_EXPONENTIAL_DISTRIBUTION

Use two exponential distributions with given means. Set Infectious_Period_Mean_1, Infectious_Period_Mean_2, and Infectious_Period_Proportion_1. This distribution does not use the parameters set for EXPONENTIAL_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Infectious_Period_Gaussian_Mean": 4,
    "Infectious_Period_Gaussian_Std_Dev": 1
}

Infectious_Period_Exponential

float

0

3.40282E+38

-1

The mean infectious period, in days, when Infectious_Period_Distribution is set to EXPONENTIAL_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "EXPONENTIAL_DISTRIBUTION",
    "Infectious_Period_Exponential": 4.25
}

Infectious_Period_Gaussian_Mean

float

0

3.40282E+38

-1

The mean infectious period, in days, when Infectious_Period_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Infectious_Period_Gaussian_Mean": 4,
    "Infectious_Period_Gaussian_Std_Dev": 1
}

Infectious_Period_Gaussian_Std_Dev

float

1.17549E-38

3.40282E+38

-1

The standard deviation of the infectious period, in days, when Infectious_Period_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Infectious_Period_Gaussian_Mean": 4,
    "Infectious_Period_Gaussian_Std_Dev": 1
}

Infectious_Period_Kappa

float

1.17549E-38

3.40282E+38

-1

The shape value for the infectious period, in days, when Infectious_Period_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "WEIBULL_DISTRIBUTION",
    "Infectious_Period_Kappa": 0.9,
    "Infectious_Period_Lambda": 1.5
}

Infectious_Period_Lambda

float

1.17549E-38

3.40282E+38

-1

The scale value for the infectious period, in days, when Infectious_Period_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "WEIBULL_DISTRIBUTION",
    "Infectious_Period_Kappa": 0.9,
    "Infectious_Period_Lambda": 1.5
}

Infectious_Period_Log_Normal_Mu

float

-3.40282e+38

1.70141e+38

3.40282e+38

The mean of the natural log of the infectious period, in days, when Infectious_Period_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Infectious_Period_Log_Normal_Mu": 9,
    "Infectious_Period_Log_Normal_Sigma": 2
}

Infectious_Period_Log_Normal_Sigma

float

-3.40282e+38

1.70141e+38

3.40282e+38

The standard deviation of the natural log of the infectious period, in days, when Infectious_Period_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Infectious_Period_Log_Normal_Mu": 9,
    "Infectious_Period_Log_Normal_Sigma": 2
}

Infectious_Period_Max

float

0

3.40282E+38

-1

The maximum infectious period, in days, when Infectious_Period_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "UNIFORM_DISTRIBUTION",
    "Infectious_Period_Min": 2,
    "Infectious_Period_Max": 7
}

Infectious_Period_Mean_1

float

1.17549E-38

3.4E+38

-1

The mean of the first exponential distribution, in days, when Infectious_Period_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Infectious_Period_Mean_1": 4,
    "Infectious_Period_Mean_2": 12,
    "Infectious_Period_Proportion_1": 0.2
}

Infectious_Period_Mean_2

float

1.17549E-38

3.40282E+38

-1

The mean of the second exponential distribution, in days, when Infectious_Period_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Infectious_Period_Mean_1": 4,
    "Infectious_Period_Mean_2": 12,
    "Infectious_Period_Proportion_1": 0.2
}

Infectious_Period_Min

float

0

3.40282E+38

-1

The minimum infectious period, in days, when Infectious_Period_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "UNIFORM_DISTRIBUTION",
    "Infectious_Period_Min": 2,
    "Infectious_Period_Max": 7
}

Infectious_Period_Peak_2_Value

float

0

3.40282E+38

-1

The infectious period value, in days, to assign to the remaining individuals when Infectious_Period_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Infectious_Period_Proportion_0": 0.25,
    "Infectious_Period_Peak_2_Value": 5
}

Infectious_Period_Poisson_Mean

float

0

3.40282E+38

-1

The mean of the infectious period, in days, when Infectious_Period_Distribution is set to POISSON_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "POISSON_DISTRIBUTION",
    "Infectious_Period_Poisson_Mean": 5
}

Infectious_Period_Proportion_0

float

0

1

-1

The proportion of individuals to assign a value of zero days infectiousness when Infectious_Period_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Infectious_Period_Proportion_0": 0.25,
    "Infectious_Period_Peak_2_Value": 5
}

Infectious_Period_Proportion_1

float

0

1

-1

The proportion of individuals in the first exponential distribution when Infectious_Period_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Infectious_Period_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Infectious_Period_Mean_1": 4,
    "Infectious_Period_Mean_2": 12,
    "Infectious_Period_Proportion_1": 0.2
}

Infectivity_Exponential_Baseline

float

0

1

0

The scale factor applied to Base_Infectivity at the beginning of a simulation, before the infectivity begins to grow exponentially. Infectivity_Scale_Type must be set to EXPONENTIAL_FUNCTION_OF_TIME.

 
{
    "Infectivity_Exponential_Baseline": 0.1,
    "Infectivity_Exponential_Delay": 90,
    "Infectivity_Exponential_Rate": 45,
    "Infectivity_Scale_Type": "EXPONENTIAL_FUNCTION_OF_TIME"
}

Malaria_Strain_Model

enum

NA

NA

FALCIPARUM_NONRANDOM_STRAIN

The generator that is used to construct the antigenic repertoire of a malaria infection. To create parasite diversity, various antigenic strains are created by conducting draws for merozoite surface protein (MSP) variants, Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) variants, and minor surface epitopes, out of available populations. Each possible value for this parameter utilizes different settings for the available population draws. Possible values are:

FALCIPARUM_NONRANDOM_STRAIN

The strain created uses the exact same MSP variant, non-specific PfEMP1 variant, and ordered repertoire of PfEMP1 major epitopes. Minor surface protein epitopes are randomly drawn from a set of 5.

FALCIPARUM_RANDOM50_STRAIN

Strain creation extends the logic used for FALCIPARUM_NONRANDOM_STRAIN, but increases variation by randomly drawing PfEMP1 variants from a population of 50, and allowing the variants to have a random switching order. Random draws are done with replacement.

FALCIPARUM_RANDOM_STRAIN

The MSP variant is drawn from the population set by the parameter Falciparum_MSP_Variants, a repertoire of 50 PfEMP1 variants is drawn from the population set by the parameter Falciparum_PfEMP1_Variants, and the set of five minor surface protein epitope variants is drawn from the population set by the parameter Falciparum_Nonspecific_Types.

FALCIPARUM_STRAIN_GENERATOR

This is an exploratory effort created to provide a pseudo-random generation of strains similar to FALCIPARUM_RANDOM_STRAIN, but with variant indices deterministically assigned according to the strain ID.

 
{
    "Malaria_Strain_Model": "FALCIPARUM_STRAIN_GENERATOR"
}

Maternal_Infection_Transmission_Probability

float

0

1

0

The probability of transmission of infection from mother to infant at birth. Enable_Maternal_Infection_Transmission must be set to 1.

Note

For malaria and vector simulations, set this to 0. Instead, use the Maternal_Antibody_Protection, Maternal_Antibody_Decay_Rate, and Maternal_Antibodies_Type parameters.

 
{
    "Maternal_Infection_Transmission_Probability": 0.3
}

Max_Individual_Infections

integer

0

1000

1

The limit on the number of infections that an individual can have simultaneously. Enable_Superinfection must be set to 1.

 
{
    "Max_Individual_Infections": 5
}

Population_Density_C50

float

0

3.40E+38

10

The population density at which R0 for a 2.5-arc minute square reaches half of its initial value. Population_Density_Infectivity_Correction must be set to SATURATING_FUNCTION_OF_DENSITY.

 
{
    "Population_Density_C50": 30
}

Population_Density_Infectivity_Correction

enum

NA

NA

CONSTANT_INFECTIVITY

Correction to alter infectivity by population density set in the Population_Density_C50 parameter. Measured in people per square kilometer. Possible values are:

  • CONSTANT_INFECTIVITY

  • SATURATING_FUNCTION_OF_DENSITY

Note

Sparsely populated areas have a lower infectivity, while densely populated areas have a higher infectivity, which rises to saturate at the Base_Infectivity value.

 
{
    "Population_Density_Infectivity_Correction": "SATURATING_FUNCTION_OF_DENSITY"
}

Pyrogenic_Threshold

float

0.1

20000

1000

The level of bloodstream infection, measured in IRBC per microliter, at which stimulation of the innate inflammatory immune response is half its maximum value.

 
{
    "Pyrogenic_Threshold": 15000
}

Relative_Sample_Rate_Immune

float

0.001

1

0.1

The relative sampling rate for people who have acquired immunity through recovery or vaccination. The immune threshold at which to downsample is set by Immune_Threshold_For_Downsampling. If set to 1, this will have no effect, even if the individual’s immunity modifier is below threshold. This can be a useful sanity check while learning this feature. Individual_Sampling_Type must be set to ADAPTED_SAMPLING_BY_IMMUNE_STATE.

 
{
    "Relative_Sample_Rate_Immune": 0.1,
    "Immune_Threshold_For_Downsampling": 0.8,
    "Individual_Sampling_Type": "ADAPTED_SAMPLING_BY_IMMUNE_STATE"
}

Susceptibility_Type

enum

NA

NA

FRACTIONAL

Controls implementation of an individual’s susceptibility. Currently only relevant to Maternal_Protection_Type parameter. Possible values are:

FRACTIONAL

All agents are assigned equal values between 0 and 1 according to a governing equation as specified by Maternal_Protection_Type.

BINARY

Agents receive a value of either 0 or 1 (complete immunity or susceptibility) with the probability determined by a governing equation as specified by Maternal_Protection_Type.

 
{
    "Susceptibility_Type": "FRACTIONAL",
    "Enable_Maternal_Protection": 1,
    "Maternal_Protection_Type": "LINEAR_FRACTIONAL"
}

Symptomatic_Infectious_Offset

float

-3.40282e+38

3.40282e+38

3.40282e+38

Amount of time, in days, after the infectious period starts that symptoms appear. Negative values imply an individual is symptomatic before infectious. If this offset is greater than the infectious duration, the infection will not be symptomatic. For example, if Incubation_Period_Constant is set to 10 and Symptomatic_Infectious_Offset is set to 4, then an infected person would become symptomatic 14 days after transmission.

 
{
    "Infectious_Period_Distribution": "CONSTANT_DISTRIBUTION",
    "Symptomatic_Infectious_Offset": 4,
    "Incubation_Period_Constant": 10
}

Transmission_Blocking_Immunity_Decay_Rate

float

0

1000

0.001

The rate at which transmission-blocking immunity decays after the base transmission-blocking immunity offset period. Used only when Enable_Immunity and Enable_Immune_Decay parameters are set to true (1).

 
{
    "Transmission_Blocking_Immunity_Decay_Rate": 0.01
}

Transmission_Blocking_Immunity_Duration_Before_Decay

float

0

45000

0

The number of days after infection until transmission-blocking immunity begins to decay. Only used when Enable_Immunity and Enable_Immune_Decay parameters are set to true (1).

 
{
    "Transmission_Blocking_Immunity_Duration_Before_Decay": 90
}

Transmission_Rate

float

0

1

0.5

The probability that the bite of an infected mosquito establishes a new infection in an immunologically naive and uninfected individual, or the modifier of the probability of success for an individual with pre-erythrocytic immunity. Note that each mosquito species will have their own Transmission_Rate parameter.

 
{
    "Vector_Species_Params": [
        {
            "Name": "arabiensis",
            "Acquire_Modifier": 0.2,
            "Adult_Life_Expectancy": 20,
            "Anthropophily": 0.65,
            "Aquatic_Arrhenius_1": 84200000000,
            "Aquatic_Arrhenius_2": 8328,
            "Aquatic_Mortality_Rate": 0.1,
            "Days_Between_Feeds": 3,
            "Egg_Batch_Size": 100,
            "Habitats": [
                {
                    "Habitat_Type": "TEMPORARY_RAINFALL",
                    "Max_Larval_Capacity": 11250000000.0
                }
            ],
            "Immature_Duration": 2,
            "Indoor_Feeding_Fraction": 1,
            "Infected_Arrhenius_1": 117000000000,
            "Infected_Arrhenius_2": 8336,
            "Infected_Egg_Batch_Factor": 0.8,
            "Infectious_Human_Feed_Mortality_Factor": 1.5,
            "Male_Life_Expectancy": 10,
            "Temperature_Dependent_Feeding_Cycle": "NO_TEMPERATURE_DEPENDENCE",
            "Transmission_Rate": 0.5,
            "Vector_Sugar_Feeding_Frequency": "VECTOR_SUGAR_FEEDING_NONE"
        }
    ]
}
Input files

The following parameters set the paths to the the campaign file and the input files for climate, migration, demographics, and load-balancing.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Air_Migration_Filename

string

NA

NA

_add-your-air-migration-file_.json

The path to the input file that defines patterns of migration by airplane. Enable_Air_Migration must be set to true (1). See Migration files for information on migration files.

 
{
    "Migration_Model": "FIXED_RATE_MIGRATION",
    "Enable_Air_Migration": 1,
    "Air_Migration_Filename": "../Global_1degree_air_migration.bin"
}

Air_Temperature_Filename

string

NA

NA

_add-your-air-temperature-file_.bin

The path to the input file that defines air temperature data measured two meters above ground. Climate_Model must be set to CLIMATE_BY_DATA. The file must be in .bin format.

 
{
    "Climate_Model": "CLIMATE_BY_DATA",
    "Air_Temperature_Filename": "Namawala_single_node_air_temperature_daily.bin"
}

Campaign_Filename

string

NA

NA

The path to the campaign file. It is required when interventions are part of the simulation and Enable_Interventions is set to true (1). The file must be in .json format.

 
{
    "Enable_Interventions": 1,
    "Campaign_Filename": "campaign.json"
}

Demographics_Filenames

array of strings

NA

NA

An array of the paths to demographics files containing information on the identity and demographics of the region to simulate. The files must be in .json format. Note that this parameter is only required when Enable_Demographics_Builtin is set to 0.

 
{
    "Demographics_Filenames": [
        "Namawala_single_node_demographics.json",
        "Namawala_demographics_overlay.json"
    ]
}

Family_Migration_Filename

string

NA

NA

_add-your-family-migration-file_.json

The name of the binary file to use to configure family migration. Enable_Family_Migration must be set to true (1). See Migration files for information on migration files.

 
{
    "Migration_Model": "FIXED_RATE_MIGRATION",
    "Enable_Family_Migration": 1,
    "Family_Migration_Filename": "../inputs/family_migration.bin"
}

Koppen_Filename

string

NA

NA

_add-your-koppen-climate-file_.json

The path to the input file used to specify Koppen climate classifications. Climate_Model must be set to CLIMATE_KOPPEN.

 
{
    "Koppen_Filename": "Mad_2_5arcminute_koppen.dat"
}

Land_Temperature_Filename

string

NA

NA

_add-your-land-temp-file_.bin

The path of the input file defining temperature data measured at land surface; used only when Climate_Model is set to CLIMATE_BY_DATA. The file must be in .bin format.

 
{
    "Land_Temperature_Filename": "Namawala_single_node_land_temperature_daily.bin"
}

Load_Balance_Filename

string

NA

NA

UNINITIALIZED STRING

The path to the input file used when a static load balancing scheme is selected. The file must be in .json format.

 
{
    "Load_Balance_Filename": "GitHub_426_LoadBalance.json"
}

Local_Migration_Filename

string

NA

NA

_add-your-local-migration-file_.json

The path of the input file which defines patterns of migration to adjacent nodes by foot travel. See Migration files for information on migration files.

 
{
    "Local_Migration_Filename": "Local_Migration.bin"
}

Rainfall_Filename

string

NA

NA

_add-your-rainfall-file_.bin

The path of the input file which defines rainfall data. Climate_Model must be set to CLIMATE_BY_DATA. The file must be in .bin format.

 
{
    "Rainfall_Filename": "Namawala_single_node_rainfall_daily.bin"
}

Regional_Migration_Filename

string

NA

NA

_add-your-regional-migration-file_.json

The path of the input file which defines patterns of migration by vehicle via road or rail network. If the node is not on a road or rail network, regional migration focuses on the closest hub city in the network. The file must be in .bin format. See Migration files for information on migration files.

 
{
    "Regional_Migration_Filename": "Regional_Migration.bin"
}

Relative_Humidity_Filename

string

NA

NA

_add-your-relative-humidity-file__hum.bin

The path of the input file which defines relative humidity data measured 2 meters above ground. Climate_Model must be set to CLIMATE_BY_DATA. The file must be in .bin format.

 
{
    "Relative_Humidity_Filename": "Namawala_single_node_relative_humidity_daily.bin"
}

Sea_Migration_Filename

string

NA

NA

_add-your-sea-migration-file_.json

The path of the input file which defines patterns of migration by ship. Only used when Enable_Sea_Migration is set to true (1). The file must be in .bin format. See Migration files for information on migration files.

 
{
    "Sea_Migration_Filename": "5x5_Households_Work_Migration.bin"
}

Serialized_Population_Filenames

array of strings

NA

NA

NA

An array of filenames with serialized population data. The number of filenames must match the number of cores used for the simulation. The files must be in .dtk format. Serialized population files are created using Serialization_Time_Steps.

 
{
    "Serialized_Population_Filenames": [
        "state-00010.dtk"
    ]
}

Serialized_Population_Path

string

NA

NA

.

The root path for the serialized population files. Serialized population files are created using Serialization_Time_Steps.

 
{
    "Serialized_Population_Path": "../00_Generic_Version_1_save/output"
}

Vector_Migration_Filename_Local

string

NA

NA

UNSPECIFIED_FILE

The path to the vector migration file which defines patterns of vector migration to adjacent nodes. Enable_Vector_Migration must be set to 1. The file must be in .bin format.

 
{
    "Vector_Migration_Filename_Local": "5x5_Households_Local_Vector_Migration.bin"
}

Vector_Migration_Filename_Regional

string

NA

NA

UNSPECIFIED_FILE

The path to the vector migration file which defines patterns of vector migration to non-adjacent nodes. Enable_Vector_Migration must be set to 1. The file must be in .bin format.

 
{
    "Vector_Migration_Filename_Regional": "5x5_Households_Regional_Vector_Migration.bin"
}
Larval habitat

The following parameters determine mosquito larval development related to habitat and climate. For more information, see Larval habitat. Parameters for the vector life cycle more broadly are described in Vector life cycle.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Aquatic_Arrhenius_1

float

0

1.00E+15

8.42E+10

The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the daily rate of fractional progression of mosquito aquatic development (egg-hatching through emergence). This duration is a decreasing function of temperature. The variable a1 is a temperature-independent scale factor on development rate.

 
{
    "Vector_Species_Params": [
        {
            "Name": "aegypti",
            "Aquatic_Arrhenius_1": 9752291.727
        }
    ]
}

Aquatic_Arrhenius_2

float

0

1.00E+15

8328

The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the daily rate of fractional progression of mosquito aquatic development (egg-hatching through emergence). This duration is a decreasing function of temperature. The variable a2 governs how quickly the rate changes with temperature.

 
{
    "Vector_Species_Params": [
        {
            "Name": "aegypti",
            "Aquatic_Arrhenius_2": 8328
        }
    ]
}

Aquatic_Mortality_Rate

float

0

1

0.1

The base aquatic mortality per day for the species before adjusting for effects of overpopulation and drying out of aquatic habitat.

 
{
    "Vector_Species_Params": [
        {
            "Name": "aegypti",
            "Aquatic_Mortality_Rate": 0.1
        }
    ]
}

Drought_Egg_Hatch_Delay

float

0

1

0.33

Proportion of regular egg hatching that still occurs when habitat dries up. Enable_Drought_Egg_Hatch_Delay must be set to 1.

 
{
    "Enable_Drought_Egg_Hatch_Delay": 1,
    "Drought_Egg_Hatch_Delay": 0.33
}

Egg_Arrhenius1

float

0

1.00E+10

6.16E+07

The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the daily rate of mosquito egg hatching. This duration is a decreasing function of temperature. The variable a1 is a temperature-independent scale factor on hatching rate. Enable_Temperature_Dependent_Egg_Hatching must be set to 1.

 
{
    "Enable_Temperature_Dependent_Egg_Hatching": 1,
    "Egg_Arrhenius1": 61599956,
    "Egg_Arrhenius2": 5754
}

Egg_Arrhenius2

float

0

1.00E+10

5754.03

The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the daily rate of mosquito egg hatching. This duration is a decreasing function of temperature. The variable a2 is a temperature-dependent scale factor on hatching rate. Enable_Temperature_Dependent_Egg_Hatching must be set to 1.

 
{
    "Enable_Temperature_Dependent_Egg_Hatching": 1,
    "Egg_Arrhenius1": 61599956,
    "Egg_Arrhenius2": 5754
}

Egg_Hatch_Density_Dependence

enum

NA

NA

NO_DENSITY_DEPENDENCE

The effect of larval density on egg hatching rate. Possible values are:

DENSITY_DEPENDENCE

Egg hatching is reduced when the habitat is nearing its carrying capacity.

NO_DENSITY_DEPENDENCE

Egg hatching is not dependent upon larval density.

 
{
    "Egg_Hatch_Density_Dependence": "NO_DENSITY_DEPENDENCE"
}

Egg_Saturation_At_Oviposition

enum

NA

NA

NO_SATURATION

If laying all eggs from ovipositing females would overflow the larval habitat capacity on a given day, the means by which the viable eggs become saturated. Possible values are:

NO_SATURATION

Egg number does not saturate; all eggs are laid.

SATURATION_AT_OVIPOSITION

Eggs are saturated at oviposition; habitat is filled to capacity and excess eggs are discarded.

SIGMOIDAL_SATURATION

Eggs are saturated along a sigmoidal curve; proportionately fewer eggs are laid depending on how oversubscribed the habitat would be.

 
{
    "Egg_Saturation_At_Oviposition": "SATURATION_AT_OVIPOSITION"
}

Enable_Drought_Egg_Hatch_Delay

boolean

0

1

0

Controls whether or not eggs hatch at delayed rates, set by Drought_Egg_Hatch_Delay, when the habitat dries up completely.

 
{
    "Enable_Drought_Egg_Hatch_Delay": 1,
    "Drought_Egg_Hatch_Delay": 0.33
}

Enable_Egg_Mortality

boolean

0

1

0

Controls whether or not to include a daily mortality rate on the egg population, which is independent of climatic factors.

 
{
    "Enable_Egg_Mortality": 1
}

Enable_Temperature_Dependent_Egg_Hatching

boolean

0

1

0

Controls whether or not temperature has an effect on egg hatching, defined by Egg_Arrhenius_1 and Egg_Arrhenius_2.

 
{
    "Enable_Temperature_Dependent_Egg_Hatching": 1,
    "Egg_Arrhenius1": 61599956.864,
    "Egg_Arrhenius2": 5754.033
}

Larval_Density_Dependence

enum

NA

NA

UNIFORM_WHEN_OVERPOPULATION

The functional form of mortality and growth delay for mosquito larvae based on population density. Possible values are:

UNIFORM_WHEN_OVERPOPULATION

Mortality is uniformly applied to all larvae when the population exceeds the specified carrying capacity for that habitat.

GRADUAL_INSTAR_SPECIFIC

Mortality and delayed growth are instar-specific, where the younger larvae are more susceptible to predation and competition from older larvae.

LARVAL_AGE_DENSITY_DEPENDENT_MORTALITY_ONLY

Mortality is based only on larval age.

DENSITY_DELAYED_GROWTH_NOT_MORTALITY

There is no mortality, only delayed growth in larvae.

NO_DENSITY_DEPENDENCE

There is no additional larval density-dependent mortality factor.

 
{
    "Larval_Density_Dependence": "GRADUAL_INSTAR_SPECIFIC"
}

Habitats

array of JSON objects

NA

NA

NA

A measure of the habitat type and scale factors used to estimate larval population. This parameter is an array of JSON objects where each object specifies scale factor for a given habitat type or, for LINEAR_SPLINE, with a more detailed configuration for scaling. The numeric scaling value represents larval density with the number of larvae in a 1x1-degree area. The factor multiplicatively scales the resulting weather or population-dependent functional form. See Larval habitat for more information. Possible habitat values are:

CONSTANT

Larval carrying capacity is constant throughout the year and does not depend on weather. However, mosquito abundance will exhibit a seasonal signal due to the effects of temperature on aquatic developmental rates.

TEMPORARY_RAINFALL

This habitat type corresponds to temporary puddles which are replenished by rainfall and drained through evaporation and infiltration. Habitat availability is proportional to temperature and humidity, and decays over time as configured by the parameter Temporary_Habitat_Decay_Factor.

WATER_VEGATATION

This habitat type corresponds to semi-permanent habitats, such as developing vegetation on the edges of small water sources such as swamps or rice cultivation settings. Development of habitat lags behind rainfall, and typically peaks near the end of the rainy season. Habitat decays daily, as specified by the parameter Semipermanent_Habitat_Decay_Rate.

HUMAN_POPULATION

This habitat type scales with correlates of human development, such as available water in pots in urban areas. It is configured by multiplying the number of people in a node’s population times the capacity value set for this parameter, and is not climate-dependent.

BRACKISH_SWAMP

This habitat type deals with the dynamics of how rain fills brackish swamps, how this habitat availability decays, and includes a rainfall-driven mortality threshold. Habitat decay rate is specified by the parameter Semipermanent_Habitat_Decay_Rate.

LINEAR_SPLINE

The LINEAR_SPLINE configuration specifies the day of year, larval value, and larval capacity scaling number. The model linearly interpolates the values to estimate the habitat availability for each vector species without requiring climatological data.

ALL_HABITATS

Scale equally across all habitat types.

The values set here can be scaled per node using LarvalHabitatMultiplier in the demographics file (see NodeAttributes) or per intervention using Larval_Habitat_Multiplier in the campaign file (see ScaleLarvalHabitat).

The following example shows how to specify larval habitat using LINEAR_SPLINE.

{
    "Vector_Species_Params": [
        {
            "Name": "funestus",
            "Habitats": [
                {
                    "Habitat_Type": "LINEAR_SPLINE",
                    "Capacity_Distribution_Number_Of_Years": 2,
                    "Capacity_Distribution_Over_Time": {
                        "Times": [0.0,  30.417,  60.833,  91.25, 121.667, 152.083, 182.5, 212.917, 243.333, 273.75, 304.167, 334.583,
                            365.0, 395.417, 425.833, 456.25, 486.667, 517.083, 547.5, 577.917, 608.333, 638.75, 669.167, 699.583
                        ],
                        "Values": [0.0, 0.0, 0.0, 0.0, 0.2, 1.0, 1.0, 1.0, 0.5, 0.2, 0.0, 0.0,
                            0.0, 0.0, 0.0, 0.0, 0.2, 1.0, 1.0, 1.0, 0.5, 0.2, 0.0, 0.0
                        ]
                     },
                     "Max_Larval_Capacity": 10000000000.0
                }
            ]
        }
    ]
}

The following example shows how to specify the larval habitat using climatological habitat types (you may use one or more for each vector species).

{
    "Vector_Species_Params": [
        {
            "Name" : "arabiensis",
            "Habitats": [
                {
                    "Habitat_Type" : "BRACKISH_SWAMP",
                    "Max_Larval_Capacity": 30000000.0
                },
                {
                    "Habitat_Type" : "TEMPORARY_RAINFALL",
                    "Max_Larval_Capacity": 11250000000.0
                },
                {
                    "Habitat_Type" : "WATER_VEGETATION",
                    "Max_Larval_Capacity": 6000000000.0
                }
            ]
        }
    ]
}

Larval_Rainfall_Mortality_Threshold

float

0.01

1000

100

The threshold value on daily rainfall in millimeters, above which larval mortality is applied when Vector_Larval_Rainfall_Mortality is set to either SIGMOID or SIGMOID_HABITAT_SHIFTING.

 
{
    "Larval_Rainfall_Mortality_Threshold": 30.0
}

Rainfall_In_mm_To_Fill_Swamp

float

1

10000

1000

Millimeters of rain to fill larval habitat to capacity. This is only used for vector species with Habitats set to BRACKISH_SWAMP.

 
{
    "Rainfall_In_mm_To_Fill_Swamp": 1000.0
}

Semipermanent_Habitat_Decay_Rate

float

0.0001

100

0.01

Daily rate of larval habitat loss for semi-permanent habitats with Habitats parameter value of WATER_VEGETATION or BRACKISH_SWAMP.

 
{
    "Semipermanent_Habitat_Decay_Rate": 0.01
}

Temporary_Habitat_Decay_Factor

float

0.001

100

0.05

The factor to convert raw evaporation rate (ignoring boundary layer effects) to the daily rate of larval habitat loss for temporary habitats (Habitats set to TEMPORARY_RAINFALL). Units are (larval carrying capacity per day) / (kg per square meter per second).

 
{
    "Temporary_Habitat_Decay_Factor": 0.05
}

Vector_Larval_Rainfall_Mortality

enum

NA

NA

NONE

The type of vector larval mortality function due to rainfall. Possible values are:

NONE

No larval mortality due to rainfall.

SIGMOID

The mortality rate grows linearly from 0 at the threshold to 1 at twice the threshold value.

SIGMOID_HABITAT_SHIFTING

The threshold value is reduced by a factor proportional to how full the larval habitat is.

 
{
    "Vector_Larval_Rainfall_Mortality": "SIGMOID"
}

Vector_Migration_Habitat_Modifier

float

0

3.40E+38

0

The preference of a vector to migrate toward a node with more habitat. Only used when Vector_Sampling_Type is set to TRACK_ALL_VECTORS. Enable_Vector_Migration must be set to 1.

 
{
    "Vector_Migration_Habitat_Modifier": 1.0
}

x_Temporary_Larval_Habitat

float

0

10000

1

Scale factor for the habitat size for all mosquito populations.

 
{
    "x_Temporary_Larval_Habitat": 1
}
Migration

The following parameters determine aspects of population migration into and outside of a node, including daily commutes, seasonal migration, and one-way moves. Modes of transport includes travel by foot, automobile, sea, or air. Migration can also be configured to move all individuals in a family at the same time.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Air_Migration_Filename

string

NA

NA

_add-your-air-migration-file_.json

The path to the input file that defines patterns of migration by airplane. Enable_Air_Migration must be set to true (1). See Migration files for information on migration files.

 
{
    "Migration_Model": "FIXED_RATE_MIGRATION",
    "Enable_Air_Migration": 1,
    "Air_Migration_Filename": "../Global_1degree_air_migration.bin"
}

Air_Migration_Roundtrip_Duration

float

0

10000

1

The average time spent (in days) at the destination node during a round-trip migration by airplane. Migration_Pattern must be set to SINGLE_ROUND_TRIPS.

 
{
    "Migration_Model": "FIXED_RATE_MIGRATION",
    "Enable_Air_Migration": 1,
    "Migration_Pattern": "SINGLE_ROUND_TRIPS",
    "Air_Migration_Roundtrip_Duration": 2
}

Air_Migration_Roundtrip_Probability

float

0

1

0.8

The likelihood that an individual who flies to another node will return to the node of origin during the next migration. Enable_Air_Migration must be set to true (1).

 
{
    "Migration_Model": "FIXED_RATE_MIGRATION",
    "Enable_Air_Migration": 1,
    "Air_Migration_Roundtrip_Probability": 0.9
}

Enable_Air_Migration

boolean

0

1

0

Controls whether or not migration by air travel will occur. Migration_Model must be set to FIXED_RATE_MIGRATION.

 
{
    "Migration_Model": "FIXED_RATE_MIGRATION",
    "Enable_Air_Migration": 1,
    "Air_Migration_Filename": "../inputs/air_migration.bin"
}

Enable_Family_Migration

boolean

0

1

0

Controls whether or not all members of a household can migrate together when a MigrateFamily event occurs. All residents must be home before they can leave on the trip. Migration_Model must be set to FIXED_RATE_MIGRATION.

 
{
    "Enable_Migration": "FIXED_RATE_MIGRATION",
    "Enable_Family_Migration": 1,
    "Family_Migration_Filename": "../inputs/family_migration.bin"
}

Enable_Local_Migration

boolean

0

1

0

Controls whether or not local migration (the diffusion of people in and out of nearby nodes by foot travel) occurs. Migration_Model must be set to FIXED_RATE_MIGRATION.

 
{
    "Migration_Model": "FIXED_RATE_MIGRATION",
    "Enable_Local_Migration": 1,
    "Local_Migration_Filename": "../inputs/local_migration.bin"
}

Enable_Migration_Heterogeneity

boolean

0

1

1

Controls whether or not migration rate is heterogeneous among individuals within each group that has a migration rate setting. Set to true (1) to apply a migration rate distribution (see NodeAttributes demographics parameters); set to false (0) to use the same migration rate applied to all individuals in the group. For example, if you are using a migration file that sets different migration rates for each age group in a node, you could apply an Gaussian distribution around a mean value in each age group or you could assign the same value to each individual in each age group.

Migration_Model must be set to FIXED_RATE_MIGRATION.

 
{
    "Migration_Model": "FIXED_RATE_MIGRATION",
    "Enable_Migration_Heterogeneity": 1
}

Enable_Regional_Migration

boolean

0

1

0

Controls whether or not there is migration by road or rail network into and out of nodes in the simulation. Migration_Model must be set to FIXED_RATE_MIGRATION.

 
{
    "Migration_Model": "FIXED_RATE_MIGRATION",
    "Enable_Regional_Migration": 1,
    "Regional_Migration_Filename": "../inputs/regional_migration.bin"
}

Enable_Sea_Migration

boolean

0

1

0

Controls whether or not there is migration on ships into and out of coastal cities with seaports. Migration_Model must be set to FIXED_RATE_MIGRATION.

 
{
    "Migration_Model": "FIXED_RATE_MIGRATION",
    "Enable_Sea_Migration": 1,
    "Sea_Migration_Filename": "../inputs/sea_migration.bin"
}

Enable_Vector_Migration

boolean

0

1

0

Controls whether or not vectors can migrate. Vector_Sampling_Type must be set to TRACK_ALL_VECTORS or SAMPLE_IND_VECTORS. Specific migration types must be enabled or disabled.

 
{
    "Vector_Sampling_Type": "TRACK_ALL_VECTORS",
    "Enable_Vector_Migration": 1,
    "Enable_Vector_Migration_Local": 1,
    "Vector_Migration_Filename_Local": "../inputs/vector_local.bin"
}

Enable_Vector_Migration_Local

boolean

0

1

0

Controls whether or not vectors may migrate to adjacent nodes. Enable_Vector_Migration must be set to true (1).

 
{
    "Vector_Sampling_Type": "TRACK_ALL_VECTORS",
    "Enable_Vector_Migration": 1,
    "Enable_Vector_Migration_Local": 1,
    "Vector_Migration_Filename_Local": "../inputs/vector_local.bin"
}

Enable_Vector_Migration_Regional

boolean

0

1

0

Controls whether or not vectors can migration to non-adjacent nodes. Enable_Vector_Migration must be set to true (1).

 
{
    "Vector_Sampling_Type": "TRACK_ALL_VECTORS",
    "Enable_Vector_Migration": 1,
    "Enable_Vector_Migration_Regional": 1,
    "Vector_Migration_Filename_Regional": "../inputs/vector_regional.bin"
}

Family_Migration_Filename

string

NA

NA

_add-your-family-migration-file_.json

The name of the binary file to use to configure family migration. Enable_Family_Migration must be set to true (1). See Migration files for information on migration files.

 
{
    "Migration_Model": "FIXED_RATE_MIGRATION",
    "Enable_Family_Migration": 1,
    "Family_Migration_Filename": "../inputs/family_migration.bin"
}

Family_Migration_Roundtrip_Duration

float

0

10000

1

The number of days to complete the trip and return to the original node. Migration_Pattern must be set to SINGLE_ROUND_TRIPS.

 
{
    "Migration_Model": "FIXED_RATE_MIGRATION",
    "Migration_Pattern": "SINGLE_ROUND_TRIPS",
    "Enable_Family_Migration": 1,
    "Family_Migration_Roundtrip_Duration": 100
}

Local_Migration_Filename

string

NA

NA

_add-your-local-migration-file_.json

The path of the input file which defines patterns of migration to adjacent nodes by foot travel. See Migration files for information on migration files.

 
{
    "Local_Migration_Filename": "Local_Migration.bin"
}

Local_Migration_Roundtrip_Duration

float

0

10000

1

The average time spent (in days) at the destination node during a round-trip migration by foot travel. Migration_Pattern must be set to SINGLE_ROUND_TRIPS.

 
{
    "Enable_Local_Migration": 1,
    "Migration_Pattern": "SINGLE_ROUND_TRIPS",
    "Local_Migration_Roundtrip_Duration": 1.0
}

Local_Migration_Roundtrip_Probability

float

0

1

0.95

The likelihood that an individual who walks into a neighboring node will return to the node of origin during the next migration. Only used when Enable_Local_Migration is set to true (1).

 
{
    "Local_Migration_Roundtrip_Probability": 1.0
}

Migration_Model

enum

NA

NA

NO_MIGRATION

Model to use for migration. Possible values are:

NO_MIGRATION

Migration into and out of nodes will not occur.

FIXED_RATE_MIGRATION

Migration into and out of nodes will occur at a fixed rate as defined in the migration files. At the beginning of the simulation or whenever an individual has just moved, they pick their next destination and the time and type of the migration. If an individual is on an outbound leg of their journey, they will query the node’s MigrationInfo object and, through probability, pick a new destination; if the individual is inbound, they will travel back to their previous location.

 
{
    "Migration_Model": "FIXED_RATE_MIGRATION",
    "Local_Migration_Filename": "../inputs/local_migration.bin",
    "Enable_Local_Migration": 1
}

Migration_Pattern

enum

NA

NA

RANDOM_WALK_DIFFUSION

Describes the pattern of travel used during migration. Migration_Model must be set to FIXED_RATE_MIGRATION. Possible values are:

RANDOM_WALK_DIFFUSION

Individuals retain no memory of where they came from; every move is to a new destination with no thought of returning home.

SINGLE_ROUND_TRIPS

Individuals have a probability (determined Local_Migration_Roundtrip_Probability, Air_Migration_Roundtrip_Probability, etc.) that they will return to their original location after some duration of time. If they do not, the current location is the new departure point for migration, but their original location remains their home node.

WAYPOINTS_HOME

Individuals go on a multi-step journey along several waypoints and then retrace their steps back along their path once they have reached a maximum number of waypoints from their home node. For example, the path of travel would be 1 -> 2 -> 3 -> 4 -> 3 -> 2 -> 1 if their home is node 1. On the outbound journey, the duration at each waypoint is not set explicitly but is determined by each node’s migration rate; on the return journey, one timestep is spent at each waypoint.

 
{
    "Migration_Model": "FIXED_RATE_MIGRATION",
    "Migration_Pattern": "SINGLE_ROUND_TRIPS",
    "Enable_Local_Migration": 1,
    "Local_Migration_Roundtrip_Probability": 0.05,
    "Local_Migration_Roundtrip_Duration": 10
}

Regional_Migration_Filename

string

NA

NA

_add-your-regional-migration-file_.json

The path of the input file which defines patterns of migration by vehicle via road or rail network. If the node is not on a road or rail network, regional migration focuses on the closest hub city in the network. The file must be in .bin format. See Migration files for information on migration files.

 
{
    "Regional_Migration_Filename": "Regional_Migration.bin"
}

Regional_Migration_Roundtrip_Duration

float

0

10000

1

The average time spent (in days) at the destination node during a round-trip migration by road network. Migration_Pattern must be set to SINGLE_ROUND_TRIPS.

 
{
    "Enable_Regional_Migration": 1,
    "Migration_Pattern": "SINGLE_ROUND_TRIPS",
    "Regional_Migration_Roundtrip_Duration": 1.0
}

Regional_Migration_Roundtrip_Probability

float

0

1

0.1

The likelihood that an individual who travels by vehicle to another node will return to the node of origin during the next migration. Migration_Pattern must be set to SINGLE_ROUND_TRIPS.

 
{
    "Regional_Migration_Roundtrip_Probability": 1.0
}

Roundtrip_Waypoints

integer

0

1000

10

The maximum number of points reached during a trip before steps are retraced on the return trip home. Migration_Pattern must be set to WAYPOINTS_HOME.

 
{
    "Roundtrip_Waypoints": 5
}

Sea_Migration_Filename

string

NA

NA

_add-your-sea-migration-file_.json

The path of the input file which defines patterns of migration by ship. Only used when Enable_Sea_Migration is set to true (1). The file must be in .bin format. See Migration files for information on migration files.

 
{
    "Sea_Migration_Filename": "5x5_Households_Work_Migration.bin"
}

Sea_Migration_Roundtrip_Duration

float

0

10000

1

The average time spent at the destination node during a round-trip migration by ship. Used only when Migration_Pattern must be set to SINGLE_ROUND_TRIPS.

 
{
    "Enable_Sea_Migration": 1,
    "Migration_Pattern": "SINGLE_ROUND_TRIPS",
    "Sea_Migration_Roundtrip_Duration": 10000
}

Sea_Migration_Roundtrip_Probability

float

0

1

0.25

The likelihood that an individual who travels by ship into a neighboring node will return to the node of origin during the next migration. Used only when Enable_Sea_Migration is set to true (1).

 
{
    "Sea_Migration_Roundtrip_Probability": 0
}

Vector_Migration_Filename_Local

string

NA

NA

UNSPECIFIED_FILE

The path to the vector migration file which defines patterns of vector migration to adjacent nodes. Enable_Vector_Migration must be set to 1. The file must be in .bin format.

 
{
    "Vector_Migration_Filename_Local": "5x5_Households_Local_Vector_Migration.bin"
}

Vector_Migration_Filename_Regional

string

NA

NA

UNSPECIFIED_FILE

The path to the vector migration file which defines patterns of vector migration to non-adjacent nodes. Enable_Vector_Migration must be set to 1. The file must be in .bin format.

 
{
    "Vector_Migration_Filename_Regional": "5x5_Households_Regional_Vector_Migration.bin"
}

Vector_Migration_Food_Modifier

float

0

3.40E+38

0

The preference of a vector to migrate toward a node currently occupied by humans, independent of the number of humans in the node. Used only when Vector_Sampling_Type is set to TRACK_ALL_VECTORS. Enable_Vector_Migration must be set to 1.

 
{
    "Vector_Migration_Food_Modifier": 1.0
}

Vector_Migration_Habitat_Modifier

float

0

3.40E+38

0

The preference of a vector to migrate toward a node with more habitat. Only used when Vector_Sampling_Type is set to TRACK_ALL_VECTORS. Enable_Vector_Migration must be set to 1.

 
{
    "Vector_Migration_Habitat_Modifier": 1.0
}

Vector_Migration_Modifier_Equation

enum

NA

NA

LINEAR

The functional form of vector migration modifiers. Enable_Vector_Migration must be set to 1. Possible values are: LINEAR EXPONENTIAL

 
{
    "Vector_Migration_Modifier_Equation": "EXPONENTIAL"
}

Vector_Migration_Stay_Put_Modifier

float

0

3.40E+38

0

The preference of a vector to remain in its current node rather than migrate to another node. Used only when Vector_Sampling_Type is set to TRACK_ALL_VECTORS. Enable_Vector_Migration must be set to 1.

 
{
    "Vector_Migration_Stay_Put_Modifier": 1.0
}

x_Air_Migration

float

0

3.40E+38

1

Scale factor for the rate of migration by air, as provided by the migration file. Enable_Air_Migration must be set to 1.

 
{
    "Scale_Factor_Air_Migration": 1
}

x_Family_Migration

float

0

3.40E+38

1

Scale factor for the rate of migration by families, as provided by the migration file. Enable_Family_Migration must be set to true (1).

 
{
    "x_Family_Migration": 1
}

x_Local_Migration

float

0

3.40E+38

1

Scale factor for rate of migration by foot travel, as provided by the migration file. Enable_Local_Migration must be set to 1.

 
{
    "x_Local_Migration": 1
}

x_Regional_Migration

float

0

3.40E+38

1

Scale factor for the rate of migration by road vehicle, as provided by the migration file. Enable_Regional_Migration must be set to 1.

 
{
    "x_Regional_Migration": 1
}

x_Sea_Migration

float

0

3.40E+38

1

Scale factor for the rate of migration by sea, as provided by the migration file. Enable_Sea_Migration must be set to 1.

 
{
    "x_Sea_Migration": 1
}

x_Vector_Migration_Local

float

0

3.40E+38

1

Scale factor for the rate of vector migration to adjacent nodes, as provided by the vector migration file. Enable_Vector_Migration must be set to 1.

 
{
    "x_Vector_Migration_Local": 1.0
}

x_Vector_Migration_Regional

float

0

3.40E+38

1

Scale factor for the rate of vector migration to non-adjacent nodes, as provided by the vector migration file. Enable_Vector_Migration must be set to 1.

 
{
    "x_Vector_Migration_Regional": 1.0
}
Mortality and survival

The following parameter determine mortality and survival characteristics of the disease being modeled and the population in general (non-disease mortality).

See Severe disease and mortality for more information about how fever, anemia, and parasite counts lead to severe malaria and mortality.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Anemia_Mortality_Inverse_Width

float

0.1

1000000

10

The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function:

probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) )

This parameter configures the inverse width relative to threshold value of severe disease turn-on around threshold for anemia.

 
{
    "Anemia_Mortality_Inverse_Width": 150
}

Anemia_Mortality_Threshold

double

NA

NA

3

The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function:

probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) )

This parameter configures the inverse width relative to threshold value of mortality turn-on around threshold for hemoglobin count in grams per deciliter (g/dL) at which 50% of individuals die per day.

 
{
    "Anemia_Mortality_Threshold": 1.5
}

Anemia_Severe_Inverse_Width

float

0.1

1000000

10

The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function:

probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) )

This parameter configures the inverse width relative to threshold value of severe disease turn-on around threshold for anemia.

 
{
    "Anemia_Severe_Inverse_Width": 20
}

Anemia_Severe_Threshold

float

0

100

5

The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function:

probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) )

This parameter configures the severe disease threshold level for anemia. Threshold units are in grams per deciliter (g/dL).

 
{
    "Anemia_Severe_Threshold": 3.0
}

Death_Rate_Dependence

enum

NA

NA

NOT_INITIALIZED

Determines how likely individuals are to die from natural, non-disease causes. Enable_Natural_Mortality must be set to 1. Possible values are:

NOT_INITIALIZED

The daily mortality rate is 0, and no one dies from non-disease related causes.

NONDISEASE_MORTALITY_BY_AGE_AND_GENDER

The individual’s age and gender are taken into account to determine the daily mortality rate.

NONDISEASE_MORTALITY_BY_YEAR_AND_AGE_FOR_EACH_GENDER

Gender, age, and year, are all taken into account to determine the daily mortality rate.

Properties, rates, and bin sizes can be set for non-disease mortality for each gender in the demographics file (see Complex distributions parameters).

 
{
    "Death_Rate_Dependence": "NONDISEASE_MORTALITY_BY_AGE_AND_GENDER"
}

Enable_Disease_Mortality

boolean

0

1

1

Controls whether or not individuals are removed from the simulation due to disease deaths.

 
{
    "Enable_Disease_Mortality": 1
}

Enable_Natural_Mortality

boolean

0

1

0

Controls whether or not individuals are removed from the simulation due to natural (non-disease) deaths. Enable_Vital_Dynamics must be set to 1 (true). Use Death_Rate_Dependence to set the natural death rate.

 
{
    "Enable_Natural_Mortality": 1,
    "Enable_Vital_Dynamics": 1
}

Fever_Mortality_Inverse_Width

float

0.1

1000000

10

The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function:

probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) )

This parameter configures the inverse width relative to threshold value of mortality turn-on around threshold for fever.

 
{
    "Fever_Mortality_Inverse_Width": 1000
}

Fever_Mortality_Threshold

double

NA

NA

3

The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function:

probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) )

This parameter configures the fever mortality threshold, in units of degrees Celsius above normal body temperature (defined as 37 C).

 
{
    "Fever_Mortality_Threshold": 10
}

Mortality_Blocking_Immunity_Decay_Rate

float

0

1

0.001

The rate at which mortality-blocking immunity decays after the mortality-blocking immunity offset period. Enable_Immune_Decay must be set to 1.

 
{
    "Mortality_Blocking_Immunity_Decay_Rate": 0.1
}

Mortality_Time_Course

enum

NA

NA

DAILY_MORTALITY

The method used to calculate disease deaths. Enable_Disease_Mortality must be set to 1.

Possible values are:

DAILY_MORTALITY

Calculated at every time step.

MORTALITY_AFTER_INFECTIOUS

Calculated once at the end of the disease duration.

 
{
    "Mortality_Time_Course": "MORTALITY_AFTER_INFECTIOUS"
}

x_Other_Mortality

float

0

3.40E+38

1

Scale factor for mortality from causes other than the disease being simulated. Base mortality is provided by the demographics file (see Complex distributions parameters). Enable_Natural_Mortality must be set to 1.

 
{
    "x_Other_Mortality": 1
}
Output settings

The following parameters configure whether or not output reports are created for the simulation, such as reports detailing spatial or demographic data at each time step. By default, the InsetChart is always created.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Custom_Coordinator_Events

array of strings

NA

NA

NA

The list of valid, user-defined events for Event Coordinators that will be included in the campaign. Any event used in the campaign must either be one of the built-in events or in this list.

Note: This configuration parameter is currently in beta release and has not yet been fully tested.

 
{
    "Custom_Coordinator_Events": [
        "Coordinator_Event_1",
        "Coordinator_Event_2",
        "Coordinator_Event_3"
    ]
}

Custom_Individual_Events

array of strings

NA

NA

NA

The list of valid, user-defined events that will be included in the campaign. Any event used in the campaign must either be one of the built-in events or in this list.

Note

This configuration parameter is currently in beta release and has not yet been fully tested.

 
{
    "Custom_Individual_Events": [
        "Individual_Event_1",
        "Individual_Event_2",
        "Individual_Event_3"
    ]
}

Custom_Node_Events

array of strings

NA

NA

NA

The list of valid, user-defined events for nodes that will be included in the campaign. Any event used in the campaign must either be one of the built-in events or in this list.

Note

This configuration parameter is currently in beta release and has not yet been fully tested.

 
{
    "Custom_Node_Events": [
        "Node_Event_1",
        "Node_Event_2",
        "Node_Event_3"
    ]
}

Custom_Reports_Filename

string

NA

NA

UNINITIALIZED STRING

The name of the file containing custom report configuration parameters. Omitting this parameter or setting it to RunAllCustomReports will load all reporters found that are valid for the given simulation type. The file must be in JSON format.

 
{
    "Custom_Reports_Filename": "custom_reports.json"
}

Enable_Default_Reporting

boolean

0

1

1

Controls whether or not the default InsetChart.json report is created.

 
{
    "Enable_Default_Reporting": 1
}

Enable_Demographics_Reporting

boolean

0

1

1

Controls whether or not demographic summary data and age-binned reports are outputted to file.

 
{
    "Enable_Demographics_Reporting": 1
}

Enable_Property_Output

boolean

0

1

0

Controls whether or not to create property output reports, which detail groups as defined in IndividualProperties in the demographics file (see NodeProperties and IndividualProperties parameters). When there is more than one property type, the report will display the channel information for all combinations of the property type groups.

 
{
    "Enable_Property_Output": 1
}

Enable_Spatial_Output

boolean

0

1

0

Controls whether or not spatial output reports are created. If set to true (1), spatial output reports include all channels listed in the parameter Spatial_Output_Channels.

Note

Spatial output files require significant processing time and disk space.

 
{
    "Enable_Spatial_Output": 1,
    "Spatial_Output_Channels": [
        "Prevalence",
        "New_Infections"
    ]
}

Report_Coordinator_Event_Recorder

boolean

0

1

0

Enables or disables the ReportCoordinatorEventRecorder.csv output report for coordinator events. When enabled (set to 1) reports will be generated for the broadcasted valid coordinator events, as specified in Report_Coordinator_Event_Recorder_Events.

Note: This configuration parameter is currently in beta release and has not yet been fully tested.

 
{
    "Custom_Coordinator_Events": [
        "Coordinator_Event_1",
        "Coordinator_Event_2",
        "Coordinator_Event_3"
    ],
    "Report_Coordinator_Event_Recorder": 1,
    "Report_Coordinator_Event_Recorder_Events": [
        "Coordinator_Event_1",
        "Coordinator_Event_2",
        "Coordinator_Event_3"
    ]
}

Report_Coordinator_Event_Recorder_Events

array of strings

NA

NA

NA

The list of events to include or exclude in the ReportCoordinatorEventRecorder.csv output report, based on how Report_Coordinator_Event_Recorder_Ignore_Events_In_List is set. This list must not be empty and is dependent upon Report_Coordinator_Event_Recorder being enabled. In addition, the events must be defined in Customer_Coordinator_Events.

Note: This configuration parameter is currently in beta release and has not yet been fully tested.

 
{
    "Custom_Coordinator_Events": [
        "Coordinator_Event_1",
        "Coordinator_Event_2",
        "Coordinator_Event_3"
    ],
    "Report_Coordinator_Event_Recorder": 1,
    "Report_Coordinator_Event_Recorder_Events": [
        "Coordinator_Event_1",
        "Coordinator_Event_2",
        "Coordinator_Event_3"
    ]
}

Report_Coordinator_Event_Recorder_Ignore_Events_In_List

boolean

0

1

0

If set to false (0), only the events listed in the Report_Coordinator_Event_Recorder_Events array will be included in the ReportCoordinatorEventRecorder.csv output report. If set to true (1), only the events listed in the array will be excluded, and all other events will be included. If you want to return all events from the simulation, leave the events array empty.

Note: This configuration parameter is currently in beta release and has not yet been fully tested.

 
{
    "Custom_Coordinator_Events": [
        "Coordinator_Event_1",
        "Coordinator_Event_2",
        "Coordinator_Event_3"
    ],
    "Report_Coordinator_Event_Recorder": 1,
    "Report_Coordinator_Event_Recorder_Events": [
        "Coordinator_Event_1",
        "Coordinator_Event_2",
        "Coordinator_Event_3"
    ],
    "Report_Coordinator_Event_Recorder_Ignore_Events_In_List": 0
}

Report_Event_Recorder

boolean

0

1

0

Set to true (1) to enable or to false (0) to disable the ReportEventRecorder.csv output report that lists individual events in the simulation. See Event list for a list of all possible built-in events that will be recorded in the output when enabled.

 
{
    "Report_Event_Recorder": 1,
    "Report_Event_Recorder_Events": [
        "VaccinatedA",
        "VaccineExpiredA",
        "VaccinatedB",
        "VaccineExpiredB"
    ],
    "Report_Event_Recorder_Ignore_Events_In_List": 0
}

Report_Event_Recorder_Events

array

NA

NA

The list of events to include or exclude in the ReportEventRecorder.csv output report, based on how Report_Event_Recorder_Ignore_Events_In_List is set. See Event list for a list of all possible built-in events. You can also define events in Custom_Individual_Events. The list cannot be empty.

 
{
    "Report_Event_Recorder": 1,
    "Report_Event_Recorder_Events": [
        "VaccinatedA",
        "VaccineExpiredA",
        "VaccinatedB",
        "VaccineExpiredB"
    ],
    "Report_Event_Recorder_Ignore_Events_In_List": 0
}

Report_Event_Recorder_Ignore_Events_In_List

boolean

0

1

0

If set to false (0), only the events listed in the Report_Event_Recorder_Events array will be included in the ReportEventRecorder.csv output report. If set to true (1), only the events listed in the array will be excluded, and all other events will be included. If you want to return all events from the simulation, leave the events array empty.

Value

Events array

Output file

0

No events

No events

0

One or more events

Only the listed events.

1

No events

All events occurring in the simulation.

1

One or more events

All simulation events occurring in the simulation, except for those listed.

 
{
    "Report_Event_Recorder": 1,
    "Report_Event_Recorder_Events": [
        "VaccinatedA",
        "VaccineExpiredA",
        "VaccinatedB",
        "VaccineExpiredB"
    ],
    "Report_Event_Recorder_Ignore_Events_In_List": 0
}

Report_Event_Recorder_Individual_Properties

array of strings

NA

NA

[]

Specifies an array of (optional) individual property keys, as defined in IndividualProperties in the demographics file, to be added as additional columns to the output report. Individual’s IP value will be added to the (key) column at the time of the event.

 
{
    "Report_Event_Recorder_Individual_Properties": [
        "Accessibility",
        "Risk"
    ]
}

Report_Event_Recorder_Start_Day

float

0

3.40282e+38

0

The day of the simulation to start collecting data.

 
{
    "Report_Event_Recorder": 1,
    "Report_Event_Recorder_Start_Day": 365,
    "Report_Event_Recorder_End_Day": 730,
    "Report_Event_Recorder_Events": [
        "VaccinatedA",
        "VaccineExpiredA",
        "VaccinatedB",
        "VaccineExpiredB"
    ],
    "Report_Event_Recorder_Ignore_Events_In_List": 0
}

Report_Event_Recorder_End_Day

float

0

3.40282e+38

3.40282e+38

The day of the simulation to stop collecting data.

 
{
    "Report_Event_Recorder": 1,
    "Report_Event_Recorder_Start_Day": 365,
    "Report_Event_Recorder_End_Day": 730,
    "Report_Event_Recorder_Events": [
        "VaccinatedA",
        "VaccineExpiredA",
        "VaccinatedB",
        "VaccineExpiredB"
    ],
    "Report_Event_Recorder_Ignore_Events_In_List": 0
}

Report_Event_Recorder_Node_IDs_Of_Interest

array of integers

0

2.14748e+09

[]

Data will be collected for the nodes in this list.

 
{
    "Report_Event_Recorder": 1,
    "Report_Event_Recorder_Start_Day": 365,
    "Report_Event_Recorder_End_Day": 730,
    "Report_Event_Recorder_Node_IDs_Of_Interest": [ 2, 7 ],
    "Report_Event_Recorder_Events": [
        "VaccinatedA",
        "VaccineExpiredA",
        "VaccinatedB",
        "VaccineExpiredB"
    ],
    "Report_Event_Recorder_Ignore_Events_In_List": 0
}

Report_Event_Recorder_Min_Age_Years

float

0

9.3228e+35

0

Minimum age in years of people to collect data on.

 
{
    "Report_Event_Recorder": 1,
    "Report_Event_Recorder_Start_Day": 365,
    "Report_Event_Recorder_End_Day": 730,
    "Report_Event_Recorder_Min_Age_Years": 5,
    "Report_Event_Recorder_Max_Age_Years": 10,
    "Report_Event_Recorder_Events": [
        "VaccinatedA",
        "VaccineExpiredA",
        "VaccinatedB",
        "VaccineExpiredB"
    ],
    "Report_Event_Recorder_Ignore_Events_In_List": 0
}

Report_Event_Recorder_Max_Age_Years

float

0

9.3228e+35

9.3228e+35

Maximum age in years of people to collect data on.

 
{
    "Report_Event_Recorder": 1,
    "Report_Event_Recorder_Start_Day": 365,
    "Report_Event_Recorder_End_Day": 730,
    "Report_Event_Recorder_Min_Age_Years": 5,
    "Report_Event_Recorder_Max_Age_Years": 10,
    "Report_Event_Recorder_Events": [
        "VaccinatedA",
        "VaccineExpiredA",
        "VaccinatedB",
        "VaccineExpiredB"
    ],
    "Report_Event_Recorder_Ignore_Events_In_List": 0
}

Report_Event_Recorder_Must_Have_IP_Key_Value

string

NA

NA

(empty string)

A Key:Value pair that the individual must have in order to be included. Empty string means to not include IPs in the selection criteria.

 
{
    "Report_Event_Recorder": 1,
    "Report_Event_Recorder_Start_Day": 365,
    "Report_Event_Recorder_End_Day": 730,
    "Report_Event_Recorder_Min_Age_Years": 5,
    "Report_Event_Recorder_Max_Age_Years": 10,
    "Report_Event_Recorder_Must_Have_IP_Key_Value": "Risk:LOW",
    "Report_Event_Recorder_Events": [
        "VaccinatedA",
        "VaccineExpiredA",
        "VaccinatedB",
        "VaccineExpiredB"
    ],
    "Report_Event_Recorder_Ignore_Events_In_List": 0
}

Report_Event_Recorder_Must_Have_Intervention

string

NA

NA

(empty string)

The name of the intervention that the person must have in order to be included. Empty string means to not include interventions in the selection criteria.

 
{
    "Report_Event_Recorder": 1,
    "Report_Event_Recorder_Start_Day": 365,
    "Report_Event_Recorder_End_Day": 730,
    "Report_Event_Recorder_Min_Age_Years": 5,
    "Report_Event_Recorder_Max_Age_Years": 10,
    "Report_Event_Recorder_Must_Have_IP_Key_Value": "Risk:LOW",
    "Report_Event_Recorder_Must_Have_Intervention": "UsageDependentBednet",
    "Report_Event_Recorder_Events": [
        "VaccinatedA",
        "VaccineExpiredA",
        "VaccinatedB",
        "VaccineExpiredB"
    ],
    "Report_Event_Recorder_Ignore_Events_In_List": 0
}

Report_Node_Event_Recorder

boolean

0

1

0

Enables or disables the ReportNodeEventRecorder.csv output report. When enabled (set to 1) reports will be generated for the broadcasted valid node events, as specified in Report_Node_Event_Recorder_Events.

Note: This configuration parameter is currently in beta release and has not yet been fully tested.

 
{
    "Custom_Node_Events": [
        "Node_Event_1",
        "Node_Event_2"
    ],
    "Report_Node_Event_Recorder": 1,
    "Report_Node_Event_Recorder_Events": [
        "Node_Event_1",
        "Node_Event_2"
    ]
}

Report_Node_Event_Recorder_Events

array of strings

NA

NA

NA

The list of node events to include or exclude in the ReportNodeEventRecorder.csv output report, based on how Report_Node_Event_Recorder_Ignore_Events_In_List is set.

Note: This configuration parameter is currently in beta release and has not yet been fully tested.

 
{
    "Custom_Node_Events": [
        "Node_Event_1",
        "Node_Event_2"
    ],
    "Report_Node_Event_Recorder": 1,
    "Report_Node_Event_Recorder_Events": [
        "Node_Event_1",
        "Node_Event_2"
    ],
    "Report_Node_Event_Recorder_Ignore_Events_In_List": 0
}

Report_Node_Event_Recorder_Ignore_Events_In_List

boolean

0

1

0

If set to false (0), only the node events listed in the Report_Node_Event_Recorder_Events array will be included in the ReportNodeEventRecorder.csv output report. If set to true (1), only the node events listed in the array will be excluded, and all other node events will be included. If you want to return all node events from the simulation, leave the node events array empty.

Note: This configuration parameter is currently in beta release and has not yet been fully tested.

Value

Events array

Output file

0

No events

No events

0

One or more events

Only the listed events.

1

No events

All events occurring in the simulation.

1

One or more events

All simulation events occurring in the simulation, except for those listed.

 
{
    "Custom_Node_Events": [
        "Node_Event_1",
        "Node_Event_2"
    ],
    "Report_Node_Event_Recorder": 1,
    "Report_Node_Event_Recorder_Events": [
        "Node_Event_1",
        "Node_Event_2"
    ],
    "Report_Node_Event_Recorder_Ignore_Events_In_List": 0
}

Report_Node_Event_Recorder_Node_Properties

array of strings

NA

NA

[]

Specifies an array of (optional) node property keys, as defined in NodeProperties in the demographics file, to be added as additional columns to the ReportNodeEventRecorder.csv output report.

 
{
    "Custom_Node_Events": [
        "Node_Event_1",
        "Node_Event_2"
    ],
    "Report_Node_Event_Recorder": 1,
    "Report_Node_Event_Recorder_Events": [
        "Node_Event_1",
        "Node_Event_2"
    ],
    "Report_Node_Event_Recorder_Ignore_Events_In_List": 0,
    "Report_Node_Event_Recorder_Node_Properties": [
        "Geographic"
    ]
}

Report_Node_Event_Recorder_Stats_By_IPs

array of strings

NA

NA

[]

Specifies an array of (optional) individual property keys, as defined in IndividualProperties in the demographics file, to be added to the ReportNodeEventRecorder.csv output report. For each key:value pair there will be two additional columns (Key:Value:NumIndividuals, Key:Value:NumInfected) added to the report. For example, with a Risk property key assigned the values of LOW and HIGH there would then be four additional columns (Risk:LOW:NumIndividuals, Risk:LOW:NumInfected, Risk:HIGH:NumIndividuals, Risk:HIGH:NumInfected). An empty array equals no additional columns added.

 
{
    "Custom_Node_Events": [
        "Node_Event_1",
        "Node_Event_2"
    ],
    "Report_Node_Event_Recorder": 1,
    "Report_Node_Event_Recorder_Events": [
        "Node_Event_1",
        "Node_Event_2"
    ],
    "Report_Node_Event_Recorder_Ignore_Events_In_List": 0,
    "Report_Node_Event_Recorder_Node_Properties": [
        "Geographic"
    ],
    "Report_Node_Event_Recorder_Stats_By_IPs": [
        "Risk"
    ]
}

Report_Surveillance_Event_Recorder

boolean

0

1

0

Enables or disables the ReportSurveillanceEventRecorder.csv output report. When enabled (set to 1) reports will be generated for the broadcasted valid coordinator events, as specified in Report_Surveillance_Event_Recorder_Events.

Note: This configuration parameter is currently in beta release and has not yet been fully tested.

 
{
    "Custom_Coordinator_Events": [
        "Start_ACF",
        "Start_SIA_2",
        "Start_SIA_4",
        "Ind_Start_SIA_2",
        "Ind_Start_SIA_4",
        "Respond_To_Surveillance"
    ],
    "Report_Surveillance_Event_Recorder": 1,
    "Report_Surveillance_Event_Recorder_Events": [
        "Respond_To_Surveillance"
    ]
}

Report_Surveillance_Event_Recorder_Events

array of strings

NA

NA

NA

The list of events to include or exclude in the ReportSurveillanceEventRecorder.csv output report, based on how Report_Surveillance_Event_Recorder_Ignore_Events_In_List is set. This list must not be empty and is dependent upon Report_Surveillance_Event_Recorder being enabled.

Note: This configuration parameter is currently in beta release and has not yet been fully tested.

 
{
    "Custom_Coordinator_Events": [
        "Start_ACF",
        "Start_SIA_2",
        "Start_SIA_4",
        "Ind_Start_SIA_2",
        "Ind_Start_SIA_4",
        "Respond_To_Surveillance"
    ],
    "Report_Surveillance_Event_Recorder": 1,
    "Report_Surveillance_Event_Recorder_Events": [
        "Respond_To_Surveillance"
    ]
}

Report_Surveillance_Event_Recorder_Ignore_Events_In_List

boolean

0

1

0

If set to false (0), only the events listed in the Report_Surveillance_Event_Recorder_Events array will be included in the ReportSurveillanceEventRecorder.csv output report. If set to true (1), only the events listed in the array will be excluded, and all other events will be included. If you want to return all events from the simulation, leave the events array empty.

Note: This configuration parameter is currently in beta release and has not yet been fully tested.

Value

Events array

Output file

0

No events

No events

0

One or more events

Only the listed events.

1

No events

All events occurring in the simulation.

1

One or more events

All simulation events occurring in the simulation, except for those listed.

 
{
    "Custom_Coordinator_Events": [
        "Start_ACF",
        "Start_SIA_2",
        "Start_SIA_4",
        "Ind_Start_SIA_2",
        "Ind_Start_SIA_4",
        "Respond_To_Surveillance"
    ],
    "Report_Surveillance_Event_Recorder": 1,
    "Report_Surveillance_Event_Recorder_Events": [
        "Respond_To_Surveillance"
    ],
    "Report_Surveillance_Event_Recorder_Ignore_Events_In_List": 0
}

Report_Surveillance_Event_Recorder_Stats_By_IPs

array of strings

NA

NA

[]

Specifies an array of (optional) individual property keys, as defined in IndividualProperties in the demographics file, to be added to the ReportSurveillanceEventRecorder.csv output report. For each key:value pair there will be two additional columns (Key:Value:NumIndividuals, Key:Value:NumInfected) added to the report. For example, with a Risk property key assigned the values of LOW and HIGH there would then be four additional columns (Risk:LOW:NumIndividuals, Risk:LOW:NumInfected, Risk:HIGH:NumIndividuals, Risk:HIGH:NumInfected). An empty array equals no additional columns added.

 
{
    "Custom_Coordinator_Events": [
        "Start_ACF",
        "Start_SIA_2",
        "Start_SIA_4",
        "Ind_Start_SIA_2",
        "Ind_Start_SIA_4",
        "Respond_To_Surveillance"
    ],
    "Report_Surveillance_Event_Recorder": 1,
    "Report_Surveillance_Event_Recorder_Events": [
        "Respond_To_Surveillance"
    ],
    "Report_Surveillance_Event_Recorder_Stats_By_IPs": []
}

Spatial_Output_Channels

array of strings

NA

NA

[]

An array of channel names for spatial output by node and time step. The data from each channel will be written to a separate binary file. Enable_Spatial_Output must be set to true (1). Possible values are:

Air_Temperature

Data related to air temperature.

Births

Data related to the number of births.

Campaign_Cost

Data related to the costs of a campaign.

Disease_Deaths

Data related to the number of deaths due to disease.

Human_Infectious_Reservoir

Data related to the total infectiousness of the population.

Infection_Rate

Data related to infection rates.

Land_Temperature

Data related to the average land temperature over all nodes.

New_Infections

Data related to the presence of new infections.

New_Reported_Infections

Data related to the presence of reported new infections.

Population

Data related to the total population in the simulation.

Prevalence

Data related to the fraction of the population that is infected.

Rainfall

Data related to the presence of rainfall.

Relative_Humidity

Data related to the presence of relative humidity.

Fever_Prevalence

Data related to the presence of fever.

Mean_Parasitemia

Data related to the presence of mean parasitemia.

New_Clinical_Cases

Data related to the presence of new clinical cases.

New_Diagnostic_Prevalence

Data related to the presence of a new diagnostic.

New_Severe_Cases

Data related to the presence of new severe cases.

Parasite_Prevalence

Data related to the presence of parasites.

Adult_Vectors

Data related to the presence of adult vectors.

Daily_EIR

Data related to entomological inoculation rate (EIR) for mosquitoes.

 
{
    "Spatial_Output_Channels": [
        "Prevalence",
        "New_Infections"
    ]
}
Parasite dynamics

The following parameters determine the dynamics of the Plasmodium falciparum parasite life cycle, including dynamics within the host and human population. For more information, see Malaria infection and immune model.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Base_Gametocyte_Fraction_Male

double

NA

NA

0.2

Fraction of gametocytes that are male.

 
{
    "Base_Gametocyte_Fraction_Male": 0.25
}

Base_Gametocyte_Mosquito_Survival_Rate

float

0

1

0.01

Average fraction of gametocytes in a blood meal that are successful in infecting the mosquito in the absence of other modulating effects, such as fever.

 
{
    "Base_Gametocyte_Mosquito_Survival_Rate": 0.015
}

Base_Gametocyte_Production_Rate

double

NA

NA

0.02

The fraction of infected red blood cells (IRBCs) producing gametocytes.

 
{
    "Base_Gametocyte_Mosquito_Survival_Rate": 0.01
}

Base_Sporozoite_Survival_Fraction

float

0

1

0.25

The fraction of sporozoites that survive to infect a hepatocyte in the absence of anti-CSP protection.

 
{
    "Base_Sporozoite_Survival_Fraction": 0.25
}

Cytokine_Gametocyte_Inactivation

float

0

1

0.02

The strength of inflammatory response inactivation of gametocytes.

 
{
    "Cytokine_Gametocyte_Inactivation": 0.0167
}

Gametocyte_Stage_Survival_Rate

double

0

1

1

The rate of gametocyte survival from one development stage to the next in the absence of drugs or inflammatory immune response.

 
{
    "Gametocyte_Stage_Survival_Rate": 0.8
}

Mean_Sporozoites_Per_Bite

float

0

1000

11

The mean number of sporozoites per infectious mosquito bite.

 
{
    "Mean_Sporozoites_Per_Bite": 11
}

Merozoites_Per_Hepatocyte

double

NA

NA

15000

The number of IRBCs caused by a single infected hepatocyte at the start of infection.

 
{
    "Merozoites_Per_Hepatocyte": 15000
}

Merozoites_Per_Schizont

double

NA

NA

16

The number of merozoites released by a single infected schizont after each asexual cycle. The number of resulting IRBC’s depends on the RBC availability and merozoite-specific immunity.

 
{
    "Merozoites_Per_Schizont": 16
}

Number_Of_Asexual_Cycles_Without_Gametocytes

integer

0

500

1

The number of asexual reproduction cycles that do not produce gametocytes. All later cycles will produce gametocytes according to Base_Gametocyte_Production_Rate.

 
{
    "Number_Of_Asexual_Cycles_Without_Gametocytes": 1
}

Parasite_Mortality_Inverse_Width

float

0.1

1000000

10

The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function:

probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) ).

This parameter configures the inverse width relative to threshold value of mortality turn-on around threshold for parasite density.

 
{
    "Parasite_Mortality_Inverse_Width": 100
}

Parasite_Mortality_Threshold

double

NA

NA

2000000

The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function:

probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) )

This parameter configures the parasite-density mortality threshold for a simulation.

 
{
    "Parasite_Mortality_Threshold": 3000000
}

Parasite_Severe_Inverse_Width

float

0.1

1000000

10

The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function:

probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) )

This parameter configures the inverse width relative to threshold value of severe disease turn-on around threshold for parasite density.

 
{
    "Parasite_Severe_Inverse_Width": 48.66825295
}

Parasite_Severe_Threshold

float

0

3.40E+38

1000000

The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function:

probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) )

This parameter configures parasite density threshold level that results in severe disease.

 
{
    "Parasite_Severe_Threshold": 253871
}

Parasite_Smear_Sensitivity

float

0.0001

100

0.1

The number of microliters of blood tested to find single parasites in a traditional smear (corresponds to inverse parasites/microliters sensitivity).

 
{
    "Parasite_Smear_Sensitivity": 0.1
}

Parasite_Switch_Type

enum

NA

NA

CONSTANT_SWITCH_RATE_2VARS

The parasite switch type for erythrocyte surface antigens. Possible values are:

CONSTANT_SWITCH_RATE_2VARS

Introduction of new variants has a fixed probability which does not depend on the level of parasitemia.

RATE_PER_PARASITE_7VARS

Each IRBC has a fixed rate of switching to each of seven possible variants.

RATE_PER_PARASITE_5VARS_DECAYING

Each IRBC has a rate of switching to several possible variants, and the rate decreases with distance from the original variant.

 
{
    "Parasite_Switch_Type": "RATE_PER_PARASITE_7VARS"
}
Population dynamics

The following parameters determine characteristics related to population dynamics, such as age distribution, births, deaths, and gender. The values set here generally interact closely with values in the demographics file.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Age_Initialization_Distribution_Type

enum

NA

NA

DISTRIBUTION_OFF

The method for initializing the age distribution in the simulated population. Possible values are:

DISTRIBUTION_OFF

All individuals default to age 20 years old.

DISTRIBUTION_SIMPLE

Individual ages are drawn from a distribution whose functional form is specified in the demographics file using the IndividualAttributes simple Age distribution parameters.

DISTRIBUTION_COMPLEX

Individual ages are drawn from a piecewise linear function specified in the demographics file complex distribution parameters.

 
{
    "Age_Initialization_Distribution_Type": "DISTRIBUTION_SIMPLE"
}

Birth_Rate_Boxcar_Forcing_Amplitude

float

0

3.40E+38

0

Fractional increase in birth rate during high birth season when Birth_Rate_Time_Dependence is set to ANNUAL_BOXCAR_FUNCTION.

 
{
    "Enable_Vital_Dynamics": 1,
    "Enable_Birth": 1,
    "Birth_Rate_Time_Dependence": "ANNUAL_BOXCAR_FUNCTION",
    "Birth_Rate_Boxcar_Forcing_Amplitude": 0.1
}

Birth_Rate_Boxcar_Forcing_End_Time

float

0

365

0

Day of the year when the high birth rate season ends when Birth_Rate_Time_Dependence is set to ANNUAL_BOXCAR_FUNCTION.

 
{
    "Enable_Vital_Dynamics": 1,
    "Enable_Birth": 1,
    "Birth_Rate_Time_Dependence": "ANNUAL_BOXCAR_FUNCTION",
    "Birth_Rate_Boxcar_Forcing_End_Time": 220
}

Birth_Rate_Boxcar_Forcing_Start_Time

float

0

365

0

Day of the year when the high birth rate season begins when Birth_Rate_Time_Dependence is set to ANNUAL_BOXCAR_FUNCTION.

 
{
    "Enable_Vital_Dynamics": 1,
    "Enable_Birth": 1,
    "Birth_Rate_Time_Dependence": "ANNUAL_BOXCAR_FUNCTION",
    "Birth_Rate_Boxcar_Forcing_Start_Time": 130
}

Birth_Rate_Dependence

enum

NA

NA

FIXED_BIRTH_RATE

The method used to modify the value set in BirthRate in the demographics file (see NodeAttributes parameters). Possible values are:

NONE

Births are not allowed during the simulation, even if Enable_Birth is set to true (1).

FIXED_BIRTH_RATE

The absolute rate at which new individuals are born, as set by BirthRate.

POPULATION_DEP_RATE

Scales the node population to determine the birth rate. If BirthRate is greater than 0.005, a value of 2% per year (0.02/365) is used instead.

DEMOGRAPHIC_DEP_RATE

Scales the female population within fertility age ranges (15-44 years) to determine the birth rate. If BirthRate is greater than 0.005, a value of 1 child every 8 years of fertility [1/8/365(~0.000342)] is used instead.

INDIVIDUAL_PREGNANCIES

Scales the female population within fertility age ranges (15-44 years) to determine the birth rate, but pregnancies are assigned on an individual basis and result in a 40-week pregnancy for a specific individual with a birth at the end.

INDIVIDUAL_PREGNANCIES_BY_AGE_AND_YEAR

Similar to INDIVIDUAL_PREGNANCIES, but determines the rate based on the FertilityDistribution (in IndividualAttributes), using the individual’s age and the year of the simulation.

 
{
    "Enable_Vital_Dynamics": 1,
    "Enable_Birth": 1,
    "Birth_Rate_Dependence": "POPULATION_DEP_RATE"
}

Birth_Rate_Sinusoidal_Forcing_Amplitude

float

0

1

0

The amplitude of sinusoidal variations in birth rate when Birth_Rate_Time_Dependence is set to SINUSOIDAL_FUNCTION_OF_TIME.

 
{
    "Enable_Vital_Dynamics": 1,
    "Enable_Birth": 1,
    "Birth_Rate_Time_Dependence": "SINUSOIDAL_FUNCTION_OF_TIME",
    "Birth_Rate_Sinusoidal_Forcing_Amplitude": 0.1
}

Birth_Rate_Sinusoidal_Forcing_Phase

float

0

365

0

The phase of sinusoidal variations in birth rate. Birth_Rate_Time_Dependence must be set to SINUSOIDAL_FUNCTION_OF_TIME.

 
{
    "Birth_Rate_Sinusoidal_Forcing_Phase": 20
}

Birth_Rate_Time_Dependence

enum

NA

NA

NONE

A scale factor for BirthRate that allows it to be altered by time or season. Enable_Birth must be set to true (1). Possible values are:

NONE

Birth rate does not vary by time.

SINUSOIDAL_FUNCTION_OF_TIME

Allows birth rate to be time-dependent, following a sinusoidal shape defined by \(1 + \sin \text{Birth\_Rate\_Sinusoidal\_Forcing\_Amplitude} \times \sin(2 \pi \times (\text{day} - \sin \text{Birth\_Rate\_Sinusoidal\_Forcing\_Phase})/365)\). Set Birth_Rate_Sinusoidal_Forcing_Amplitude and Birth_Rate_Sinusoidal_Forcing_Phase.

ANNUAL_BOXCAR_FUNCTION

Allows birth rate to follow a boxcar function. The scale factor will be equal to 1 except for a single interval in which it is equal to a given constant equal to 1 + Birth_Rate_Boxcar_Forcing_Amplitude. Set Birth_Rate_Boxcar_Forcing_Amplitude, Birth_Rate_Boxcar_Forcing_End_Time, and Birth_Rate_Boxcar_Forcing_Start_Time.

 
{
    "Enable_Vital_Dynamics": 1,
    "Enable_Birth": 1,
    "Birth_Rate_Time_Dependence": "ANNUAL_BOXCAR_FUNCTION"
}

Death_Rate_Dependence

enum

NA

NA

NOT_INITIALIZED

Determines how likely individuals are to die from natural, non-disease causes. Enable_Natural_Mortality must be set to 1. Possible values are:

NOT_INITIALIZED

The daily mortality rate is 0, and no one dies from non-disease related causes.

NONDISEASE_MORTALITY_BY_AGE_AND_GENDER

The individual’s age and gender are taken into account to determine the daily mortality rate.

NONDISEASE_MORTALITY_BY_YEAR_AND_AGE_FOR_EACH_GENDER

Gender, age, and year, are all taken into account to determine the daily mortality rate.

Properties, rates, and bin sizes can be set for non-disease mortality for each gender in the demographics file (see Complex distributions parameters).

 
{
    "Death_Rate_Dependence": "NONDISEASE_MORTALITY_BY_AGE_AND_GENDER"
}

Default_Geography_Initial_Node_Population

integer

0

1000000

1000

When using the built-in demographics for default geography, the initial number of individuals in each node. Note that the built-in demographics feature does not represent a real geographical location and is mostly used for testing. Enable_Demographics_Builtin must be set to true (1).

 
{
    "Enable_Demographics_Builtin": 1,
    "Default_Geography_Initial_Node_Population": 1000,
    "Default_Geography_Torus_Size": 3
}

Demographics_Filenames

array of strings

NA

NA

An array of the paths to demographics files containing information on the identity and demographics of the region to simulate. The files must be in .json format. Note that this parameter is only required when Enable_Demographics_Builtin is set to 0.

 
{
    "Demographics_Filenames": [
        "Namawala_single_node_demographics.json",
        "Namawala_demographics_overlay.json"
    ]
}

Enable_Aging

boolean

0

1

1

Controls whether or not individuals in a population age during the simulation. Enable_Vital_Dynamics must be set to true (1).

 
{
    "Enable_Vital_Dynamics": 1,
    "Enable_Aging": 1
}

Enable_Birth

boolean

0

1

1

Controls whether or not individuals will be added to the simulation by birth. Enable_Vital_Dynamics must be set to true (1). If you want new individuals to have the same intervention coverage as existing individuals, you must add a BirthTriggeredIV to the campaign file.

 
{
    "Enable_Vital_Dynamics": 1,
    "Enable_Birth": 1
}

Enable_Demographics_Birth

boolean

0

1

0

Controls whether or not newborns have disease risk drawn from a distribution; uniform disease risk if false. Enable_Birth, Enable_Demographics_Risk, and Enable_Vital_Dynamics must be set to true (1).

 
{
    "Enable_Birth": 1,
    "Enable_Demographics_Birth": 1,
    "Enable_Vital_Dynamics": 1
}

Enable_Demographics_Builtin

boolean

0

1

0

Controls whether or not built-in demographics for default geography will be used. Note that the built-in demographics feature does not represent a real geographical location and is mostly used for testing. Set to true (1) to define the initial population and number of nodes using Default_Geography_Initial_Node_Population and Default_Geography_Torus_Size. Set to false (0) to use demographics input files defined in Demographics_Filenames.

 
{
    "Enable_Demographics_Builtin": 1,
    "Default_Geography_Initial_Node_Population": 1000,
    "Default_Geography_Torus_Size": 3
}

Enable_Demographics_Risk

boolean

0

1

0

Controls whether or not the simulation includes the impact of disease risk in demographics.

 
{
    "Enable_Demographics_Risk": 1
}

Enable_Vital_Dynamics

boolean

0

1

1

Controls whether or not births and deaths occur in the simulation. Births and deaths must be individually enabled and set.

 
{
    "Enable_Vital_Dynamics": 1,
    "Enable_Birth": 1,
    "Death_Rate_Dependence": "NOT_INITIALIZED",
    "Base_Mortality": 0.002
}

Minimum_Adult_Age_Years

float

0

3.40E+38

15

The age, in years, after which an individual is considered an adult. Individual_Sampling_Type must be set to ADAPTED_SAMPLING_BY_AGE_GROUP.

 
{
    "Minimum_Adult_Age_Years": 17
}

Population_Density_Infectivity_Correction

enum

NA

NA

CONSTANT_INFECTIVITY

Correction to alter infectivity by population density set in the Population_Density_C50 parameter. Measured in people per square kilometer. Possible values are:

  • CONSTANT_INFECTIVITY

  • SATURATING_FUNCTION_OF_DENSITY

Note

Sparsely populated areas have a lower infectivity, while densely populated areas have a higher infectivity, which rises to saturate at the Base_Infectivity value.

 
{
    "Population_Density_Infectivity_Correction": "SATURATING_FUNCTION_OF_DENSITY"
}

Population_Scale_Type

enum

NA

NA

USE_INPUT_FILE

The method to use for scaling the initial population specified in the demographics input file. Possible values are:

USE_INPUT_FILE

Turns off population scaling and uses InitialPopulation in the demographics file (see NodeAttributes parameters).

FIXED_SCALING

Enables Base_Population_Scale_Factor.

 
{
    "Population_Scale_Type": "FIXED_SCALING"
}

x_Base_Population

float

0

3.40E+38

1

Scale factor for InitialPopulation in the demographics file (see NodeAttributes parameters). If Population_Scale_Type is set to FIXED_SCALING, the initial simulation population is uniformly scaled over the entire area to adjust for historical or future population density.

 
{
    "x_Base_Population": 0.0001
}

x_Birth

float

0

3.40E+38

1

Scale factor for birth rate, as provided by the demographics file (see NodeAttributes parameters). Enable_Birth must be set to 1.

 
{
    "x_Birth": 1
}
Sampling

The following parameters determine how a population is sampled in the simulation. While you may want every agent (individual object) to represent a single person, you can often optimize CPU time with without degrading the accuracy of the simulation but having an agent represent multiple people. The sampling rate may be adapted to have a higher or lower sampling rate for particular regions or age groups.

For vector and malaria simulations, the same concepts apply to sampling the vector population.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Base_Individual_Sample_Rate

float

0.001

1

1

The base rate of sampling for individuals, equal to the fraction of individuals in each node being sampled. Reducing the sampling rate will reduce the time needed to run simulations. Individual_Sampling_Type must be set to FIXED_SAMPLING or ADAPTED_SAMPLING_BY_IMMUNE_STATE.

 
{
    "Base_Individual_Sample_Rate": 0.01
}

Immune_Threshold_For_Downsampling

float

0

1

0

The threshold on acquisition immunity at which to apply immunity-dependent downsampling; individuals who are more immune than this this threshold are downsampled. A value of 1 is equivalent to full susceptibility and 0 is equivalent to full immunity. If the acquisition immunity modifier is larger than the threshold, no downsampling occurs. Individual_Sampling_Type must set to ADAPTED_SAMPLING_BY_IMMUNE_STATE.

 
{
    "Relative_Sample_Rate_Immune": 0.1,
    "Immune_Threshold_For_Downsampling": 0.5,
    "Individual_Sampling_Type": "ADAPTED_SAMPLING_BY_IMMUNE_STATE"
}

Individual_Sampling_Type

enum

NA

NA

TRACK_ALL

The type of individual human sampling to use, which can be used to down sample large populations, certain age groups, or the immune population that does not contribute to transmission. Possible values are:

TRACK_ALL

Each person in the population is tracked as a single Individual object with a sampling weight of 1.

FIXED_SAMPLING

Members of the population are added to the simulation with a probability of Base_Individual_Sample_Rate and sampling weight of 1 / Base_Individual_Sample_Rate. This allows a smaller set of Individual objects to represent the overall population.

ADAPTED_SAMPLING_BY_POPULATION_SIZE

For each node, a maximum number of Individual objects is tracked (set in Max_Node_Population_Samples). If the population is smaller than this number, each person is tracked with a sampling rate of 1; if the population is larger, members of the population are added to the simulation with a probability of Max_Node_Population_Samples / population and sampling weight of population / Max_Node_Population_Samples. This setting is useful for simulations over large geographic areas with large heterogeneities in population density.

ADAPTED_SAMPLING_BY_AGE_GROUP

The population is binned by age and each age bin is sampled at a different rate as defined by Sample_Rate parameters. The setting is useful for diseases in which only infants or children are relevant by allowing full sampling of these age groups while using fewer objects to represent the rest of the population.

ADAPTED_SAMPLING_BY_AGE_GROUP_AND_POP_SIZE

Each node has a maximum number of Individual objects as described in ADAPTED_SAMPLING_BY_POPULATION_SIZE, but when the population grows beyond that threshold, sampling is not done at a fixed rate, but varies by age as described in ADAPTED_SAMPLING_BY_AGE_GROUP.

ADAPTED_SAMPLING_BY_IMMUNE_STATE

Individuals who have acquired immunity are sampled at a lower rate as defined by Relative_Sample_Rate_Immune and Immune_Threshold_For_Downsampling parameters. This converges with regular sampling when those parameters are set to 1 or 0, respectively.

The following example shows how to sampling immune individuals at a lower rate.

{
    "Individual_Sampling_Type": "ADAPTED_SAMPLING_BY_IMMUNE_STATE",
    "Immune_Threshold_For_Downsampling": 0.5,
    "Relative_Sample_Rate_Immune": 0.1
}

The following example shows how to sampling older individuals at a lower rate.

{
    "Individual_Sampling_Type": "ADAPTED_SAMPLING_BY_AGE_GROUP",
    "Sample_Rate_0_18mo": 1,
    "Sample_Rate_10_14": 0.5,
    "Sample_Rate_15_19": 0.3,
    "Sample_Rate_18mo_4yr": 1,
    "Sample_Rate_20_Plus": 0.2,
    "Sample_Rate_5_9": 1,
    "Sample_Rate_Birth": 1
}

Max_Node_Population_Samples

float

1

3.40E+38

30

The maximum number of individuals to track in a node. When the population exceeds this number, the sampling rate will drop according to the value set in Individual_Sampling_Type.

 
{
    "Individual_Sampling_Type": "ADAPTED_SAMPLING_BY_POPULATION_SIZE",
    "Max_Node_Population_Samples": 100000
}

Mosquito_Weight

integer

1

10000

1

The value indicating how many mosquitoes are represented by a sample mosquito in the simulation. Vector_Sampling_Type must be set to SAMPLE_IND_VECTORS.

 
{
    "Mosquito_Weight": 10
}

Relative_Sample_Rate_Immune

float

0.001

1

0.1

The relative sampling rate for people who have acquired immunity through recovery or vaccination. The immune threshold at which to downsample is set by Immune_Threshold_For_Downsampling. If set to 1, this will have no effect, even if the individual’s immunity modifier is below threshold. This can be a useful sanity check while learning this feature. Individual_Sampling_Type must be set to ADAPTED_SAMPLING_BY_IMMUNE_STATE.

 
{
    "Relative_Sample_Rate_Immune": 0.1,
    "Immune_Threshold_For_Downsampling": 0.8,
    "Individual_Sampling_Type": "ADAPTED_SAMPLING_BY_IMMUNE_STATE"
}

Sample_Rate_0_18mo

float

0.001

1000

1

For age-adapted sampling, the relative sampling rate for infants age 0 to 18 months. Individual_Sampling_Type must be set to ADAPTED_SAMPLING_BY_AGE_GROUP or ADAPTED_SAMPLING_BY_AGE_GROUP_AND_POP_SIZE.

 
{
    "Sample_Rate_0_18mo": 1
}

Sample_Rate_10_14

float

0.001

1000

1

For age-adapted sampling, the relative sampling rate for children age 10 to 14 years. Individual_Sampling_Type must be set to ADAPTED_SAMPLING_BY_AGE_GROUP or ADAPTED_SAMPLING_BY_AGE_GROUP_AND_POP_SIZE.

 
{
    "Sample_Rate_10_14": 1
}

Sample_Rate_15_19

float

0.001

1000

1

For age-adapted sampling, the relative sampling rate for adolescents age 15 to 19 years. Individual_Sampling_Type must be set to ADAPTED_SAMPLING_BY_AGE_GROUP or ADAPTED_SAMPLING_BY_AGE_GROUP_AND_POP_SIZE.

 
{
    "Sample_Rate_15_19": 1
}

Sample_Rate_18mo_4yr

float

0.001

1000

1

For age-adapted sampling, the relative sampling rate for children age 18 months to 4 years. Individual_Sampling_Type must be set to ADAPTED_SAMPLING_BY_AGE_GROUP or ADAPTED_SAMPLING_BY_AGE_GROUP_AND_POP_SIZE.

 
{
    "Sample_Rate_18mo_4yr": 1
}

Sample_Rate_20_plus

float

0.001

1000

1

For age-adapted sampling, the relative sampling rate for adults age 20 and older. Individual_Sampling_Type must be set to ADAPTED_SAMPLING_BY_AGE_GROUP or ADAPTED_SAMPLING_BY_AGE_GROUP_AND_POP_SIZE.

 
{
    "Sample_Rate_20_plus": 1
}

Sample_Rate_5_9

float

0.001

1000

1

For age-adapted sampling, the relative sampling rate for children age 5 to 9 years. Individual_Sampling_Type must be set to ADAPTED_SAMPLING_BY_AGE_GROUP or ADAPTED_SAMPLING_BY_AGE_GROUP_AND_POP_SIZE.

 
{
    "Sample_Rate_5_9": 1
}

Sample_Rate_Birth

float

0.001

1000

1

For age-adapted sampling, the relative sampling rate for births. Individual_Sampling_Type must be set to ADAPTED_SAMPLING_BY_AGE_GROUP or ADAPTED_SAMPLING_BY_AGE_GROUP_AND_POP_SIZE.

 
{
    "Sample_Rate_Birth": 1
}

Vector_Sampling_Type

enum

NA

NA

TRACK_ALL_VECTORS

The type of vector sampling used. Possible values are:

TRACK_ALL_VECTORS

Each vector in a node is part of the simulation as a separate object.

SAMPLE_IND_VECTORS

Each vector object in the simulation represents multiple vectors in the node population. Mosquito_Weight sets the number of mosquitos represented by each object.

VECTOR_COMPARTMENTS_NUMBER

Does not track every vector individually, but provides a compartment for every possible entry in state space and tracks the number of vectors in each compartment. This successfully accounts for each vector, but does not distinguish between vectors that have identical states, just counts how many identical vectors of each state are present at a location.

VECTOR_COMPARTMENTS_PERCENT

Similar to VECTOR_COMPARTMENTS_NUMBER, but it only tracks the percentage of the population in each state. Less useful, but included for comparison to other models.

 
{
    "Vector_Sampling_Type": "TRACK_ALL_VECTORS"
}
Scalars and multipliers

The following parameters scale or multiply values set in other areas of the configuration file or other input files. This can be especially useful for understanding the sensitivities of disease dynamics to input data without requiring modifications to those base values. For example, one might set x_Birth to a value less than 1 to simulate a lower future birth rate due to increased economic prosperity and available medical technology.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Acquire_Modifier

float

0

1

1

Modifier of the probability of successful infection of a mosquito by an infected individual, given the individual’s infectiousness.

 
{
    "Vector_Species_Params": [
        {
            "Name": "aegypti",
            "Acquire_Modifier": 1
        }
    ]
}

Antibody_IRBC_Kill_Rate

double

NA

NA

2

The scale factor multiplied by antibody level to produce the rate of clearance of the infected red blood cell (IRBC) population.

 
{
    "Antibody_IRBC_Kill_Rate": 1.595
}

Aquatic_Arrhenius_1

float

0

1.00E+15

8.42E+10

The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the daily rate of fractional progression of mosquito aquatic development (egg-hatching through emergence). This duration is a decreasing function of temperature. The variable a1 is a temperature-independent scale factor on development rate.

 
{
    "Vector_Species_Params": [
        {
            "Name": "aegypti",
            "Aquatic_Arrhenius_1": 9752291.727
        }
    ]
}

Aquatic_Arrhenius_2

float

0

1.00E+15

8328

The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the daily rate of fractional progression of mosquito aquatic development (egg-hatching through emergence). This duration is a decreasing function of temperature. The variable a2 governs how quickly the rate changes with temperature.

 
{
    "Vector_Species_Params": [
        {
            "Name": "aegypti",
            "Aquatic_Arrhenius_2": 8328
        }
    ]
}

Birth_Rate_Time_Dependence

enum

NA

NA

NONE

A scale factor for BirthRate that allows it to be altered by time or season. Enable_Birth must be set to true (1). Possible values are:

NONE

Birth rate does not vary by time.

SINUSOIDAL_FUNCTION_OF_TIME

Allows birth rate to be time-dependent, following a sinusoidal shape defined by \(1 + \sin \text{Birth\_Rate\_Sinusoidal\_Forcing\_Amplitude} \times \sin(2 \pi \times (\text{day} - \sin \text{Birth\_Rate\_Sinusoidal\_Forcing\_Phase})/365)\). Set Birth_Rate_Sinusoidal_Forcing_Amplitude and Birth_Rate_Sinusoidal_Forcing_Phase.

ANNUAL_BOXCAR_FUNCTION

Allows birth rate to follow a boxcar function. The scale factor will be equal to 1 except for a single interval in which it is equal to a given constant equal to 1 + Birth_Rate_Boxcar_Forcing_Amplitude. Set Birth_Rate_Boxcar_Forcing_Amplitude, Birth_Rate_Boxcar_Forcing_End_Time, and Birth_Rate_Boxcar_Forcing_Start_Time.

 
{
    "Enable_Vital_Dynamics": 1,
    "Enable_Birth": 1,
    "Birth_Rate_Time_Dependence": "ANNUAL_BOXCAR_FUNCTION"
}

Cycle_Arrhenius_1

float

0

1.00E+15

4.09E+10

The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the mosquito feeding cycle rate. This duration is a decreasing function of temperature. The variable a1 is a temperature-independent scale factor on feeding rate. Temperature_Dependent_Feeding_Cycle must be set to ARRHENIUS_DEPENDENCE.

 
{
    "Vector_Species_Params": [
        {
            "Name": "aegypti",
            "Temperature_Dependent_Feeding_Cycle": "ARRHENIUS_DEPENDENCE",
            "Cycle_Arrhenius_1": 99,
            "Cycle_Arrhenius_2": 88
        }
    ]
}

Cycle_Arrhenius_2

float

0

1.00E+15

7740

The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the mosquito feeding cycle rate. This duration is a decreasing function of temperature. The variable a2 is a temperature-independent scale factor on feeding rate. Temperature_Dependent_Feeding_Cycle must be set to ARRHENIUS_DEPENDENCE.

 
{
    "Vector_Species_Params": [
        {
            "Name": "aegypti",
            "Temperature_Dependent_Feeding_Cycle": "ARRHENIUS_DEPENDENCE",
            "Cycle_Arrhenius_1": 99,
            "Cycle_Arrhenius_2": 88
        }
    ]
}

Cycle_Arrhenius_Reduction_Factor

float

0

1

1

The scale factor applied to cycle duration (from oviposition to oviposition) to reduce the duration when primary follicles are at stage II rather than I in the case of newly emerged females. Temperature_Dependent_Feeding_Cycle must be set to ARRHENIUS_DEPENDENCE.

 
{
    "Vector_Species_Params": [
        {
            "Name": "aegypti",
            "Temperature_Dependent_Feeding_Cycle": "ARRHENIUS_DEPENDENCE",
            "Cycle_Arrhenius_Reduction_Factor": 0.44
        }
    ]
}

Egg_Arrhenius1

float

0

1.00E+10

6.16E+07

The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the daily rate of mosquito egg hatching. This duration is a decreasing function of temperature. The variable a1 is a temperature-independent scale factor on hatching rate. Enable_Temperature_Dependent_Egg_Hatching must be set to 1.

 
{
    "Enable_Temperature_Dependent_Egg_Hatching": 1,
    "Egg_Arrhenius1": 61599956,
    "Egg_Arrhenius2": 5754
}

Egg_Arrhenius2

float

0

1.00E+10

5754.03

The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the daily rate of mosquito egg hatching. This duration is a decreasing function of temperature. The variable a2 is a temperature-dependent scale factor on hatching rate. Enable_Temperature_Dependent_Egg_Hatching must be set to 1.

 
{
    "Enable_Temperature_Dependent_Egg_Hatching": 1,
    "Egg_Arrhenius1": 61599956,
    "Egg_Arrhenius2": 5754
}

Infected_Arrhenius_1

float

0

1.00E+15

1.17E+11

The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the daily rate of fractional progression of infected mosquitoes to an infectious state. The duration of sporogony is a decreasing function of temperature. The variable a1 is a temperature-independent scale factor on the progression rate to infectiousness.

 
{
    "Vector_Species_Params": [
        {
            "Name": "arabiensis",
            "Acquire_Modifier": 0.2,
            "Adult_Life_Expectancy": 20,
            "Anthropophily": 0.65,
            "Aquatic_Arrhenius_1": 84200000000,
            "Aquatic_Arrhenius_2": 8328,
            "Aquatic_Mortality_Rate": 0.1,
            "Days_Between_Feeds": 3,
            "Egg_Batch_Size": 100,
            "Habitats": [
                {
                    "Habitat_Type": "TEMPORARY_RAINFALL",
                    "Max_Larval_Capacity": 11250000000.0
                }
            ],
            "Immature_Duration": 2,
            "Indoor_Feeding_Fraction": 1,
            "Infected_Arrhenius_1": 117000000000,
            "Infected_Arrhenius_2": 8336,
            "Infected_Egg_Batch_Factor": 0.8,
            "Infectious_Human_Feed_Mortality_Factor": 1.5,
            "Male_Life_Expectancy": 10,
            "Temperature_Dependent_Feeding_Cycle": "NO_TEMPERATURE_DEPENDENCE",
            "Transmission_Rate": 0.5,
            "Vector_Sugar_Feeding_Frequency": "VECTOR_SUGAR_FEEDING_NONE"
        }
    ]
}

Infected_Arrhenius_2

float

0

1.00E+15

8340

The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the daily rate of fractional progression of infected mosquitoes to an infectious state. The duration of sporogony is a decreasing function of temperature. The variable a2 is a temperature-dependent scale factor on the progression rate to infectiousness.

 
{
    "Vector_Species_Params": [
        {
            "Name": "arabiensis",
            "Acquire_Modifier": 0.2,
            "Adult_Life_Expectancy": 20,
            "Anthropophily": 0.65,
            "Aquatic_Arrhenius_1": 84200000000,
            "Aquatic_Arrhenius_2": 8328,
            "Aquatic_Mortality_Rate": 0.1,
            "Days_Between_Feeds": 3,
            "Egg_Batch_Size": 100,
            "Habitats": [
                {
                    "Habitat_Type": "TEMPORARY_RAINFALL",
                    "Max_Larval_Capacity": 11250000000.0
                }
            ],
            "Immature_Duration": 2,
            "Indoor_Feeding_Fraction": 1,
            "Infected_Arrhenius_1": 117000000000,
            "Infected_Arrhenius_2": 8336,
            "Infected_Egg_Batch_Factor": 0.8,
            "Infectious_Human_Feed_Mortality_Factor": 1.5,
            "Male_Life_Expectancy": 10,
            "Temperature_Dependent_Feeding_Cycle": "NO_TEMPERATURE_DEPENDENCE",
            "Transmission_Rate": 0.5,
            "Vector_Sugar_Feeding_Frequency": "VECTOR_SUGAR_FEEDING_NONE"
        }
    ]
}

Infected_Egg_Batch_Factor

float

0

10

0.8

The dimensionless factor used to modify mosquito egg batch size in order to account for reduced fertility effects arising due to infection (e.g. when females undergo sporogony).

 
{
    "Vector_Species_Params": [
        {
            "Name": "arabiensis",
            "Acquire_Modifier": 0.2,
            "Adult_Life_Expectancy": 20,
            "Anthropophily": 0.65,
            "Aquatic_Arrhenius_1": 84200000000,
            "Aquatic_Arrhenius_2": 8328,
            "Aquatic_Mortality_Rate": 0.1,
            "Days_Between_Feeds": 3,
            "Egg_Batch_Size": 100,
            "Habitats": [
                {
                    "Habitat_Type": "TEMPORARY_RAINFALL",
                    "Max_Larval_Capacity": 11250000000.0
                }
            ],
            "Immature_Duration": 2,
            "Indoor_Feeding_Fraction": 1,
            "Infected_Arrhenius_1": 117000000000,
            "Infected_Arrhenius_2": 8336,
            "Infected_Egg_Batch_Factor": 0.8,
            "Infectious_Human_Feed_Mortality_Factor": 1.5,
            "Male_Life_Expectancy": 10,
            "Temperature_Dependent_Feeding_Cycle": "NO_TEMPERATURE_DEPENDENCE",
            "Transmission_Rate": 0.5,
            "Vector_Sugar_Feeding_Frequency": "VECTOR_SUGAR_FEEDING_NONE"
        }
    ]
}

Infectious_Human_Feed_Mortality_Factor

float

0

1000

1.5

The (dimensionless) factor used to modify the death rate of mosquitoes when feeding on humans, to account for the higher mortality rate infected mosquitoes experience during human feeds versus uninfected mosquitoes.

 
{
    "Vector_Species_Params": [
        {
            "Name": "arabiensis",
            "Acquire_Modifier": 0.2,
            "Adult_Life_Expectancy": 20,
            "Anthropophily": 0.65,
            "Aquatic_Arrhenius_1": 84200000000,
            "Aquatic_Arrhenius_2": 8328,
            "Aquatic_Mortality_Rate": 0.1,
            "Days_Between_Feeds": 3,
            "Egg_Batch_Size": 100,
            "Habitats": [
                {
                    "Habitat_Type": "TEMPORARY_RAINFALL",
                    "Max_Larval_Capacity": 11250000000.0
                }
            ],
            "Immature_Duration": 2,
            "Indoor_Feeding_Fraction": 1,
            "Infected_Arrhenius_1": 117000000000,
            "Infected_Arrhenius_2": 8336,
            "Infected_Egg_Batch_Factor": 0.8,
            "Infectious_Human_Feed_Mortality_Factor": 1.5,
            "Male_Life_Expectancy": 10,
            "Temperature_Dependent_Feeding_Cycle": "NO_TEMPERATURE_DEPENDENCE",
            "Transmission_Rate": 0.5,
            "Vector_Sugar_Feeding_Frequency": "VECTOR_SUGAR_FEEDING_NONE"
        }
    ]
}

Infectivity_Exponential_Baseline

float

0

1

0

The scale factor applied to Base_Infectivity at the beginning of a simulation, before the infectivity begins to grow exponentially. Infectivity_Scale_Type must be set to EXPONENTIAL_FUNCTION_OF_TIME.

 
{
    "Infectivity_Exponential_Baseline": 0.1,
    "Infectivity_Exponential_Delay": 90,
    "Infectivity_Exponential_Rate": 45,
    "Infectivity_Scale_Type": "EXPONENTIAL_FUNCTION_OF_TIME"
}

Larval_Density_Mortality_Scalar

float

0.01

1000

10

A scale factor in the formula determining the larval-age-dependent mortality for the GRADUAL_INSTAR_SPECIFIC and LARVAL_AGE_DENSITY_DEPENDENT_MORTALITY_ONLY models.

 
{
    "Larval_Density_Mortality_Scalar": 1.0
}

Newborn_Biting_Risk_Multiplier

float

0

1

0.2

The scale factor that defines the y-intercept of the linear portion of the biting risk curve when Age_Dependent_Biting_Risk_Type is set to LINEAR.

 
{
    "Newborn_Biting_Risk_Multiplier": 0.2
}

Nonspecific_Antibody_Growth_Rate_Factor

float

0

1000

0.5

The factor that adjusts Antibody_Capacity_Growth_Rate for less immunogenic surface proteins, called minor epitopes.

 
{
    "Nonspecific_Antibody_Growth_Rate_Factor": 0.5
}

Population_Scale_Type

enum

NA

NA

USE_INPUT_FILE

The method to use for scaling the initial population specified in the demographics input file. Possible values are:

USE_INPUT_FILE

Turns off population scaling and uses InitialPopulation in the demographics file (see NodeAttributes parameters).

FIXED_SCALING

Enables Base_Population_Scale_Factor.

 
{
    "Population_Scale_Type": "FIXED_SCALING"
}

Post_Infection_Acquisition_Multiplier

float

0

1

0

The multiplicative reduction in the probability of reacquiring disease. At the time of recovery, the immunity against acquisition is multiplied by Acquisition_Blocking_Immunity_Decay_Rate x (1 - Post_Infection_Acquisition_Multiplier). Enable_Immunity must be set to 1 (true).

 
{
    "Enable_Immunity": 1,
    "Enable_Immune_Decay": 1,
    "Post_Infection_Acquisition_Multiplier": 0.9
}

Post_Infection_Mortality_Multiplier

float

0

1

0

The multiplicative reduction in the probability of dying from infection after getting reinfected. At the time of recovery, the immunity against mortality is multiplied by Mortality_Blocking_Immunity_Decay_Rate x (1 - Post_Infection_Mortality_Multiplier). Enable_Immunity must be set to 1 (true).

 
{
    "Enable_Immunity": 1,
    "Enable_Immune_Decay": 1,
    "Post_Infection_Mortality_Multiplier": 0.5
}

Post_Infection_Transmission_Multiplier

float

0

1

0

The multiplicative reduction in the probability of transmitting infection after getting reinfected. At the time of recovery, the immunity against transmission is multiplied by Transmission_Blocking_Immunity_Decay_Rate x (1 - Post_Infection_Transmission_Multiplier). Enable_Immunity must be set to 1 (true).

 
{
    "Enable_Immunity": 1,
    "Enable_Immunity_Decay": 1,
    "Post_Infection_Transmission_Multiplier": 0.9
}

Vector_Migration_Food_Modifier

float

0

3.40E+38

0

The preference of a vector to migrate toward a node currently occupied by humans, independent of the number of humans in the node. Used only when Vector_Sampling_Type is set to TRACK_ALL_VECTORS. Enable_Vector_Migration must be set to 1.

 
{
    "Vector_Migration_Food_Modifier": 1.0
}

Vector_Migration_Habitat_Modifier

float

0

3.40E+38

0

The preference of a vector to migrate toward a node with more habitat. Only used when Vector_Sampling_Type is set to TRACK_ALL_VECTORS. Enable_Vector_Migration must be set to 1.

 
{
    "Vector_Migration_Habitat_Modifier": 1.0
}

Vector_Migration_Modifier_Equation

enum

NA

NA

LINEAR

The functional form of vector migration modifiers. Enable_Vector_Migration must be set to 1. Possible values are: LINEAR EXPONENTIAL

 
{
    "Vector_Migration_Modifier_Equation": "EXPONENTIAL"
}

Vector_Migration_Stay_Put_Modifier

float

0

3.40E+38

0

The preference of a vector to remain in its current node rather than migrate to another node. Used only when Vector_Sampling_Type is set to TRACK_ALL_VECTORS. Enable_Vector_Migration must be set to 1.

 
{
    "Vector_Migration_Stay_Put_Modifier": 1.0
}

x_Air_Migration

float

0

3.40E+38

1

Scale factor for the rate of migration by air, as provided by the migration file. Enable_Air_Migration must be set to 1.

 
{
    "Scale_Factor_Air_Migration": 1
}

x_Base_Population

float

0

3.40E+38

1

Scale factor for InitialPopulation in the demographics file (see NodeAttributes parameters). If Population_Scale_Type is set to FIXED_SCALING, the initial simulation population is uniformly scaled over the entire area to adjust for historical or future population density.

 
{
    "x_Base_Population": 0.0001
}

x_Birth

float

0

3.40E+38

1

Scale factor for birth rate, as provided by the demographics file (see NodeAttributes parameters). Enable_Birth must be set to 1.

 
{
    "x_Birth": 1
}

x_Family_Migration

float

0

3.40E+38

1

Scale factor for the rate of migration by families, as provided by the migration file. Enable_Family_Migration must be set to true (1).

 
{
    "x_Family_Migration": 1
}

x_Temporary_Larval_Habitat

float

0

10000

1

Scale factor for the habitat size for all mosquito populations.

 
{
    "x_Temporary_Larval_Habitat": 1
}

x_Local_Migration

float

0

3.40E+38

1

Scale factor for rate of migration by foot travel, as provided by the migration file. Enable_Local_Migration must be set to 1.

 
{
    "x_Local_Migration": 1
}

x_Other_Mortality

float

0

3.40E+38

1

Scale factor for mortality from causes other than the disease being simulated. Base mortality is provided by the demographics file (see Complex distributions parameters). Enable_Natural_Mortality must be set to 1.

 
{
    "x_Other_Mortality": 1
}

x_Regional_Migration

float

0

3.40E+38

1

Scale factor for the rate of migration by road vehicle, as provided by the migration file. Enable_Regional_Migration must be set to 1.

 
{
    "x_Regional_Migration": 1
}

x_Sea_Migration

float

0

3.40E+38

1

Scale factor for the rate of migration by sea, as provided by the migration file. Enable_Sea_Migration must be set to 1.

 
{
    "x_Sea_Migration": 1
}

x_Vector_Migration_Local

float

0

3.40E+38

1

Scale factor for the rate of vector migration to adjacent nodes, as provided by the vector migration file. Enable_Vector_Migration must be set to 1.

 
{
    "x_Vector_Migration_Local": 1.0
}

x_Vector_Migration_Regional

float

0

3.40E+38

1

Scale factor for the rate of vector migration to non-adjacent nodes, as provided by the vector migration file. Enable_Vector_Migration must be set to 1.

 
{
    "x_Vector_Migration_Regional": 1.0
}
Simulation setup

These parameters determine the basic setup of a simulation including the type of simulation you are running, such as “GENERIC_SIM” or “MALARIA_SIM”, the simulation duration, and the time step duration.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Config_Name

string

NA

NA

UNINITIALIZED STRING

The optional, user-supplied title naming a configuration file.

 
{
    "Config_Name": "My_First_Config"
}

Custom_Coordinator_Events

array of strings

NA

NA

NA

The list of valid, user-defined events for Event Coordinators that will be included in the campaign. Any event used in the campaign must either be one of the built-in events or in this list.

Note: This configuration parameter is currently in beta release and has not yet been fully tested.

 
{
    "Custom_Coordinator_Events": [
        "Coordinator_Event_1",
        "Coordinator_Event_2",
        "Coordinator_Event_3"
    ]
}

Custom_Individual_Events

array of strings

NA

NA

NA

The list of valid, user-defined events that will be included in the campaign. Any event used in the campaign must either be one of the built-in events or in this list.

Note

This configuration parameter is currently in beta release and has not yet been fully tested.

 
{
    "Custom_Individual_Events": [
        "Individual_Event_1",
        "Individual_Event_2",
        "Individual_Event_3"
    ]
}

Custom_Node_Events

array of strings

NA

NA

NA

The list of valid, user-defined events for nodes that will be included in the campaign. Any event used in the campaign must either be one of the built-in events or in this list.

Note

This configuration parameter is currently in beta release and has not yet been fully tested.

 
{
    "Custom_Node_Events": [
        "Node_Event_1",
        "Node_Event_2",
        "Node_Event_3"
    ]
}

Enable_Interventions

boolean

0

1

0

Controls whether or not campaign interventions will be used in the simulation. Set Campaign_Filename to the path of the file that contains the campaign interventions.

 
{
    "Enable_Interventions": 1,
    "Campaign_Filename": "campaign.json"
}

Enable_Random_Generator_From_Serialized_Population

boolean

0

1

0

Determines if the random number generator(s) should be extracted from a serialized population file. Enabling this will start a simulation from this file with the exact same random number stream and location in that stream as when the file was serialized.

 
{
    "Run_Number": 12,
    "Random_Number_Generator_Type": "USE_AES_COUNTER",
    "Random_Number_Generator_Policy": "ONE_PER_NODE",
    "Enable_Random_Generator_From_Serialized_Population": 1
}

Enable_Skipping

boolean

0

1

0

Controls whether or not the simulation uses an optimization that can increase performance by up to 50% in some cases by probabilistically exposing individuals rather than exposing every single person. Useful in low-prevalence, high-population scenarios.

 
{
    "Enable_Skipping": 0
}

Enable_Termination_On_Zero_Total_Infectivity

boolean

0

1

0

Controls whether or not the simulation should be ended when total infectivity falls to zero. Supported only in single-node simulations.

 
{
    "Enable_Termination_On_Zero_Total_Infectivity": 1,
    "Minimum_End_Time": 3650
}

Listed_Events

array of strings

NA

NA

[]

The list of valid, user-defined events that will be included in the campaign. Any event used in the campaign must either be one of the built-in events or in this list.

 
{
    "Listed_Events": [
        "Vaccinated",
        "VaccineExpired"
    ]
}

Malaria_Model

enum

NA

NA

MALARIA_MECHANISTIC_MODEL

The type of malaria model used in a simulation. Possible values are:

  • MALARIA_MECHANISTIC_MODEL

  • MALARIA_MECHANISTIC_MODEL_WITH_CO_TRANSMISSION

  • MALARIA_MECHANISTIC_MODEL_WITH_PARASITE_GENETICS

Note

Most malaria simulations should use MALARIA_MECHANISTIC_MODEL.

 
{
    "Malaria_Model": "MALARIA_MECHANISTIC_MODEL"
}

Memory_Usage_Halting_Threshold_Working_Set_MB

integer

0

1.00E+06

8000

The maximum size (in MB) of working set memory before the system throws an exception and halts.

 
{
    "Memory_Usage_Halting_Threshold_Working_Set_MB": 4000
}

Memory_Usage_Warning_Threshold_Working_Set_MB

integer

0

1.00E+06

7000

The maximum size (in MB) of working set memory before memory usage statistics are written to the log regardless of log level.

 
{
    "Memory_Usage_Warning_Threshold_Working_Set_MB": 3500
}

Node_Grid_Size

float

0.00416

90

0.004167

The spatial resolution indicating the node grid size for a simulation in degrees.

 
{
    "Node_Grid_Size": 0.042
}

Random_Number_Generator_Policy

enum

NA

NA

ONE_PER_CORE

The policy that determines if random numbers are generated for objects in a simulation on a per-core or per-node basis. The following values are available:

ONE_PER_CORE

A random number generator (RNG) is created for each computing core running a simulation. When running a simulation on a single core, there will be only one RNG. If running the single simulation on multiple cores, each core will have its own RNG. The RNGs on the different cores start out such that they will not generate the same stream of random numbers. Prior to EMOD 2.19, all simulations used this policy.

ONE_PER_NODE

An RNG is created for each geographic node in the simulation. The advantages of this policy are that 1) an event that causes a random number to be drawn in one node does not cause things to change in another node and 2) changing a simulation from single core to multi-core will not change the results. The RNGs on the different nodesstart out such that they will not generate the same stream of random numbers.

 
{
    "Run_Number": 15,
    "Random_Number_Generator_Type": "USE_AES_COUNTER",
    "Random_Number_Generator_Policy": "ONE_PER_NODE"
}

Random_Number_Generator_Type

enum

NA

NA

USE_PSEUDO_DES

The type of random number generator to use for objects in a simulation. Must set the RNG seed in Run_Number. The following values are available:

USE_PSEUDO_DES

Based on Numerical Recipes in C. The Art of Scientific Computing, 2nd ed. Press, William H. et. al, 1992. Prior to EMOD 2.19, this was the only generator available.

USE_LINEAR_CONGRUENTIAL

Based on The Structure of Linear Congruential Sequences, Marsaglia, George, 1972.

USE_AES_COUNTER

Based on AES in CTR Mode encryption as implemented in Intel (R) Advanced Encryption Standard (AES) New Instruction Set.

 
{
    "Run_Number": 15,
    "Random_Number_Generator_Type": "USE_LINEAR_CONGRUENTIAL",
    "Random_Number_Generator_Policy": "ONE_PER_NODE",
    "Enable_Random_Generator_From_Serialized_Population": 1
}

Run_Number

integer

0

65535

1

Sets the random number seed used with Random_Number_Generator_Type and Random_Number_Generator_Policy to assign random numbers to objects in a simulation. This value must be different for each simulation run to ensure model stochasticity.

 
{
    "Run_Number": 15,
    "Random_Number_Generator_Type": "USE_PSEUDO_DES",
    "Random_Number_Generator_Policy": "ONE_PER_NODE",
    "Enable_Random_Generator_From_Serialized_Population": 1
}

Serialization_Times

array of floats

0

The list of times at which to save the serialized state to file. 0 indicates the initial state before simulation, n indicates the time to serialize in terms of start time and step size, and will be rounded up to the nearest time step. The serialized population files can then be loaded at the beginning of a simulation using Serialized_Population_Filenames and Serialized_Population_Path.

 
{
    "Serialization_Type": "TIME",
    "Serialization_Times": [
        40.5,
        81
    ]
}

Serialization_Time_Steps

array of integers

0

2.15E+09

The list of time steps after which to save the serialized state to file. 0 (zero) indicates the initial state before simulation, n indicates after the nth time step. Serialization_Type must be set to TIMESTEP. The serialized population files can then be loaded at the beginning of a simulation using Serialized_Population_Filenames and Serialized_Population_Path.

 
{
    "Serialization_Type": "TIMESTEP",
    "Serialization_Time_Steps": [
        0,
        10
    ]
}

Serialization_Type

enum

NA

NA

NONE

The type of serialization to perform. Serialization saves the population state at particular times so you can start from that state in other simulations. Accepted values are:

NONE

No serialization.

TIME

Use the definition from Serialization_Times.

TIMESTEP

Use the definition from Serialization_Timestep.

 
{
    "Serialization_Times": [
        365,
        3650
    ],
    "Serialization_Type": "TIMSTEP"
}

j

{}

Serialized_Population_Filenames

array of strings

NA

NA

NA

An array of filenames with serialized population data. The number of filenames must match the number of cores used for the simulation. The files must be in .dtk format. Serialized population files are created using Serialization_Time_Steps.

 
{
    "Serialized_Population_Filenames": [
        "state-00010.dtk"
    ]
}

Serialized_Population_Path

string

NA

NA

.

The root path for the serialized population files. Serialized population files are created using Serialization_Time_Steps.

 
{
    "Serialized_Population_Path": "../00_Generic_Version_1_save/output"
}

Simulation_Duration

float

0

1000000

1

The elapsed time (in days) from the start to the end of a simulation.

 
{
    "Simulation_Duration": 7300
}

Simulation_Timestep

float

0

1000000

1

The duration of a simulation time step, in days.

 
{
    "Simulation_Timestep": 1
}

Simulation_Type

enum

NA

NA

GENERIC_SIM

The type of disease being simulated. Possible IDM-supported values are:

  • GENERIC_SIM

  • VECTOR_SIM

  • MALARIA_SIM

  • TBHIV_SIM

  • STI_SIM

  • HIV_SIM

  • ENVIRONMENTAL_SIM

  • TYPHOID_SIM

 
{
    "Simulation_Type": "STI_SIM"
}

Start_Time

float

0

1000000

1

The time, in days, when the simulation begins. This time is used to identify the starting values of the temporal input data, such as specifying which day’s climate values should be used for the first day of the simulation.

Note

The Start_Day of campaign events is in absolute time, so time relative to the beginning of the simulation depends on this parameter.

 
{
    "Start_Time": 365
}
Symptoms and diagnosis

The following parameters determine the characteristics of malaria diagnosis and symptoms as the disease progresses, such as anemia and fever. See Malaria symptoms and diagnostics for more information on malaria diagnostics and symptoms.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Anemia_Mortality_Inverse_Width

float

0.1

1000000

10

The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function:

probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) )

This parameter configures the inverse width relative to threshold value of severe disease turn-on around threshold for anemia.

 
{
    "Anemia_Mortality_Inverse_Width": 150
}

Anemia_Mortality_Threshold

double

NA

NA

3

The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function:

probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) )

This parameter configures the inverse width relative to threshold value of mortality turn-on around threshold for hemoglobin count in grams per deciliter (g/dL) at which 50% of individuals die per day.

 
{
    "Anemia_Mortality_Threshold": 1.5
}

Anemia_Severe_Inverse_Width

float

0.1

1000000

10

The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function:

probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) )

This parameter configures the inverse width relative to threshold value of severe disease turn-on around threshold for anemia.

 
{
    "Anemia_Severe_Inverse_Width": 20
}

Anemia_Severe_Threshold

float

0

100

5

The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function:

probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) )

This parameter configures the severe disease threshold level for anemia. Threshold units are in grams per deciliter (g/dL).

 
{
    "Anemia_Severe_Threshold": 3.0
}

Clinical_Fever_Threshold_High

float

0

15

1

The fever threshold (in degrees Celsius above normal) to start a clinical incident.

 
{
    "Clinical_Fever_Threshold_High": 1.5
}

Clinical_Fever_Threshold_Low

float

0

5

1

The fever threshold (in degrees Celsius above normal) to end a clinical incident.

 
{
    "Clinical_Fever_Threshold_Low": 0.5
}

Erythropoiesis_Anemia_Effect

float

0

1000

3.5

The exponential rate of increased red-blood-cell production from reduced red-blood-cell availability.

 
{
    "Erythropoiesis_Anemia_Effect": 3
}

Fever_Detection_Threshold

float

0.5

5

1

The level of body temperature above normal (defined as 37 C), in degrees Celsius, corresponding to detectable fever.

 
{
    "Fever_Detection_Threshold": 1
}

Fever_IRBC_Kill_Rate

float

0

1000

0.15

The maximum kill rate for infected red blood cells due to the inflammatory innate immune response. As fever increases above 38.5 degrees Celsius, the kill rate becomes successively higher along a sigmoidal curve approaching this rate.

 
{
    "Fever_IRBC_Kill_Rate": 1.4
}

Fever_Mortality_Inverse_Width

float

0.1

1000000

10

The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function:

probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) )

This parameter configures the inverse width relative to threshold value of mortality turn-on around threshold for fever.

 
{
    "Fever_Mortality_Inverse_Width": 1000
}

Fever_Mortality_Threshold

double

NA

NA

3

The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function:

probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) )

This parameter configures the fever mortality threshold, in units of degrees Celsius above normal body temperature (defined as 37 C).

 
{
    "Fever_Mortality_Threshold": 10
}

Fever_Severe_Inverse_Width

float

0.1

1000000

10

The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function:

probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) )

This parameter configures the inverse width relative to threshold value of mortality turn-on around threshold for fever.

 
{
    "Fever_Severe_Inverse_Width": 18.7
}

Fever_Severe_Threshold

float

0

10

1.5

The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function:

probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) )

This parameter configures the threshold (in degrees Celsius above normal) for fever indicating severe disease.

 
{
    "Fever_Severe_Threshold": 2.5
}

Min_Days_Between_Clinical_Incidents

float

0

1000000

3

The number of days with fever below the low-threshold before a new incident can start, when fever again exceeds the high-threshold.

 
{
    "Min_Days_Between_Clinical_Incidents": 14
}

New_Diagnostic_Sensitivity

float

0.0001

100000

0.01

The number of microliters of blood tested to find single parasites in a new diagnostic (corresponds to inverse parasites/microliters sensitivity).

 
{
    "New_Diagnostic_Sensitivity": 0.025
}
Vector control

The following parameters determine the characteristics of campaign interventions aimed at vector control, such as the homing endonuclease gene (HEG), sugar feeding, and Wolbachia infection. When, to whom, and how those interventions are distributed is determined by the campaign file.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Vector_Sugar_Feeding_Frequency

enum

NA

NA

VECTOR_SUGAR_FEEDING_NONE

The frequency of sugar feeding by a female mosquito. Used is used in conjunction with the SugarTrap and OvipositionTrap interventions. Vector_Sampling_Type must be set to TRACK_ALL_VECTORS or SAMPLE_IND_VECTORS. Possible values are:

VECTOR_SUGAR_FEEDING_NONE

No sugar feeding.

VECTOR_SUGAR_FEEDING_ON_EMERGENCE_ONLY

Sugar feeding once at emergence.

VECTOR_SUGAR_FEEDING_EVERY_FEED

Sugar feeding occurs once per blood meal.

VECTOR_SUGAR_FEEDING_EVERY_DAY

Sugar feeding occurs every day.

 
{
    "Vector_Sugar_Feeding_Frequency": "VECTOR_SUGAR_FEEDING_NONE"
}

Wolbachia_Infection_Modification

float

0

100

1

The change in vector susceptibility to infection due to a Wolbachia infection.

 
{
    "Wolbachia_Infection_Modification": 1.0
}

Wolbachia_Mortality_Modification

float

0

100

1

The change in vector mortality due to a Wolbachia infection.

 
{
    "Wolbachia_Infection_Modification": 0.0
}
Vector life cycle

The following parameters determine the characteristics of the vector life cycle. Set the vector species to include in the simulation and the feeding, egg-laying, migration, and larval development habits of each using these parameters. The parameters for larval development related to habitat and climate are described in Larval habitat.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Adult_Life_Expectancy

float

0

730

10

The number of days an adult mosquito survives. The daily adult mortality rate is 1 / Adult_Life_Expectancy, in absence of vector aging. With Enable_Vector_Aging set to 1, Adult_Life_Expectancy equal to 20 days corresponds to a mean adult female mosquito lifespan of 9-9.5 days in calibrated settings. The senescence formula that EMOD uses was described by Styer, et al <https://www.ncbi.nlm.nih.gov/pubmed/17255238>.

 
{
    "Enable_Vector_Aging": 1,
    "Vector_Species_Params": [
        {
            "Name": "arabiensis",
            "Adult_Life_Expectancy": 18
        }
    ]
}

Male_Life_Expectancy

float

0

730

10

The number of days an adult male mosquito survives. The daily male mortality rate is 1 / Male_Life_Expectancy, in absence of vector aging. The senescence formula that EMOD uses was described by Styer, et al <https://www.ncbi.nlm.nih.gov/pubmed/17255238>.

 
{
    "Enable_Vector_Aging": 1
    "Vector_Species_Params": [
        {
            "Name": "arabiensis",
            "Male_Life_Expectancy": 10,
        }
    ]
}

Anthropophily

float

0

1

1

Mosquito preference for humans over animals, measured as a fraction of blood meals from human hosts. This dimensionless propensity is important in differentiating between mosquito species.

 
{
    "Vector_Species_Params": [
        {
            "Name": "arabiensis",
            "Anthropophily": 0.95
        }
    ]
}

Cycle_Arrhenius_1

float

0

1.00E+15

4.09E+10

The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the mosquito feeding cycle rate. This duration is a decreasing function of temperature. The variable a1 is a temperature-independent scale factor on feeding rate. Temperature_Dependent_Feeding_Cycle must be set to ARRHENIUS_DEPENDENCE.

 
{
    "Vector_Species_Params": [
        {
            "Name": "arabiensis",
            "Temperature_Dependent_Feeding_Cycle": "ARRHENIUS_DEPENDENCE",
            "Cycle_Arrhenius_1": 99,
            "Cycle_Arrhenius_2": 88
        }
    ]
}

Cycle_Arrhenius_2

float

0

1.00E+15

7740

The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the mosquito feeding cycle rate. This duration is a decreasing function of temperature. The variable a2 is a temperature-independent scale factor on feeding rate. Temperature_Dependent_Feeding_Cycle must be set to ARRHENIUS_DEPENDENCE.

 
{
    "Vector_Species_Params": [
        {
            "Name": "arabiensis",
            "Temperature_Dependent_Feeding_Cycle": "ARRHENIUS_DEPENDENCE",
            "Cycle_Arrhenius_1": 99,
            "Cycle_Arrhenius_2": 88
        }
    ]
}

Cycle_Arrhenius_Reduction_Factor

float

0

1

1

The scale factor applied to cycle duration (from oviposition to oviposition) to reduce the duration when primary follicles are at stage II rather than I in the case of newly emerged females. Temperature_Dependent_Feeding_Cycle must be set to ARRHENIUS_DEPENDENCE.

 
{
    "Vector_Species_Params": [
        {
            "Name": "arabiensis",
            "Temperature_Dependent_Feeding_Cycle": "ARRHENIUS_DEPENDENCE",
            "Cycle_Arrhenius_Reduction_Factor": 0.44
        }
    ]
}

Days_Between_Feeds

float

0

730

2

The number of days between female mosquito feeding attempts. In the cohort model, this value is converted to a feeding rate, equal to 1/(value of this parameter), and is used to determine the probability that one of each of the various feeding modes (animal host, human host, indoor, outdoor, etc) occurs. In the individual model, this parameter determines the number of days a vector waits between feeds.

 
{
    "Vector_Species_Params": [
        {
            "Name": "arabiensis",
            "Days_Between_Feeds": 3
        }
    ]
}

Drought_Egg_Hatch_Delay

float

0

1

0.33

Proportion of regular egg hatching that still occurs when habitat dries up. Enable_Drought_Egg_Hatch_Delay must be set to 1.

 
{
    "Enable_Drought_Egg_Hatch_Delay": 1,
    "Drought_Egg_Hatch_Delay": 0.33
}

Egg_Arrhenius1

float

0

1.00E+10

6.16E+07

The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the daily rate of mosquito egg hatching. This duration is a decreasing function of temperature. The variable a1 is a temperature-independent scale factor on hatching rate. Enable_Temperature_Dependent_Egg_Hatching must be set to 1.

 
{
    "Enable_Temperature_Dependent_Egg_Hatching": 1,
    "Egg_Arrhenius1": 61599956,
    "Egg_Arrhenius2": 5754
}

Egg_Arrhenius2

float

0

1.00E+10

5754.03

The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the daily rate of mosquito egg hatching. This duration is a decreasing function of temperature. The variable a2 is a temperature-dependent scale factor on hatching rate. Enable_Temperature_Dependent_Egg_Hatching must be set to 1.

 
{
    "Enable_Temperature_Dependent_Egg_Hatching": 1,
    "Egg_Arrhenius1": 61599956,
    "Egg_Arrhenius2": 5754
}

Egg_Batch_Size

float

0

10000

100

Number of eggs laid by one female mosquito that has fed successfully. The value of Egg_Batch_Size is used for both the number of female eggs and for the number of male eggs. For example, the default value of 100 means that there are 100 female eggs and 100 male eggs.

 
{
    "Vector_Species_Params": [
        {
            "Name": "arabiensis",
            "Egg_Batch_Size": 100
        }
    ]
}

Egg_Hatch_Delay_Distribution

enum

NA

NA

NO_DELAY

The distribution of the delay from oviposition to egg hatching. Possible values are:

NO_DELAY

No delay between oviposition and egg hatching.

EXPONENTIAL_DISTRIBUTION

There is a delay that follows an exponential distribution. Set the mean delay in Mean_Egg_Hatch_Delay.

 
{
    "Egg_Hatch_Delay_Distribution": "EXPONENTIAL_DISTRIBUTION",
    "Mean_Egg_Hatch_Delay": 2
}

Egg_Hatch_Density_Dependence

enum

NA

NA

NO_DENSITY_DEPENDENCE

The effect of larval density on egg hatching rate. Possible values are:

DENSITY_DEPENDENCE

Egg hatching is reduced when the habitat is nearing its carrying capacity.

NO_DENSITY_DEPENDENCE

Egg hatching is not dependent upon larval density.

 
{
    "Egg_Hatch_Density_Dependence": "NO_DENSITY_DEPENDENCE"
}

Egg_Saturation_At_Oviposition

enum

NA

NA

NO_SATURATION

If laying all eggs from ovipositing females would overflow the larval habitat capacity on a given day, the means by which the viable eggs become saturated. Possible values are:

NO_SATURATION

Egg number does not saturate; all eggs are laid.

SATURATION_AT_OVIPOSITION

Eggs are saturated at oviposition; habitat is filled to capacity and excess eggs are discarded.

SIGMOIDAL_SATURATION

Eggs are saturated along a sigmoidal curve; proportionately fewer eggs are laid depending on how oversubscribed the habitat would be.

 
{
    "Egg_Saturation_At_Oviposition": "SATURATION_AT_OVIPOSITION"
}

Enable_Egg_Mortality

boolean

0

1

0

Controls whether or not to include a daily mortality rate on the egg population, which is independent of climatic factors.

 
{
    "Enable_Egg_Mortality": 1
}

Enable_Temperature_Dependent_Egg_Hatching

boolean

0

1

0

Controls whether or not temperature has an effect on egg hatching, defined by Egg_Arrhenius_1 and Egg_Arrhenius_2.

 
{
    "Enable_Temperature_Dependent_Egg_Hatching": 1,
    "Egg_Arrhenius1": 61599956.864,
    "Egg_Arrhenius2": 5754.033
}

Enable_Vector_Aging

boolean

0

1

0

Controls whether or not vectors senesce as they get older. When enabled, the daily mortality rate (1/Adult_Life_Expectancy) is modified by environmental conditions such as dry heat, feeding on humans, etc. With vector aging enabled, Adult_Life_Expectancy equal to 20 days corresponds to a mean adult female mosquito lifespan of 9-9.5 days in calibrated settings. The senescence formula that EMOD uses was described by Styer, et al <https://www.ncbi.nlm.nih.gov/pubmed/17255238>.

 
{
    "Enable_Vector_Aging": 1,
    "Adult_Life_Expectancy": 20
}

Enable_Vector_Mortality

boolean

0

1

1

Controls whether or not vectors can die. Vector_Sampling_Type must be set to TRACK_ALL_VECTORS.

 
{
    "Vector_Sampling_Type": "TRACK_ALL_VECTORS",
    "Enable_Vector_Mortality": 1
}

Human_Feeding_Mortality

float

0

1

0.1

The fraction of mosquitoes, for all species, that die while feeding on a human.

 
{
    "Human_Feeding_Mortality": 0.15
}

Immature_Duration

float

0

730

2

The number of days for larvae to develop into adult mosquitoes. The value is used to calculate mosquito development rate, which equals 1 / (value of this parameter). Development from immature to adult is not dependent on temperature.

 
{
    "Vector_Species_Params": [
        {
            "Name": "arabiensis",
            "Immature_Duration": 4
        }
    ]
}

Indoor_Feeding_Fraction

float

0

1

1

The fraction (dimensionless) of feeds in which mosquitoes feed on humans indoors; the fraction of feeds on humans that occur outdoors equals 1 - (value of this parameter).

 
{
    "Vector_Species_Params": [
        {
            "Name": "arabiensis",
            "Acquire_Modifier": 0.2,
            "Adult_Life_Expectancy": 20,
            "Anthropophily": 0.65,
            "Aquatic_Arrhenius_1": 84200000000,
            "Aquatic_Arrhenius_2": 8328,
            "Aquatic_Mortality_Rate": 0.1,
            "Days_Between_Feeds": 3,
            "Egg_Batch_Size": 100,
            "Habitats": [
                {
                    "Habitat_Type": "TEMPORARY_RAINFALL",
                    "Max_Larval_Capacity": 11250000000.0
                }
            ],
            "Immature_Duration": 2,
            "Indoor_Feeding_Fraction": 1,
            "Infected_Arrhenius_1": 117000000000,
            "Infected_Arrhenius_2": 8336,
            "Infected_Egg_Batch_Factor": 0.8,
            "Infectious_Human_Feed_Mortality_Factor": 1.5,
            "Male_Life_Expectancy": 10,
            "Temperature_Dependent_Feeding_Cycle": "NO_TEMPERATURE_DEPENDENCE",
            "Transmission_Rate": 0.5,
            "Vector_Sugar_Feeding_Frequency": "VECTOR_SUGAR_FEEDING_NONE"
        }
    ]
}

Infected_Egg_Batch_Factor

float

0

10

0.8

The dimensionless factor used to modify mosquito egg batch size in order to account for reduced fertility effects arising due to infection (e.g. when females undergo sporogony).

 
{
    "Vector_Species_Params": [
        {
            "Name": "arabiensis",
            "Acquire_Modifier": 0.2,
            "Adult_Life_Expectancy": 20,
            "Anthropophily": 0.65,
            "Aquatic_Arrhenius_1": 84200000000,
            "Aquatic_Arrhenius_2": 8328,
            "Aquatic_Mortality_Rate": 0.1,
            "Days_Between_Feeds": 3,
            "Egg_Batch_Size": 100,
            "Habitats": [
                {
                    "Habitat_Type": "TEMPORARY_RAINFALL",
                    "Max_Larval_Capacity": 11250000000.0
                }
            ],
            "Immature_Duration": 2,
            "Indoor_Feeding_Fraction": 1,
            "Infected_Arrhenius_1": 117000000000,
            "Infected_Arrhenius_2": 8336,
            "Infected_Egg_Batch_Factor": 0.8,
            "Infectious_Human_Feed_Mortality_Factor": 1.5,
            "Male_Life_Expectancy": 10,
            "Temperature_Dependent_Feeding_Cycle": "NO_TEMPERATURE_DEPENDENCE",
            "Transmission_Rate": 0.5,
            "Vector_Sugar_Feeding_Frequency": "VECTOR_SUGAR_FEEDING_NONE"
        }
    ]
}

Larval_Density_Dependence

enum

NA

NA

UNIFORM_WHEN_OVERPOPULATION

The functional form of mortality and growth delay for mosquito larvae based on population density. Possible values are:

UNIFORM_WHEN_OVERPOPULATION

Mortality is uniformly applied to all larvae when the population exceeds the specified carrying capacity for that habitat.

GRADUAL_INSTAR_SPECIFIC

Mortality and delayed growth are instar-specific, where the younger larvae are more susceptible to predation and competition from older larvae.

LARVAL_AGE_DENSITY_DEPENDENT_MORTALITY_ONLY

Mortality is based only on larval age.

DENSITY_DELAYED_GROWTH_NOT_MORTALITY

There is no mortality, only delayed growth in larvae.

NO_DENSITY_DEPENDENCE

There is no additional larval density-dependent mortality factor.

 
{
    "Larval_Density_Dependence": "GRADUAL_INSTAR_SPECIFIC"
}

Larval_Density_Mortality_Offset

float

0.0001

1000

0.1

An offset factor in the formula determining the larval-age-dependent mortality for the GRADUAL_INSTAR_SPECIFIC and LARVAL_AGE_DENSITY_DEPENDENT_MORTALITY_ONLY models.

 
{
    "Larval_Density_Mortality_Offset": 0.001
}

Larval_Density_Mortality_Scalar

float

0.01

1000

10

A scale factor in the formula determining the larval-age-dependent mortality for the GRADUAL_INSTAR_SPECIFIC and LARVAL_AGE_DENSITY_DEPENDENT_MORTALITY_ONLY models.

 
{
    "Larval_Density_Mortality_Scalar": 1.0
}

Mean_Egg_Hatch_Delay

float

0

120

0

The mean delay in egg hatch time, in days, from the time of oviposition. Set the delay distribution with Egg_Hatch_Delay_Distribution.

 
{
    "Egg_Hatch_Delay_Distribution": "EXPONENTIAL_DURATION",
    "Mean_Egg_Hatch_Delay": 2
}

Temperature_Dependent_Feeding_Cycle

enum

NA

NA

NO_TEMPERATURE_DEPENDENCE

The effect of temperature on the duration between blood feeds. Possible values are:

NO_TEMPERATURE_DEPENDENCE

No relation to temperature; days between feeds will be constant and specified by Days_Between_Feeds for each species.

ARRHENIUS_DEPENDENCE

Use the Arrhenius equation with parameters Cycle_Arrhenius_1 and Cycle_Arrhenius_2.

BOUNDED_DEPENDENCE

Use an equation bounded at 10 days at 15C and use Days_Between_Feeds to set the duration at 30C.

 
{
    "Vector_Species_Params": [
        {
            "Name": "arabiensis",
            "Temperature_Dependent_Feeding_Cycle": "BOUNDED_DEPENDENCE"
        }
    ]
}

Vector_Migration_Food_Modifier

float

0

3.40E+38

0

The preference of a vector to migrate toward a node currently occupied by humans, independent of the number of humans in the node. Used only when Vector_Sampling_Type is set to TRACK_ALL_VECTORS. Enable_Vector_Migration must be set to 1.

 
{
    "Vector_Migration_Food_Modifier": 1.0
}

Vector_Migration_Modifier_Equation

enum

NA

NA

LINEAR

The functional form of vector migration modifiers. Enable_Vector_Migration must be set to 1. Possible values are: LINEAR EXPONENTIAL

 
{
    "Vector_Migration_Modifier_Equation": "EXPONENTIAL"
}

Vector_Migration_Stay_Put_Modifier

float

0

3.40E+38

0

The preference of a vector to remain in its current node rather than migrate to another node. Used only when Vector_Sampling_Type is set to TRACK_ALL_VECTORS. Enable_Vector_Migration must be set to 1.

 
{
    "Vector_Migration_Stay_Put_Modifier": 1.0
}

Vector_Species_Params

array of JSON objects

NA

NA

NA

An array of JSON objects where each object represents the parameters for a particular species. The species listed will be those in the simulation.

 
{
    "Vector_Species_Params": [
        {
            "Name": "arabiensis",
            "Acquire_Modifier": 0.2,
            "Adult_Life_Expectancy": 20,
            "Anthropophily": 0.65,
            "Aquatic_Arrhenius_1": 84200000000,
            "Aquatic_Arrhenius_2": 8328,
            "Aquatic_Mortality_Rate": 0.1,
            "Days_Between_Feeds": 3,
            "Egg_Batch_Size": 100,
            "Habitats": [
                {
                    "Habitat_Type": "TEMPORARY_RAINFALL",
                    "Max_Larval_Capacity": 11250000000.0
                }
            ],
            "Immature_Duration": 2,
            "Indoor_Feeding_Fraction": 1,
            "Infected_Arrhenius_1": 117000000000,
            "Infected_Arrhenius_2": 8336,
            "Infected_Egg_Batch_Factor": 0.8,
            "Infectious_Human_Feed_Mortality_Factor": 1.5,
            "Male_Life_Expectancy": 10,
            "Temperature_Dependent_Feeding_Cycle": "NO_TEMPERATURE_DEPENDENCE",
            "Transmission_Rate": 0.5,
            "Vector_Sugar_Feeding_Frequency": "VECTOR_SUGAR_FEEDING_NONE"
        }
    ]
}

Vector_Sugar_Feeding_Frequency

enum

NA

NA

VECTOR_SUGAR_FEEDING_NONE

The frequency of sugar feeding by a female mosquito. Used is used in conjunction with the SugarTrap and OvipositionTrap interventions. Vector_Sampling_Type must be set to TRACK_ALL_VECTORS or SAMPLE_IND_VECTORS. Possible values are:

VECTOR_SUGAR_FEEDING_NONE

No sugar feeding.

VECTOR_SUGAR_FEEDING_ON_EMERGENCE_ONLY

Sugar feeding once at emergence.

VECTOR_SUGAR_FEEDING_EVERY_FEED

Sugar feeding occurs once per blood meal.

VECTOR_SUGAR_FEEDING_EVERY_DAY

Sugar feeding occurs every day.

 
{
    "Vector_Species_Params": [
        {
            "Name": "arabiensis",
            "Vector_Sugar_Feeding_Frequency": "VECTOR_SUGAR_FEEDING_NONE"
        }
    ]
}
Campaign parameters

The parameters described in this reference section can be added to the JSON (JavaScript Object Notation) formatted campaign file to determine the interventions that are used to control the spread of disease and where, when, how, and to whom the interventions are distributed. Additionally, the campaign file specifies the details of the outbreak of the disease itself. For more conceptual background on how to create a campaign file, see Creating campaigns.

In the configuration file, you must set the Enable_Interventions parameter to 1 and set the Campaign_Filename parameter to the name of the campaign file for the interventions to take place. The campaign file must be in the same directory as the configuration file.

The campaign file is hierarchically organized into logical groups of parameters that can have arbitrary levels of nesting. It contains an Events array of campaign events, each of which contains a JSON object describing the event coordinator, which in turn contains a nested JSON object describing the intervention. At the same level as the Events array is the boolean Use_Defaults to indicate whether or not to use the default values for required parameters that are not included in the file. It is common to include JSON keys for campaign name, event name, or intervention name; these are informational only and not used by EMOD.

The skeletal JSON example below illustrates the basic file structure (this does not include all required parameters for each object). Note that the nested JSON elements have been organized to best illustrate their hierarchy, but that many files in the EMOD GitHub repository list the parameters and nested objects differently.

{
    "Campaign_Name": "Campaign example",
    "Use_Defaults": 1,
    "Events": [{
        "Event_Name": "The first event",
        "class": "CampaignEvent",
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Intervention_Config": {
                "Intervention_Name": "The vaccine",
                "class": "SimpleVaccine"
            }
        }
    }, {
        "Event_Name": "The second event",
        "class": "CampaignEvent",
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Intervention_Config": {
                "Intervention_Name": "The disease outbreak",
                "class": "OutbreakIndividual"
            }
        }
    }]
}

The topics below contain only parameters available when using the malaria simulation type.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

Events and event coordinators

Campaign events determine when and where an intervention is distributed during a campaign. “When” can be the number of days after the beginning of the simulation or at a point during a particular calendar year. “Where” is the geographic node or nodes in which the event takes place.

Event coordinators are nested within the campaign event JSON object and determine who receives the intervention. “Who” is determined by filtering on age, gender, or on the individual properties configured in the demographics files, such as risk group or sociodemographic category. See Individual and node properties.

BroadcastCoordinatorEvent

Note

This campaign class and associated parameters are currently in beta release and have not yet been fully tested.

The BroadcastCoordinatorEvent coordinator class broadcasts the event you specify. This can be used with the campaign class, SurveillanceEventCoordinator, that can monitor and listen for events received from BroadcastCoordinatorEvent and then perform an action based on the broadcasted event. You can also use this for the reporting of the broadcasted events by setting the configuraton parameters, Report_Node_Event_Recorder and Report_Surveillance_Event_Recorder, which listen to events to be recorded. You must use this coordinator class with listeners that are operating on the same core. For more information, see Simulation core components.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Broadcast_Event

string

NA

NA

“”

The name of the event to be broadcast. This event must be set in the Custom_Coordinator_Events configuration parameter. This cannot be assigned an empty string for the event.

 
{
    "class": "CampaignEvent",
    "Start_Day": 5,
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Event_Coordinator_Config": {
        "class": "BroadcastCoordinatorEvent",
        "Coordinator_Name": "Coordnator_1",
        "Cost_To_Consumer": 10,
        "Broadcast_Event": "Coordinator_Event_1"
    }
}

Coordinator_Name

string

NA

NA

NA

The unique identifying coordinator name, which is useful with the output report, ReportCoordinatorEventRecorder.csv.

 
{
    "class": "CampaignEvent",
    "Start_Day": 25,
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Event_Coordinator_Config": {
        "class": "BroadcastCoordinatorEvent",
        "Coordinator_Name": "Coordinator_3",
        "Cost_To_Consumer": 30,
        "Broadcast_Event": "Coordinator_Event_3"
    }
}

Cost_To_Consumer

float

0

3.40282e+38

0

The unit cost of broadcasting the event.

 
{
    "class": "CampaignEvent",
    "Start_Day": 15,
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Event_Coordinator_Config": {
        "class": "BroadcastCoordinatorEvent",
        "Coordinator_Name": "Coordnator_2",
        "Cost_To_Consumer": 20,
        "Broadcast_Event": "Coordinator_Event_2"
    }
}

{
    "Events": [

        {
            "comment": "Broadcast Event to start Surveillance",
            "class": "CampaignEvent",
            "Start_Day": 2,
            "Nodeset_Config": { "class": "NodeSetAll" },
            "Event_Coordinator_Config": {
                "class": "BroadcastCoordinatorEvent",
                "Coordinator_Name" : "Coordnator_1",
                "Cost_To_Consumer" : 10,
                "Broadcast_Event" : "Start_ACF"
            }
        },
        {
            "comment": "Triggered by Broadcast_Event, stops itself by broadcasting Start_SIA_X Event",
            "class": "CampaignEvent",
            "Start_Day": 1,
            "Nodeset_Config": { "class": "NodeSetAll" },
            "Event_Coordinator_Config": {
                "class": "SurveillanceEventCoordinator",
                "Coordinator_Name" : "ACF_Counter",
                "Start_Trigger_Condition_List" : [ "Start_ACF" ],
                "Stop_Trigger_Condition_List" : [ 
                    "Start_SIA_2", 
                    "Start_SIA_4"
                ],
                "Duration" : -1,
                "Incidence_Counter" : {
                    "Counter_Type" : "PERIODIC",
                    "Counter_Period" : 365,
                    "Counter_Event_Type" : "INDIVIDUAL",
                    "Trigger_Condition_List" : ["HappyBirthday"],
                    "Node_Property_Restrictions" : [],
                    "Property_Restrictions_Within_Node" : [],
                    "Target_Demographic": "Everyone",
                    "Demographic_Coverage" : 1.0
                },
                "Responder" : {
                    "Threshold_Type" : "COUNT",
                    "Action_List" :
                    [
                        {
                            "Threshold" : 8,
                            "Event_Type" : "COORDINATOR",
                            "Event_To_Broadcast" : "Ind_Start_SIA_2"
                        },
                        {
                            "Threshold" : 5,
                            "Event_Type" : "COORDINATOR",
                            "Event_To_Broadcast" : "Ind_Start_SIA_4"
                        }
                    ]
                }
            }            
        }
    ],
    "Use_Defaults": 1
}
CalendarEventCoordinator

The CalendarEventCoordinator coordinator class distributes individual-level interventions at a specified time and coverage. See the following JSON example and table, which shows all available parameters for this event coordinator.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Demographic_Coverage

float

0

1

1

The fraction of individuals in the target demographic that will receive this intervention.

 
{
    "Demographic_Coverage": 1
}

Distribution_Coverages

array of floats

0

1

0

A vector of floats for the fraction of individuals that will receive this intervention in a CalendarEventCoordinator.

 
{
    "Distribution_Times": [
        100,
        200,
        400,
        800,
        1200
    ],
    "Distribution_Coverages": [
        0.01,
        0.05,
        0.1,
        0.2,
        1.0
    ]
}

Distribution_Times

array of integers

1

2147480000.0

0

A vector of integers for simulation times at which distribution of events occurs in a CalendarEventCoordinator. These times must be in ascending order.

 
{
    "Distribution_Times": [
        100,
        200,
        400,
        800,
        1200
    ],
    "Distribution_Coverages": [
        0.01,
        0.05,
        0.1,
        0.2,
        1.0
    ]
}

Intervention_Config

JSON object

NA

NA

NA

The nested JSON of the actual intervention to be distributed by this event coordinator.

 
{
    "Intervention_Config": {
        "class": "OutbreakIndividual",
        "Incubation_Period_Override": 1,
        "Outbreak_Source": "PrevalenceIncrease"
    }
}

Node_Property_Restrictions

array of JSON objects

NA

NA

NA

A list of the NodeProperty key:value pairs, as defined in the demographics file, that nodes must have to be targeted by the intervention. See NodeProperties and IndividualProperties parameters for more information.

You can specify AND and OR combinations of key:value pairs with this parameter.

The following example restricts the intervention to nodes that are urban and medium risk or rural and low risk.

{
    "Node_Property_Restrictions": [
        {
            "Place": "URBAN",
            "Risk": "MED"
        },
        {
            "Place": "RURAL",
            "Risk": "LOW"
        }
    ]
}

Property_Restrictions

array of JSON objects

NA

NA

NA

A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information.

To specify AND and OR combinations of key:value pairs, use Property_Restrictions_Within_Node. You cannot use both of these parameters in the same event coordinator.

 
{
    "Property_Restrictions": [
        "Risk:HIGH"
    ]
}

Property_Restrictions_Within_Node

array of JSON objects

NA

NA

NA

A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information.

This parameter allows you to specify AND and OR combinations of key:value pairs. You may specify individual property restrictions using either this parameter or Property_Restrictions, but not both.

The following example restricts the intervention to individuals who are urban and high risk or urban and medium risk.

{
    "Property_Restrictions_Within_Node": [
        {
            "Risk": "HIGH",
            "Geographic": "URBAN"
        },
        {
            "Risk": "MEDIUM",
            "Geographic": "URBAN"
        }
    ]
}

Target_Age_Max

float

0

9.3228e+35

9.3228e+35

The upper end of ages targeted for an intervention, in years. Used when Target_Demographic is set to ExplicitAgeRanges or ExplicitAgeRangesAndGender.

 
{
    "Target_Age_Max": 20,
    "Target_Age_Min": 10,
    "Target_Demographic": "ExplicitAgeRanges"
}

Target_Age_Min

float

0

9.3228e+35

0

The lower end of ages targeted for an intervention, in years. Used when Target_Demographic is set to ExplicitAgeRanges or ExplicitAgeRangesAndGender.

 
{
    "Target_Age_Max": 20,
    "Target_Age_Min": 10,
    "Target_Demographic": "ExplicitAgeRanges"
}

Target_Demographic

enum

NA

NA

Everyone

The target demographic group. Possible values are:

  • Everyone

  • ExplicitAgeRanges

  • ExplicitAgeRangesAndGender

  • ExplicitGender

  • ExplicitDiseaseState

 
{
    "Target_Age_Max": 20,
    "Target_Age_Min": 10,
    "Target_Demographic": "ExplicitAgeRanges"
}

Target_Gender

enum

NA

NA

All

Specifies the gender restriction for the intervention. Possible values are:

  • Male

  • Female

  • All

 
{
    "Target_Gender": "Male"
}

Target_Residents_Only

boolean

0

1

0

When set to true (1), the intervention is only distributed to individuals that began the simulation in the node (i.e. those that claim the node as their residence).

 
{
    "Target_Residents_Only": 1
}

Timesteps_Between_Repetitions

integer

-1

10000

-1

The repetition interval.

 
{
    "Timesteps_Between_Repetitions": 50
}

Targeting_Config

JSON object

NA

NA

NA

Be more selective of individuals by using the Targeting_Config classes.

 
{
    "Targeting_Config": {
        "class": "HasIntervention",
        "Is_Equal_To": 0,
        "Intervention_Name": "MyVaccine"
    }
}

{
    "Events": [{
        "class": "CampaignEvent",
        "Event_Name": "High-risk vaccination",
        "Start_Day": 1,
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Event_Coordinator_Config": {
            "class": "CalendarEventCoordinator",
            "Demographic_Coverage": 1,
            "Property_Restrictions": [
                "Risk:High"
            ],
            "Number_Repetitions": 1,
            "Timesteps_Between_Repetitions": 0,
            "Target_Demographic": "Everyone",
            "Target_Residents_Only": 1,
            "Distribution_Times": [100, 200, 400, 800, 1200],
            "Distribution_Coverages": [0.01, 0.05, 0.1, 0.2, 1.0],
            "Intervention_Config": {
                "Cost_To_Consumer": 0,
                "Vaccine_Take": 1,
                "Vaccine_Type": "AcquisitionBlocking",
                "class": "SimpleVaccine",
                "Waning_Config": {
                    "Initial_Effect": 1,
                    "Box_Duration": 1825,
                    "class": "WaningEffectBox"
                }
            }
        }
    }]
}
CampaignEvent

The CampaignEvent event class determines when to distribute the intervention based on the first day of the simulation. See the following JSON example and table, which shows all available parameters for this campaign event.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Event_Coordinator_Config

JSON object

NA

NA

NA

An object that specifies how the event is handled by the simulation. It specifies which Event Coordinator class will handle the event, and then configures the coordinator. This description starts with specifying class, and then the other fields depend on the class.

 
{
    "Event_Coordinator_Config": {
        "class": "StandardInterventionDistributionEventCoordinator",
        "Demographic_Coverage": 1.0
    }
}

Event_Name

string

NA

NA

NA

The optional name used to refer to this event as a means to differentiate it from others that use the same class.

 
{
    "Events": [
        {
            "Event_Name": "Outbreak",
            "class": "CampaignEvent",
            "Nodeset_Config": "NodeSetAll"
        }
    ]
}

Nodeset_Config

JSON object

NA

NA

NA

An object that specifies in which nodes the interventions will be distributed. Set to one of the Nodeset_Config classes.

 
{
    "Nodeset_Config": {
        "class": "NodeSetAll"
    }
}

Start_Day

float

0

3.40282e+38

1

The day of the simulation to activate the event’s event coordinator. Depending on the event coordinator and intervention, this may indicate when to distribute an intervention or when to begin listening for a trigger.

 
{
    "Start_Day": 0,
    "End_Day": 100
}

{
    "Events": [{
        "class": "CampaignEvent",
        "Event_Name": "Individual outbreak",
        "Start_Day": 1,
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Target_Demographic": "Everyone",
            "Demographic_Coverage": 1.0,
            "Intervention_Config": {
                "class": "OutbreakIndividual"
            }
        }
    }]
}
Nodeset_Config classes

The following classes determine in which nodes the event will occur.

NodeSetAll

The event will occur in all nodes in the simulation. This class has no associated parameters. For example,

{
    "Nodeset_Config": {
        "class": "NodeSetAll"
    }
}
NodeSetNodeList

The event will occur in the nodes listed by Node ID.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Node_List

array of integers

NA

NA

NA

A comma-separated list of node IDs in which this event will occur.

 
{
    "Nodeset_Config": {
        "class": "NodeSetNodeList",
        "Node_List": [
            1,
            5,
            27
        ]
    }
}
NodeSetPolygon

The event will occur in the nodes that fall within a given polygon.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Polygon_Format

enum

NA

NA

SHAPE

The type of polygon to create. Currently, SHAPE is the only supported value.

 
{
    "Nodeset_Config": {
        "class": "NodeSetPolygon",
        "Polygon_Format": "SHAPE",
        "Vertices": "-122.320726936496,47.6597902588541 -122.320406460197,47.6520988276782 -122.308388598985,47.6471314450438"
    }
}

Vertices

string

NA

NA

UNINTIALIZED STRING

Comma-separated list of latitude/longitude points that bound a polygon.

 
{
    "Nodeset_Config": {
        "class": "NodeSetPolygon",
        "Polygon_Format": "SHAPE",
        "Vertices": "-122.320726936496,47.6597902588541 -122.320406460197,47.6520988276782 -122.308388598985,47.6471314450438"
    }
}
CommunityHealthWorkerEventCoordinator

The CommunityHealthWorkerEventCoordinator coordinator class is used to model a health care worker’s ability to distribute interventions to the nodes and individual in their coverage area. This coordinator distributes a limited number of interventions per day, and can create a backlog of individuals or nodes requiring the intervention. For example, individual-level interventions could be distribution of drugs and node-level interventions could be spraying houses with insecticide.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Amount_In_Shipment

integer

1

2147480000.0

2147480000.0

The number of interventions (such as vaccine doses) that a health worker or clinic receives in a shipment. Interventions can only be distributed if they are in stock; stock is updated every Days_Between_Shipments with the Amount_In_Shipment.

 
{
    "Amount_In_Shipment": 10
}

Days_Between_Shipments

float

1

3.40282e+38

3.40E+38

The number of days to wait before a clinic or health worker receives a new shipment of interventions (such as vaccine doses). Interventions can only be distributed if they are in stock; stock is updated every Days_Between_Shipments with the Amount_In_Shipment.

 
{
    "Days_Between_Shipments": 30
}

Demographic_Coverage

float

0

1

1

The fraction of individuals in the target demographic that will receive this intervention.

 
{
    "Demographic_Coverage": 1
}

Duration

float

0

3.40282e+38

3.40E+38

The number of days for an event coordinator to be active before it expires.

 
{
    "Duration": 65
}

Initial_Amount_Constant

float

0

3.40282E+38

-1

The initial amount to use for all health workers when Initial_Amount_Distribution is set to CONSTANT_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "CONSTANT_DISTRIBUTION",
    "Initial_Amount_Constant": 8
}

Initial_Amount_Distribution

enum

NA

NA

NOT_INITIALIZED

The distribution type to use for assigning the initial amount of interventions in stock. Each assigned value is a random draw from the distribution.

Possible values are:

NOT_INITIALIZED

No distribution set.

CONSTANT_DISTRIBUTION

Use the same value for each individual. Set Initial_Amount_Constant.

UNIFORM_DISTRIBUTION

Use a uniform distribution with a given minimum and maximum. Set Initial_Amount_Max and Initial_Amount_Min.

GAUSSIAN_DISTRIBUTION

The distribution is Gaussian (or normal). Values are resampled to ensure >= 0. Set Initial_Amount_Gaussian_Mean and Initial_Amount_Gaussian_Std_Dev.

EXPONENTIAL_DISTRIBUTION

The distribution is exponential with a given mean. Set Initial_Amount_Exponential.

WEIBULL_DISTRIBUTION

Use a Weibull distribution with a given shape and scale. Set Initial_Amount_Kappa and Initial_Amount_Lambda.

LOG_NORMAL_DISTRIBUTION

Use a log-normal distribution with a given mean and standard deviation of the natural log. Set Initial_Amount_Log_Normal_Mu and Initial_Amount_Log_Normal_Sigma.

POISSON_DISTRIBUTION

Use a Poisson distribution with a given mean. Set Initial_Amount_Poisson_Mean.

DUAL_CONSTANT_DISTRIBUTION

Use a distribution where some individuals are set to a value of zero and the rest to a given value. Set Initial_Amount_Proportion_0 and Peak_2_Value. This distribution does not use the parameters set for CONSTANT_DISTRIBUTION.

DUAL_EXPONENTIAL_DISTRIBUTION

Use two exponential distributions with given means. Set Initial_Amount_Mean_1, Initial_Amount_Mean_2, and Initial_Amount_Proportion_1. This distribution does not use the parameters set for EXPONENTIAL_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Initial_Amount_Mean_1": 4,
    "Initial_Amount_Mean_2": 12,
    "Initial_Amount_Proportion_1": 0.2
}

Initial_Amount_Exponential

float

0

3.40282E+38

-1

The mean of the initial amount of intervention in stock when Initial_Amount_Distribution is set to EXPONENTIAL_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "EXPONENTIAL_DISTRIBUTION",
    "Initial_Amount_Exponential": 4.25
}

Initial_Amount_Gaussian_Mean

float

0

3.40282E+38

-1

The mean of the initial amount of intervention in stock when Initial_Amount_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Initial_Amount_Gaussian_Mean": 8,
    "Initial_Amount_Gaussian_Std_Dev": 1.5
}

Initial_Amount_Gaussian_Std_Dev

float

1.17549E-38

3.40282E+38

-1

The standard deviation of the initial amount of intervention in stock when Initial_Amount_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Initial_Amount_Gaussian_Mean": 8,
    "Initial_Amount_Gaussian_Std_Dev": 1.5
}

Initial_Amount_Kappa

float

1.17549E-38

3.40282E+38

-1

The shape value for the initial amount of intervention in stock when Initial_Amount_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "WEIBULL_DISTRIBUTION",
    "Initial_Amount_Kappa": 0.9,
    "Initial_Amount_Lambda": 1.5
}

Initial_Amount_Lambda

float

1.17549E-38

3.40282E+38

-1

The scale value for the initial amount of intervention in stock when Initial_Amount_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "WEIBULL_DISTRIBUTION",
    "Initial_Amount_Kappa": 0.9,
    "Initial_Amount_Lambda": 1.5
}

Initial_Amount_Log_Normal_Mu

float

-3.40282e+38

1.70141e+38

3.40282E+38

The mean of the natural log of the initial amount of intervention in stock when Initial_Amount_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Initial_Amount_Log_Normal_Mu": 9,
    "Initial_Amount_Log_Normal_Sigma": 2
}

Initial_Amount_Log_Normal_Sigma

float

-3.40282e+38

3.40282E+38

1

The standard deviation of the natural log of the initial amount of intervention in stock when Initial_Amount_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Initial_Amount_Log_Normal_Mu": 9,
    "Initial_Amount_Log_Normal_Sigma": 2
}

Initial_Amount_Max

float

0

3.40282E+38

-1

The maximum initial amount of intervention in stock when Initial_Amount_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "UNIFORM_DISTRIBUTION",
    "Initial_Amount_Min": 2,
    "Initial_Amount_Max": 7
}

Initial_Amount_Mean_1

float

1.17549E-38

3.40282E+38

-1

The mean of the first exponential distribution when Initial_Amount_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Initial_Amount_Mean_1": 4,
    "Initial_Amount_Mean_2": 12,
    "Initial_Amount_Proportion_1": 0.2
}

Initial_Amount_Mean_2

float

1.17549E-38

3.40282E+38

-1

The mean of the second exponential distribution when Initial_Amount_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Initial_Amount_Mean_1": 4,
    "Initial_Amount_Mean_2": 12,
    "Initial_Amount_Proportion_1": 0.2
}

Initial_Amount_Min

float

0

3.40282E+38

-1

The minimum of initial amount of intervention in stock when Initial_Amount_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "UNIFORM_DISTRIBUTION",
    "Initial_Amount_Min": 2,
    "Initial_Amount_Max": 7
}

Initial_Amount_Peak_2_Value

float

0

3.40282E+38

-1

The initial amount in stock to assign to the remaining health workers when Initial_Amount_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Initial_Amount_Proportion_0": 0.25,
    "Initial_Amount_Peak_2_Value": 5
}

Initial_Amount_Poisson_Mean

float

0

3.40282E+38

-1

The mean of the initial amount of intervention in stock when Initial_Amount_Distribution is set to the POISSON_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "POISSON_DISTRIBUTION",
    "Initial_Amount_Poisson_Mean": 5
}

Initial_Amount_Proportion_0

float

0

1

-1

The proportion of health workers to assign a value of zero stock when Initial_Amount_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Initial_Amount_Proportion_0": 0.25,
    "Initial_Amount_Peak_2_Value": 5
}

Initial_Amount_Proportion_1

float

0

1

-1

The proportion of health workers in the first exponential distribution when Initial_Amount_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Initial_Amount_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Initial_Amount_Mean_1": 4,
    "Initial_Amount_Mean_2": 12,
    "Initial_Amount_Proportion_1": 0.2
}

Intervention_Config

JSON object

NA

NA

NA

The nested JSON of the actual intervention to be distributed by this event coordinator.

 
{
    "Intervention_Config": {
        "class": "BroadcastEvent",
        "Broadcast_Event": "EventFromIntervention"
    }
}

Max_Distributed_Per_Day

integer

1

2147480000.0

2147480000.0

The maximum number of interventions (such as vaccine doses) that can be distributed by health workers or clinics in a given day.

 
{
    "Max_Distributed_Per_Day": 1
}

Max_Stock

integer

0

2147480000.0

2147480000.0

The maximum number of interventions (such as vaccine doses) that can be stored by a health worker or clinic. If the amount of interventions in a new shipment and current stock exceeds this value, only the number of interventions specified by this value will be stored.

 
{
    "Max_Stock": 12
}

Node_Property_Restrictions

array of JSON objects

NA

NA

NA

A list of the NodeProperty key:value pairs, as defined in the demographics file, that nodes must have to be targeted by the intervention. See NodeProperties and IndividualProperties parameters for more information.

You can specify AND and OR combinations of key:value pairs with this parameter.

The following example restricts the intervention to nodes that are urban and medium risk or rural and low risk.

{
    "Node_Property_Restrictions": [
        {
            "Place": "URBAN",
            "Risk": "MED"
        },
        {
            "Place": "RURAL",
            "Risk": "LOW"
        }
    ]
}

Property_Restrictions

array of JSON objects

NA

NA

NA

A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information.

To specify AND and OR combinations of key:value pairs, use Property_Restrictions_Within_Node. You cannot use both of these parameters in the same event coordinator.

 
{
    "Property_Restrictions": [
        "Risk:HIGH"
    ]
}

Property_Restrictions_Within_Node

array of JSON objects

NA

NA

NA

A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information.

This parameter allows you to specify AND and OR combinations of key:value pairs. You may specify individual property restrictions using either this parameter or Property_Restrictions, but not both.

The following example restricts the intervention to individuals who are urban and high risk or urban and medium risk.

{
    "Property_Restrictions_Within_Node": [
        {
            "Risk": "HIGH",
            "Geographic": "URBAN"
        },
        {
            "Risk": "MEDIUM",
            "Geographic": "URBAN"
        }
    ]
}

Target_Age_Min

float

0

9.3228e+35

0

The lower end of ages targeted for an intervention, in years. Used when Target_Demographic is set to ExplicitAgeRanges or ExplicitAgeRangesAndGender.

 
{
    "Target_Age_Max": 20,
    "Target_Age_Min": 10,
    "Target_Demographic": "ExplicitAgeRanges"
}

Target_Demographic

enum

NA

NA

Everyone

The target demographic group. Possible values are:

  • Everyone

  • ExplicitAgeRanges

  • ExplicitAgeRangesAndGender

  • ExplicitGender

  • ExplicitDiseaseState

 
{
    "Target_Age_Max": 20,
    "Target_Age_Min": 10,
    "Target_Demographic": "ExplicitAgeRanges"
}

Target_Gender

enum

NA

NA

All

Specifies the gender restriction for the intervention. Possible values are:

  • Male

  • Female

  • All

 
{
    "Target_Gender": "Male"
}

Target_Residents_Only

boolean

0

1

0

When set to true (1), the intervention is only distributed to individuals that began the simulation in the node (i.e. those that claim the node as their residence).

 
{
    "Target_Residents_Only": 1
}

Trigger_Condition_List

array of strings

NA

NA

NA

The list of events that are of interest to the community health worker (CHW). If one of these events occurs, the individual or node is put into a queue to receive the CHW’s intervention. The CHW processes the queue when the event coordinator is updated. See Event list for possible values.

 
{
    "Trigger_Condition_List": [
        "ListenForEvent"
    ]
}

Waiting_Period

float

0

3.40282e+38

3.40E+38

The number of days a person or node can be in the queue waiting to get the intervention from the community health worker (CHW). If a person or node is in the queue, they will not be re-added to the queue if the event that would add them to the queue occurs. This allows them to maintain their priority, however they could be removed from the queue due to this waiting period.

 
{
    "Waiting_Period": 15
}

Targeting_Config

JSON object

NA

NA

NA

Be more selective of individuals by using the Targeting_Config classes.

 
{
    "Targeting_Config": {
        "class": "HasIntervention",
        "Is_Equal_To": 0,
        "Intervention_Name": "MyVaccine"
    }
}

{
  "Events": [
    {
      "class": "CampaignEvent",
      "Start_Day": 1,
      "Nodeset_Config": {
        "class": "NodeSetNodeList",
        "Node_List": [
          2,
          3,
          4
        ]
      },
      "Event_Coordinator_Config": {
        "class": "CommunityHealthWorkerEventCoordinator",
        "Duration": 400,
        "Distribution_Rate": 25,
        "Waiting_Period": 10,
        "Days_Between_Shipments": 25,
        "Amount_In_Shipment": 250,
        "Max_Stock": 1000,
        "Initial_Amount_Distribution": "CONSTANT_DISTRIBUTION",
        "Initial_Amount_Constant": 500,
        "Target_Demographic": "Everyone",
        "Demographic_Coverage": 1.0,
        "Property_Restrictions": [],
        "Target_Residents_Only": 0,
        "Trigger_Condition_List": [
          "NewInfectionEvent"
        ],
        "Intervention_Config": {
          "class": "SimpleVaccine",
          "Cost_To_Consumer": 10.0,
          "Vaccine_Take": 1,
          "Vaccine_Type": "AcquisitionBlocking",
          "Waning_Config": {
            "class": "WaningEffectBox",
            "Initial_Effect": 1,
            "Box_Duration": 200
          }
        }
      }
    }
  ]
}
CoverageByNodeEventCoordinator

The CoverageByNodeEventCoordinator coordinator class distributes individual-level interventions and is similar to the StandardInterventionDistributionEventCoordinator, but adds the ability to specify different demographic coverages by node. If no coverage has been specified for a particular node ID, the coverage will be zero. See the following JSON example and table, which shows all available parameters for this event coordinator.

Note

This can only be used with individual-level interventions, but EMOD will not produce an error if you attempt to use it with an node-level intervention.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Coverage_By_Node

array of arrays

NA

NA

NA

An array of (nodeID, coverage) pairs configuring the demographic coverage of interventions by node for the targeted populations. The coverage value must be a float between 0 and 1.

 
{
    "Event_Coordinator_Config": {
        "Coverage_By_Node": [
            [
                1,
                0.6
            ],
            [
                2,
                0.9
            ],
            [
                3,
                0.1
            ]
        ]
    }
}

Demographic_Coverage

float

0

1

1

The fraction of individuals in the target demographic that will receive this intervention.

 
{
    "Demographic_Coverage": 1
}

Intervention_Config

JSON object

NA

NA

NA

The nested JSON of the actual intervention to be distributed by this event coordinator.

 
{
    "Intervention_Config": {
        "class": "OutbreakIndividual",
        "Incubation_Period_Override": 1,
        "Outbreak_Source": "PrevalenceIncrease"
    }
}

Node_Property_Restrictions

array of JSON objects

NA

NA

NA

A list of the NodeProperty key:value pairs, as defined in the demographics file, that nodes must have to be targeted by the intervention. See NodeProperties and IndividualProperties parameters for more information.

You can specify AND and OR combinations of key:value pairs with this parameter.

The following example restricts the intervention to nodes that are urban and medium risk or rural and low risk.

{
    "Node_Property_Restrictions": [
        {
            "Place": "URBAN",
            "Risk": "MED"
        },
        {
            "Place": "RURAL",
            "Risk": "LOW"
        }
    ]
}

Number_Repetitions

integer

-1

10000

1

The number of times an intervention is given, used with Timesteps_Between_Repetitions. A value of -1 implies an infinite number of repetitions.

 
{
    "Event_Coordinator_Config": {
        "Intervention_Config": {
            "class": "Outbreak",
            "Num_Cases": 1
        },
        "Number_Repetitions": 10,
        "Timesteps_Between_Repetitions": 50,
        "class": "StandardInterventionDistributionEventCoordinator"
    }
}

Property_Restrictions

array of JSON objects

NA

NA

NA

A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information.

To specify AND and OR combinations of key:value pairs, use Property_Restrictions_Within_Node. You cannot use both of these parameters in the same event coordinator.

 
{
    "Property_Restrictions": [
        "Risk:HIGH"
    ]
}

Property_Restrictions_Within_Node

array of JSON objects

NA

NA

NA

A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information.

This parameter allows you to specify AND and OR combinations of key:value pairs. You may specify individual property restrictions using either this parameter or Property_Restrictions, but not both.

The following example restricts the intervention to individuals who are urban and high risk or urban and medium risk.

{
    "Property_Restrictions_Within_Node": [
        {
            "Risk": "HIGH",
            "Geographic": "URBAN"
        },
        {
            "Risk": "MEDIUM",
            "Geographic": "URBAN"
        }
    ]
}

Target_Age_Min

float

0

9.3228e+35

0

The lower end of ages targeted for an intervention, in years. Used when Target_Demographic is set to ExplicitAgeRanges or ExplicitAgeRangesAndGender.

 
{
    "Target_Age_Max": 20,
    "Target_Age_Min": 10,
    "Target_Demographic": "ExplicitAgeRanges"
}

Target_Demographic

enum

NA

NA

Everyone

The target demographic group. Possible values are:

  • Everyone

  • ExplicitAgeRanges

  • ExplicitAgeRangesAndGender

  • ExplicitGender

  • ExplicitDiseaseState

 
{
    "Target_Age_Max": 20,
    "Target_Age_Min": 10,
    "Target_Demographic": "ExplicitAgeRanges"
}

Target_Gender

enum

NA

NA

All

Specifies the gender restriction for the intervention. Possible values are:

  • Male

  • Female

  • All

 
{
    "Target_Gender": "Male"
}

Target_Residents_Only

boolean

0

1

0

When set to true (1), the intervention is only distributed to individuals that began the simulation in the node (i.e. those that claim the node as their residence).

 
{
    "Target_Residents_Only": 1
}

Timesteps_Between_Repetitions

integer

-1

10000

-1

The repetition interval.

 
{
    "Timesteps_Between_Repetitions": 50
}

Targeting_Config

JSON object

NA

NA

NA

Be more selective of individuals by using the Targeting_Config classes.

 
{
    "Targeting_Config": {
        "class": "HasIntervention",
        "Is_Equal_To": 0,
        "Intervention_Name": "MyVaccine"
    }
}

{
    "Events": [{
        "class": "CampaignEvent",
        "Start_Day": 0,
        "Nodeset_Config": {
            "Node_List": [
                1,
                2,
                3
            ],
            "class": "NodeSetNodeList"
        },
        "Event_Coordinator_Config": {
            "class": "CoverageByNodeEventCoordinator",
            "Target_Demographic": "Everyone",
            "Coverage_By_Node": [
                [1, 0.6],
                [2, 0.9],
                [3, 0.1]
            ],
            "Intervention_Config": {
                "Cost_To_Consumer": 10.0,
                "Reduced_Transmit": 0,
                "Vaccine_Take": 1,
                "Vaccine_Type": "AcquisitionBlocking",
                "Waning_Config": {
                    "Box_Duration": 3650,
                    "Initial_Effect": 1,
                    "class": "WaningEffectBox"
                },
                "class": "SimpleVaccine"
            }
        }
    }]
}
DelayEventCoordinator

Note

This campaign class and associated parameters are currently in beta release and have not yet been fully tested.

The DelayEventCoordinator coordinator class insert delays into coordinator event chains. This campaign event is typically used with BroadcastCoordinatorEvent to broadcast events after the delays.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Coordinator_Name

string

NA

NA

DelayEventCoordinator

The unique identifying coordinator name, which is useful with the output report, ReportCoordinatorEventRecorder.csv.

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "DelayEventCoordinator_10",
        "Delay_Complete_Event": "Completion_Delayed_Coordinator_Event_1",
        "Delay_Period_Distribution": "FIXED_DURATION",
        "Delay_Period_Fixed": 25,
        "Duration": 100,
        "Start_Trigger_Condition_List": [
            "Coordinator_Event_1"
        ],
        "Stop_Trigger_Condition_List": [],
        "class": "DelayEventCoordinator"
    },
    "Event_Name": "Delay",
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "Target_Demographic": "Everyone",
    "class": "CampaignEvent",
    "comment": "Delay"
}

Delay_Complete_Event

string

NA

NA

NA

The delay completion event to be included in the ReportCoordinatorEventRecorder.csv output report, upon completion of the delay period.

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "DelayEventCoordinator_10",
        "Delay_Complete_Event": "Completion_Delayed_Coordinator_Event_1",
        "Delay_Distribution": "GAUSSIAN_DURATION",
        "Delay_Period_Mean": 25,
        "Delay_Period_Std_Dev": 5,
        "Duration": 100,
        "Start_Trigger_Condition_List": [
            "Coordinator_Event_1"
        ],
        "Stop_Trigger_Condition_List": [],
        "class": "DelayEventCoordinator"
    },
    "Event_Name": "Delay",
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "Target_Demographic": "Everyone",
    "class": "CampaignEvent",
    "comment": "Delay"
}

Delay_Period_Constant

float

0

3.40282E+38

-1

The delay period to use for all interventions, in days, when Delay_Period_Distribution is set to CONSTANT_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "CONSTANT_DISTRIBUTION",
    "Delay_Period_Constant": 8
}

Delay_Period_Distribution

enum

NA

NA

NOT_INITIALIZED

The distribution type to use for assigning the delay period for distributing interventions. Each assigned value is a random draw from the distribution.

Possible values are:

NOT_INITIALIZED

No distribution set.

CONSTANT_DISTRIBUTION

Use the same value for each individual. Set Delay_Period_Constant.

UNIFORM_DISTRIBUTION

Use a uniform distribution with a given minimum and maximum. Set Delay_Period_Max and Delay_Period_Min.

GAUSSIAN_DISTRIBUTION

The distribution is Gaussian (or normal). Values are resampled to ensure >= 0. Set Delay_Period_Gaussian_Mean and Delay_Period_Gaussian_Std_Dev.

EXPONENTIAL_DISTRIBUTION

The distribution is exponential with a given mean. Set Delay_Period_Exponential.

WEIBULL_DISTRIBUTION

Use a Weibull distribution with a given shape and scale. Set Delay_Period_Kappa and Delay_Period_Lambda.

LOG_NORMAL_DISTRIBUTION

Use a log-normal distribution with a given mean and standard deviation of the natural log. Set Delay_Period_Log_Normal_Mu and Delay_Period_Log_Normal_Sigma.

POISSON_DISTRIBUTION

Use a Poisson distribution with a given mean. Set Delay_Period_Poisson_Mean.

DUAL_CONSTANT_DISTRIBUTION

Use a distribution where some individuals are set to a value of zero and the rest to a given value. Set Delay_Period_Proportion_0 and Peak_2_Value. This distribution does not use the parameters set for CONSTANT_DISTRIBUTION.

DUAL_EXPONENTIAL_DISTRIBUTION

Use two exponential distributions with given means. Set Delay_Period_Mean_1, Delay_Period_Mean_2, and Delay_Period_Proportion_1. This distribution does not use the parameters set for EXPONENTIAL_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Delay_Period_Gaussian_Mean": 8,
    "Delay_Period_Gaussian_Std_Dev": 1.5
}

Delay_Period_Exponential

float

0

3.40282E+38

-1

The mean of the delay period, in days, when Delay_Period_Distribution is set to EXPONENTIAL_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "EXPONENTIAL_DISTRIBUTION",
    "Delay_Period_Exponential": 4.25
}

Delay_Period_Gaussian_Mean

float

0

3.40282E+38

-1

The mean of the delay period, in days, when Delay_Period_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Delay_Period_Gaussian_Mean": 8,
    "Delay_Period_Gaussian_Std_Dev": 1.5
}

Delay_Period_Gaussian_Std_Dev

float

1.17549E-38

3.40282E+38

-1

The standard deviation of the delay period, in days, when Delay_Period_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Delay_Period_Gaussian_Mean": 8,
    "Delay_Period_Gaussian_Std_Dev": 1.5
}

Delay_Period_Kappa

float

1.17549E-38

3.40282E+38

-1

The shape value for the delay period, in days, when Delay_Period_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "WEIBULL_DISTRIBUTION",
    "Delay_Period_Kappa": 0.9,
    "Delay_Period_Lambda": 1.5
}

Delay_Period_Lambda

float

1.17549E-38

3.40282E+38

-1

The scale value for the delay period, in days, when Delay_Period_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "WEIBULL_DISTRIBUTION",
    "Delay_Period_Kappa": 0.9,
    "Delay_Period_Lambda": 1.5
}

Delay_Period_Log_Normal_Mu

float

-3.40282e+38

1.70141e+38

3.40282e+38

The mean of the natural log of the delay period, in days, when Delay_Period_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Delay_Period_Log_Normal_Mu": 9,
    "Delay_Period_Log_Normal_Sigma": 2
}

Delay_Period_Log_Normal_Sigma

float

-3.40282e+38

1.70141e+38

3.40282E+38

The standard deviation of the natural log of the delay period, in days, when Delay_Period_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Delay_Period_Log_Normal_Mu": 9,
    "Delay_Period_Log_Normal_Sigma": 2
}

Delay_Period_Max

float

0

3.40282E+38

-1

The maximum delay period, in days, when Delay_Period_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "UNIFORM_DISTRIBUTION",
    "Delay_Period_Min": 2,
    "Delay_Period_Max": 7
}

Delay_Period_Mean_1

float

1.17549E-38

3.40282E+38

-1

The mean of the first exponential distribution, in days, when Delay_Period_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Delay_Period_Mean_1": 4,
    "Delay_Period_Mean_2": 12,
    "Delay_Period_Proportion_1": 0.2
}

Delay_Period_Mean_2

float

1.17549E-38

3.40282E+38

-1

The mean of the second exponential distribution, in days, when Delay_Period_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Delay_Period_Mean_1": 4,
    "Delay_Period_Mean_2": 12,
    "Delay_Period_Proportion_1": 0.2
}

Delay_Period_Min

float

0

3.40282E+38

-1

The minimum delay period, in days, when Delay_Period_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "UNIFORM_DISTRIBUTION",
    "Delay_Period_Min": 2,
    "Delay_Period_Max": 7
}

Delay_Period_Peak_2_Value

float

0

3.40282E+38

-1

The delay period value, in days, to assign to the remaining interventions when Delay_Period_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Delay_Period_Proportion_0": 0.25,
    "Delay_Period_Peak_2_Value": 5
}

Delay_Period_Poisson_Mean

float

0

3.40282E+38

-1

The mean of the delay period, in days, when Delay_Period_Distribution is set to POISSON_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "POISSON_DISTRIBUTION",
    "Delay_Period_Poisson_Mean": 5
}

Delay_Period_Proportion_0

float

0

1

-1

The proportion of interventions to assign a value of zero delay when Delay_Period_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Delay_Period_Proportion_0": 0.25,
    "Delay_Period_Peak_2_Value": 5
}

Delay_Period_Proportion_1

float

0

1

-1

The proportion of interventions in the first exponential distribution when Delay_Period_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Delay_Period_Mean_1": 4,
    "Delay_Period_Mean_2": 12,
    "Delay_Period_Proportion_1": 0.2
}

Duration

float

-1

3.40282e+38

-1

The number of days from when the delay event coordinator was created by the campaign event. Once the number of days has passed, the delay event coordinator will unregister for events and expire. The default value of ‘-1’ = never expire.

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "DelayEventCoordinator_10",
        "Delay_Complete_Event": "Completion_Delayed_Coordinator_Event_1",
        "Delay_Period_Distribution": "FIXED_DURATION",
        "Delay_Period_Fixed": 25,
        "Duration": 100,
        "Start_Trigger_Condition_List": [
            "Coordinator_Event_1"
        ],
        "Stop_Trigger_Condition_List": [],
        "class": "DelayEventCoordinator"
    },
    "Event_Name": "Delay",
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "Target_Demographic": "Everyone",
    "class": "CampaignEvent"
}

Start_Trigger_Condition_List

array of strings

NA

NA

NA

The start trigger event condition list contains events defined in the Custom_Coordinator_Events config parameter that will trigger to start a new delay. If the delay event coordinator has already been triggered and is currently waiting for the duration of a delay, it will then ignore a new delay event. The list cannot be empty.

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "DelayEventCoordinator_10",
        "Delay_Complete_Event": "Completion_Delayed_Coordinator_Event_1",
        "Delay_Period_Distribution": "FIXED_DURATION",
        "Delay_Period_Fixed": 25,
        "Duration": 100,
        "Start_Trigger_Condition_List": [
            "Coordinator_Event_1"
        ],
        "Stop_Trigger_Condition_List": [],
        "class": "DelayEventCoordinator"
    },
    "Event_Name": "Delay",
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "Target_Demographic": "Everyone",
    "class": "CampaignEvent",
    "comment": "Delay"
}

Stop_Trigger_Condition_List

array of strings

NA

NA

NA

The stop trigger event condition list contains events defined in the Custom_Coordinator_Events config parameter that will trigger to stop a delay period, but does not stop the delay event coordinator. The event is not broadcast.

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "DelayEventCoordinator_10",
        "Delay_Complete_Event": "Completion_Delayed_Coordinator_Event_1",
        "Delay_Period_Distribution": "FIXED_DURATION",
        "Delay_Period_Fixed": 25,
        "Duration": 100,
        "Start_Trigger_Condition_List": [
            "Coordinator_Event_1"
        ],
        "Stop_Trigger_Condition_List": [],
        "class": "DelayEventCoordinator"
    },
    "Event_Name": "Delay",
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "Target_Demographic": "Everyone",
    "class": "CampaignEvent",
    "comment": "Delay"
}

{
  "Events": [
    {
      "comment": "Trigger to start Delay",
      "class": "CampaignEvent",
      "Start_Day": 2,
      "Nodeset_Config": {
        "class": "NodeSetAll"
      },
      "Event_Coordinator_Config": {
        "class": "BroadcastCoordinatorEvent",
        "Coordinator_Name": "Coordnator_1",
        "Cost_To_Consumer": 10,
        "Broadcast_Event": "Coordinator_Event_1"
      }
    },
    {
      "comment": "restart Delay",
      "class": "CampaignEvent",
      "Start_Day": 10,
      "Nodeset_Config": {
        "class": "NodeSetAll"
      },
      "Event_Coordinator_Config": {
        "class": "BroadcastCoordinatorEvent",
        "Coordinator_Name": "Coordnator_1",
        "Cost_To_Consumer": 10,
        "Broadcast_Event": "Coordinator_Event_1"
      }
    },
    {
      "comment": "Delay",
      "Event_Coordinator_Config": {
        "class": "DelayEventCoordinator",
        "Coordinator_Name": "DelayEventCoordinator_10",
        "Start_Trigger_Condition_List": [
          "Coordinator_Event_1"
        ],
        "Stop_Trigger_Condition_List": [],
        "Duration": 100,
        "Delay_Period_Distribution": "CONSTANT_DISTRIBUTION",
        "Delay_Complete_Event": "Completion_Delayed_Coordinator_Event_1",
        "Delay_Period_Constant": 25
      },
      "Event_Name": "Delay",
      "Nodeset_Config": {
        "class": "NodeSetAll"
      },
      "Start_Day": 1,
      "Target_Demographic": "Everyone",
      "class": "CampaignEvent"
    }
  ],
  "Use_Defaults": 1
}
IncidenceEventCoordinator

The IncidenceEventCoordinator coordinator class distributes interventions based on the number of events counted over a period of time.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Action_List

array of JSON objects

NA

NA

NA

List (array) of JSON objects, including the values specified in the following parameters: Threshold, Event_Type, Event_To_Broadcast.

 
{
    "Action_List": [
        {
            "Event_To_Broadcast": "Action_Event_1",
            "Event_Type": "COORDINATOR",
            "Threshold": 20
        },
        {
            "Event_To_Broadcast": "Action_Event_2",
            "Event_Type": "COORDINATOR",
            "Threshold": 30
        }
    ],
    "Threshold_Type": "COUNT"
}

Count_Events_For_Num_Timesteps

integer

1

2.15E+00

1

If set to true (1), then the waning effect values, as specified in the Effect_List list of objects for the WaningEffectCombo classes, are added together. If set to false (0), the waning effect values are multiplied. The resulting waning effect value cannot be greater than 1.

 
{
    "Incidence_Counter": {
        "Count_Events_For_Num_Timesteps": 5,
        "Trigger_Condition_List": [
            "PropertyChange"
        ],
        "Node_Property_Restrictions": [
            {
                "Risk": "HIGH"
            }
        ],
        "Target_Demographic": "Everyone",
        "Demographic_Coverage": 0.6,
        "Property_Restrictions_Within_Node": [
            {
                "Accessibility": "YES"
            }
        ]
    }
}

Event_To_Broadcast

string

NA

NA

NA

The action event to occur when the specified trigger value in the Threshold parameter is met. At least one action must be defined for Event_To_Broadcast. The events contained in the list can be built-in events (see Event list for possible events).

 
{
    "Threshold_Type": "COUNT",
    "Action_List": [
        {
            "Threshold": 20,
            "Event_To_Broadcast": "Action_Event_1"
        },
        {
            "Threshold": 30,
            "Event_To_Broadcast": "Action_Event_2"
        }
    ]
}

Event_Type

enum

NA

NA

INDIVIDUAL

The type of event to be broadcast. Possible values are:

  • COORDINATOR

  • INDIVIDUAL

  • NODE

 
{
    "Action_List": [
        {
            "Event_To_Broadcast": "Action_Event_1",
            "Event_Type": "COORDINATOR",
            "Threshold": 20
        },
        {
            "Event_To_Broadcast": "Action_Event_2",
            "Event_Type": "COORDINATOR",
            "Threshold": 30
        }
    ],
    "Threshold_Type": "COUNT"
}

Incidence_Counter

array of JSON objects

NA

NA

NA

List of JSON objects for specifying the conditions and parameters that must be met for an incidence to be counted. Some of the values are specified in the following parameters: Count_Events_For_Num_Timesteps, Trigger_Condition_List, Node_Property_Restrictions.

 
{
    "Incidence_Counter": {
        "Count_Events_For_Num_Timesteps": 5,
        "Trigger_Condition_List": [
            "PropertyChange"
        ],
        "Node_Property_Restrictions": [
            {
                "Risk": "HIGH"
            }
        ],
        "Target_Demographic": "Everyone",
        "Demographic_Coverage": 0.6,
        "Property_Restrictions_Within_Node": [
            {
                "Accessibility": "YES"
            }
        ]
    }
}

Node_Property_Restrictions

array of JSON objects

NA

NA

NA

A list of the NodeProperty key:value pairs, as defined in the demographics file, that nodes must have to be targeted by the intervention. See NodeProperties and IndividualProperties parameters for more information. You can specify AND and OR combinations of key:value pairs with this parameter.

The following example restricts the intervention to nodes that are urban and medium risk or rural and low risk.

{
    "Node_Property_Restrictions": [
        {
            "Place": "URBAN",
            "Risk": "MED"
        },
        {
            "Place": "RURAL",
            "Risk": "LOW"
        }
    ]
}

Number_Repetitions

integer

-1

10000

1

The number of times an intervention is given, used with Timesteps_Between_Repetitions. A value of -1 implies an infinite number of repetitions.

 
{
    "class": "IncidenceEventCoordinator",
    "Number_Repetitions": 3
}

Responder

array of JSON objects

NA

NA

NA

List of JSON objects for specifying the threshold type, values, and the actions to take when the specified conditions and parameters have been met, as configured in the Incidence_Counter parameter. Some of the values are specified in the following parameters:

  • Threshold_Type

  • Action_List

  • Threshold

  • Event_To_Broadcast

 
{
    "Responder": {
        "Threshold_Type": "COUNT",
        "Action_List": [
            {
                "Threshold": 5,
                "Event_To_Broadcast": "Action_Event_1"
            },
            {
                "Threshold": 10,
                "Event_To_Broadcast": "Action_Event_2"
            }
        ]
    }
}

Threshold

float

0

3.40E+03

0

The threshold value that triggers the specified action for the Event_To_Broadcast parameter. When you use the Threshold parameter you must also use the Event_To_Broadcast parameter.

 
{
    "Threshold_Type": "COUNT",
    "Action_List": [
        {
            "Threshold": 20,
            "Event_To_Broadcast": "Action_Event_1"
        },
        {
            "Threshold": 30,
            "Event_To_Broadcast": "Action_Event_2"
        }
    ]
}

Threshold_Type

enum

NA

NA

COUNT

Threshold type. Possible values are:

  • COUNT

  • PERCENTAGE.

 
{
    "Threshold_Type": "COUNT",
    "Action_List": [
        {
            "Threshold": 20,
            "Event_To_Broadcast": "Action_Event_1"
        },
        {
            "Threshold": 30,
            "Event_To_Broadcast": "Action_Event_2"
        }
    ]
}

Timesteps_Between_Repetitions

integer

-1

1000

-1

The repetition interval.

 
{
    "class": "IncidenceEventCoordinator",
    "Number_Repetitions": 3,
    "Timesteps_Between_Repetitions": 6
}

Trigger_Condition_List

array of strings

NA

NA

NA

A list of events that will trigger an intervention.

 
{
    "Trigger_Condition_List": [
        "NewClinicalCase",
        "NewSevereCase"
    ]
}

Targeting_Config

JSON object

NA

NA

NA

Be more selective of individuals by using the Targeting_Config classes.

 
{
    "Targeting_Config": {
        "class": "HasIntervention",
        "Is_Equal_To": 0,
        "Intervention_Name": "MyVaccine"
    }
}

{
    "class": "IncidenceEventCoordinator",
    "Number_Repetitions" : 3,
    "Timesteps_Between_Repetitions" : 6
}
NChooserEventCoordinator

The NChooserEventCoordinator coordinator class is used to distribute an individual-level intervention to exactly N people of a targeted demographic. This contrasts with other event coordinators that distribute an intervention to a percentage of the population, not to an exact count. See the following JSON example and table, which shows all available parameters for this event coordinator.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Age_Ranges_Years

array of JSON objects

NA

NA

NA

A list of age ranges that individuals must be in to qualify for an intervention. Each age range is a JSON object with a minimum and a maximum property. An individual is considered in range if their age is greater than or equal to the minimum age and less than the maximum age, in floating point years of age. It must have the same number of objects as Num_Targeted_XXX has elements.

 
{
    "Age_Ranges_Years": [
        {
            "Min": 10,
            "Max": 19
        },
        {
            "Min": 30,
            "Max": 39
        },
        {
            "Min": 50,
            "Max": 59
        }
    ],
    "Num_Targeted_Males": [
        600000,
        400000,
        200000
    ],
    "Num_Targeted_Females": [
        500000,
        300000,
        100000
    ]
}

Distributions

array of JSON objects

NA

NA

NA

The ordered list of elements defining when, to whom, and how many interventions to distribute.

 
{
    "Distributions": {
        "Start_Day": 10,
        "End_Day": 20,
        "Property_Restrictions_Within_Node": [],
        "Age_Ranges_Years": [
            {
                "Min": 10,
                "Max": 19
            },
            {
                "Min": 40,
                "Max": 49
            }
        ],
        "Num_Targeted": [
            100,
            300
        ]
    }
}

End_Day

float

0

3.40282e+38

3.40E+38

The day to stop distributing the intervention. No interventions are distributed on this day or going forward.

 
{
    "Start_Day": 10,
    "End_Day": 20
}

Intervention_Config

JSON object

NA

NA

NA

The nested JSON of the actual intervention to be distributed by this event coordinator.

 
{
    "Intervention_Config": {
        "class": "OutbreakIndividual",
        "Antigen": 0,
        "Genome": 0,
        "Outbreak_Source": "PrevalenceIncrease",
        "Incubation_Period_Override": 1
    }
}

Max

float

0

125

125

This parameter determines the maximum age, in years for individuals to be included in the targeted population. An individual is considered in range if their age is greater than or equal to the minimum age and less than the maximum age, in floating point years of age.

 
{
    "Age_Ranges_Years": [
        {
            "Min": 20,
            "Max": 29
        },
        {
            "Min": 50,
            "Max": 59
        }
    ]
}

Min

float

0

125

0

This parameter determines the minimum age, in years for individuals to be included in the targeted population. An individual is considered in range if their age is greater than or equal to the minimum age and less than the maximum age, in floating point years of age.

 
{
    "Age_Ranges_Years": [
        {
            "Min": 20,
            "Max": 29
        },
        {
            "Min": 50,
            "Max": 59
        }
    ]
}

Num_Targeted

array of integers

0

2147480000.0

0

The number of individuals to target with the intervention. Note that this value will be scaled up by the population scaling factor equal to Base_Population_Scale_Factor. If using this parameter, Num_Targeted_Males and Num_Targeted_Females must be empty.

 
{
    "Age_Ranges_Years": [
        {
            "Min": 10,
            "Max": 19
        },
        {
            "Min": 40,
            "Max": 49
        }
    ],
    "Num_Targeted": [
        100,
        300
    ]
}

Num_Targeted_Females

array of integers

0

2147480000.0

0

The number of female individuals to distribute interventions to during this time period. If using this parameter with Num_Targeted_Males to target specific genders, they both must be the same length, and Num_Targeted must be empty.

 
{
    "Age_Ranges_Years": [
        {
            "Min": 10,
            "Max": 19
        },
        {
            "Min": 30,
            "Max": 39
        },
        {
            "Min": 50,
            "Max": 59
        }
    ],
    "Num_Targeted_Males": [
        600000,
        400000,
        200000
    ],
    "Num_Targeted_Females": [
        500000,
        300000,
        100000
    ]
}

Num_Targeted_Males

array of integers

0

2147480000.0

0

The number of male individuals to distribute interventions to during this time period. If using this parameter with Num_Targeted_Females to target specific genders, they both must be the same length, and Num_Targeted must be empty.

 
{
    "Age_Ranges_Years": [
        {
            "Min": 10,
            "Max": 19
        },
        {
            "Min": 30,
            "Max": 39
        },
        {
            "Min": 50,
            "Max": 59
        }
    ],
    "Num_Targeted_Males": [
        600000,
        400000,
        200000
    ],
    "Num_Targeted_Females": [
        500000,
        300000,
        100000
    ]
}

Property_Restrictions_Within_Node

array of JSON objects

NA

NA

NA

A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information.

This parameter allows you to specify AND and OR combinations of key:value pairs. You may specify individual property restrictions using either this parameter or Property_Restrictions, but not both.

The following example restricts the intervention to individuals who are urban and high risk or urban and medium risk.

{
    "Property_Restrictions_Within_Node": [
        {
            "Risk": "HIGH",
            "Geographic": "URBAN"
        },
        {
            "Risk": "MEDIUM",
            "Geographic": "URBAN"
        }
    ]
}

Start_Day

float

0

3.40282e+38

0

The day to start distributing the intervention.

 
{
    "Start_Day": 0,
    "End_Day": 100
}

{
    "Use_Defaults": 1,
    "Events": [{
        "class": "CampaignEvent",
        "Start_Day": 1,
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Event_Coordinator_Config__KP1": "",
        "Event_Coordinator_Config": {
            "class": "NChooserEventCoordinator",
            "Distributions": [{
                "Start_Day": 10,
                "End_Day": 11,
                "Property_Restrictions_Within_Node": [{
                    "QualityOfCare": "Bad"
                }],
                "Age_Ranges_Years": [{
                    "Min": 20,
                    "Max": 40
                }],
                "Num_Targeted": [
                    99999999
                ]
            }],
            "Intervention_Config": {
                "class": "ControlledVaccine",
                "Cost_To_Consumer": 10,
                "Vaccine_Type": "AcquisitionBlocking",
                "Vaccine_Take": 1.0,
                "Waning_Config": {
                    "class": "WaningEffectMapLinear",
                    "Initial_Effect": 1.0,
                    "Expire_At_Durability_Map_End": 1,
                    "Durability_Map": {
                        "Times": [
                            0,
                            50,
                            100
                        ],
                        "Values": [
                            1.0,
                            1.0,
                            0.0
                        ]
                    }
                },
                "Distributed_Event_Trigger": "Vaccinated",
                "Expired_Event_Trigger": "VaccineExpired",
                "Duration_To_Wait_Before_Revaccination": 0
            }
        }
    }]
}
StandardInterventionDistributionEventCoordinator

The StandardInterventionDistributionEventCoordinator coordinator class distributes an individual-level or node-level intervention to a specified fraction of individuals or nodes within a node set. Recurring campaigns can be created by specifying the number of times distributions should occur and the time between repetitions.

Demographic restrictions such as Demographic_Coverage and Target_Gender do not apply when distributing node-level interventions. The node-level intervention must handle the demographic restrictions.

See the following JSON example and table, which shows all available parameters for this event coordinator.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Demographic_Coverage

float

0

1

1

The fraction of individuals in the target demographic that will receive this intervention.

 
{
    "Demographic_Coverage": 1
}

Individual_Selection_Type

enum

NA

NA

DEMOGRAPHIC_COVERAGE

Defines how the people that pass all of the other target restrictions are selected.

Possible values are:

DEMOCGRAPHIC_COVERAGE

When set, the parameter Demographic_Coverage is used to randomly select a certain percentage of the targeted group.

TARGET_NUM_INDIVIDUALS

When set, the parameter Target_Num_Individuals is used to select exactly that number of people out of the targeted group.

 
{
    "Individual_Selection_Type": "DEMOGRAPHIC_COVERAGE",
    "Demographic_Coverage": 0.75
}

Intervention_Config

JSON object

NA

NA

NA

The nested JSON of the actual intervention to be distributed by this event coordinator.

 
{
    "Intervention_Config": {
        "class": "OutbreakIndividual",
        "Incubation_Period_Override": 1,
        "Outbreak_Source": "PrevalenceIncrease"
    }
}

Node_Property_Restrictions

array of JSON objects

NA

NA

NA

A list of the NodeProperty key:value pairs, as defined in the demographics file, that nodes must have to be targeted by the intervention. See NodeProperties and IndividualProperties parameters for more information.

You can specify AND and OR combinations of key:value pairs with this parameter.

The following example restricts the intervention to nodes that are urban and medium risk or rural and low risk.

{
    "Node_Property_Restrictions": [
        {
            "Place": "URBAN",
            "Risk": "MED"
        },
        {
            "Place": "RURAL",
            "Risk": "LOW"
        }
    ]
}

Number_Repetitions

integer

-1

10000

1

The number of times an intervention is given, used with Timesteps_Between_Repetitions. A value of -1 implies an infinite number of repetitions.

 
{
    "Event_Coordinator_Config": {
        "Intervention_Config": {
            "class": "Outbreak",
            "Num_Cases": 1
        },
        "Number_Repetitions": 10,
        "Timesteps_Between_Repetitions": 50,
        "class": "StandardInterventionDistributionEventCoordinator"
    }
}

Property_Restrictions

array of JSON objects

NA

NA

NA

A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information.

To specify AND and OR combinations of key:value pairs, use Property_Restrictions_Within_Node. You cannot use both of these parameters in the same event coordinator.

 
{
    "Property_Restrictions": [
        "Risk:HIGH"
    ]
}

Property_Restrictions_Within_Node

array of JSON objects

NA

NA

NA

A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information.

This parameter allows you to specify AND and OR combinations of key:value pairs. You may specify individual property restrictions using either this parameter or Property_Restrictions, but not both.

The following example restricts the intervention to individuals who are urban and high risk or urban and medium risk.

{
    "Property_Restrictions_Within_Node": [
        {
            "Risk": "HIGH",
            "Geographic": "URBAN"
        },
        {
            "Risk": "MEDIUM",
            "Geographic": "URBAN"
        }
    ]
}

Target_Age_Max

float

0

9.3228e+35

9.3228e+35

The upper end of ages targeted for an intervention, in years. Used when Target_Demographic is set to ExplicitAgeRanges or ExplicitAgeRangesAndGender.

 
{
    "Target_Age_Max": 20,
    "Target_Age_Min": 10,
    "Target_Demographic": "ExplicitAgeRanges"
}

Target_Age_Min

float

0

9.3228e+35

0

The lower end of ages targeted for an intervention, in years. Used when Target_Demographic is set to ExplicitAgeRanges or ExplicitAgeRangesAndGender.

 
{
    "Target_Age_Max": 20,
    "Target_Age_Min": 10,
    "Target_Demographic": "ExplicitAgeRanges"
}

Target_Demographic

enum

NA

NA

Everyone

The target demographic group. Possible values are:

  • Everyone

  • ExplicitAgeRanges

  • ExplicitAgeRangesAndGender

  • ExplicitGender

  • ExplicitDiseaseState

 
{
    "Target_Age_Max": 20,
    "Target_Age_Min": 10,
    "Target_Demographic": "ExplicitAgeRanges"
}

Target_Gender

enum

NA

NA

All

Specifies the gender restriction for the intervention. Possible values are:

  • Male

  • Female

  • All

 
{
    "Target_Gender": "Male"
}

Target_Residents_Only

boolean

0

1

0

When set to true (1), the intervention is only distributed to individuals that began the simulation in the node (i.e. those that claim the node as their residence).

 
{
    "Target_Residents_Only": 1
}

Timesteps_Between_Repetitions

integer

-1

10000

-1

The repetition interval.

 
{
    "Timesteps_Between_Repetitions": 50
}

Targeting_Config

JSON object

NA

NA

NA

Be more selective of individuals by using the Targeting_Config classes.

 
{
    "Targeting_Config": {
        "class": "HasIntervention",
        "Is_Equal_To": 0,
        "Intervention_Name": "MyVaccine"
    }
}

{
    "Use_Defaults": 1,
    "Events": [{
        "Event_Name": "Outbreak",
        "class": "CampaignEvent",
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 1,
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Demographic_Coverage": 0.005,
            "Intervention_Config": {
                "Outbreak_Source": "PrevalenceIncrease",
                "class": "OutbreakIndividual"
            }
        }
    }]
}
SurveillanceEventCoordinator

Note

This campaign class and associated parameters are currently in beta release and have not yet been fully tested.

The SurveillanceEventCoordinator coordinator class listens for and detects events happening and then responds with broadcasted events when a threshold has been met. This campaign event is typically used with other classes, such as BroadcastCoordinatorEvent, TriggeredEventCoordinator, and DelayEventCoordinator.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Action_List

array of JSON objects

NA

NA

NA

A list of possible actions to take if a particular threshold is met. An action is taken when the specified threshold value is less than the number of incidence counted. If there are multiple actions listed then the action with the highest threshold value, that is also less than the number of incidence counted, is selected. The list cannot be empty.

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "ACF_Counter",
        "Duration": 30,
        "Incidence_Counter": {
            "Counter_Event_Type": "NODE",
            "Counter_Period": 14,
            "Counter_Type": "PERIODIC",
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Trigger_Condition_List": [
                "Node_Event_1",
                "Node_Event_2"
            ]
        },
        "Responder": {
            "Action_List": [
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_2",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 5
                },
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_4",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 2
                }
            ],
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "COUNT"
        },
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [
            "Start_SIA_2",
            "Start_SIA_4"
        ],
        "class": "SurveillanceEventCoordinator"
    },
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "class": "CampaignEvent"
}

Coordinator_Name

string

NA

NA

TriggeredEventCoordinator

The unique identifying coordinator name, which is useful with the output report, ReportSurveillanceEventRecorder.csv.

 
{
    "class": "CampaignEvent",
    "Start_Day": 1,
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Event_Coordinator_Config": {
        "class": "SurveillanceEventCoordinator",
        "Coordinator_Name": "ACF_Counter",
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [
            "Start_SIA_2",
            "Start_SIA_4"
        ],
        "Duration": 30,
        "Incidence_Counter": {
            "Counter_Type": "PERIODIC",
            "Counter_Period": 14,
            "Counter_Event_Type": "NODE",
            "Trigger_Condition_List": [
                "Node_Event_1",
                "Node_Event_2"
            ],
            "Target_Demographic": "Everyone",
            "Demographic_Coverage": 1
        },
        "Responder": {
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "COUNT",
            "Action_List": [
                {
                    "Threshold": 5,
                    "Event_Type": "COORDINATOR",
                    "Event_To_Broadcast": "Ind_Start_SIA_2"
                },
                {
                    "Threshold": 2,
                    "Event_Type": "COORDINATOR",
                    "Event_To_Broadcast": "Ind_Start_SIA_4"
                }
            ]
        }
    }
}

Counter_Event_Type

enum

NA

NA

INDIVIDUAL

Type of events that can be included in Trigger_Condition_List. Possible values are:

  • COORDINATOR

  • INDIVIDUAL

  • NODE

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "ACF_Counter",
        "Duration": 30,
        "Incidence_Counter": {
            "Counter_Event_Type": "NODE",
            "Counter_Period": 14,
            "Counter_Type": "PERIODIC",
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Trigger_Condition_List": [
                "Node_Event_1",
                "Node_Event_2"
            ]
        },
        "Responder": {
            "Action_List": [
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_2",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 5
                },
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_4",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 2
                }
            ],
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "COUNT"
        },
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [
            "Start_SIA_2",
            "Start_SIA_4"
        ],
        "class": "SurveillanceEventCoordinator"
    },
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "class": "CampaignEvent"
}

Counter_Period

float

1

10000

1

When Counter_Type is set to PERIODIC, this is the counter period (in days).

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "ACF_Counter",
        "Duration": 30,
        "Incidence_Counter": {
            "Counter_Event_Type": "NODE",
            "Counter_Period": 14,
            "Counter_Type": "PERIODIC",
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Trigger_Condition_List": [
                "Node_Event_1",
                "Node_Event_2"
            ]
        },
        "Responder": {
            "Action_List": [
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_2",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 5
                },
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_4",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 2
                }
            ],
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "COUNT"
        },
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [
            "Start_SIA_2",
            "Start_SIA_4"
        ],
        "class": "SurveillanceEventCoordinator"
    },
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "class": "CampaignEvent"
}

Counter_Type

enum

NA

NA

PERIODIC

Counter type used for surveillance of events. The counter is triggered by events in Start_Trigger_Condition_List and the counter stops when it receives an event in Stop_Trigger_Condition_List or the listening duration expires. Possible values are:

PERIODIC

Once triggered, events are counted during the period (in days) as set in Counter_Period. At the end of the period, the counter will notify Responder with the data accumulated and then start listening again, continuing forever until it is stopped or the duration expires. For example, if the listening duration is 45 days, Counter_Period is 30, and the counter is triggered on day 20, it will never complete the counter period and trigger the responder.

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "ACF_Counter",
        "Duration": 30,
        "Incidence_Counter": {
            "Counter_Event_Type": "NODE",
            "Counter_Period": 14,
            "Counter_Type": "PERIODIC",
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Trigger_Condition_List": [
                "Node_Event_1",
                "Node_Event_2"
            ]
        },
        "Responder": {
            "Action_List": [
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_2",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 5
                },
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_4",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 2
                }
            ],
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "COUNT"
        },
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [
            "Start_SIA_2",
            "Start_SIA_4"
        ],
        "class": "SurveillanceEventCoordinator"
    },
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "class": "CampaignEvent"
}

Demographic_Coverage

float

0

1

1

The fraction of individuals in the target demographic that are counted.

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "ACF_Counter",
        "Duration": 30,
        "Incidence_Counter": {
            "Counter_Event_Type": "NODE",
            "Counter_Period": 14,
            "Counter_Type": "PERIODIC",
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Trigger_Condition_List": [
                "Node_Event_1",
                "Node_Event_2"
            ]
        },
        "Responder": {
            "Action_List": [
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_2",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 5
                },
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_4",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 2
                }
            ],
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "COUNT"
        },
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [
            "Start_SIA_2",
            "Start_SIA_4"
        ],
        "class": "SurveillanceEventCoordinator"
    },
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "class": "CampaignEvent"
}

Duration

float

-1

3.40282e+38

-1

The number of days from when the surveillance event coordinator was created by the campaign event. Once the number of days has passed, the delay event coordinator will unregister for events and expire. The default value of ‘-1’ = never expire.

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "ACF_Counter",
        "Duration": 30,
        "Incidence_Counter": {
            "Counter_Event_Type": "NODE",
            "Counter_Period": 14,
            "Counter_Type": "PERIODIC",
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Trigger_Condition_List": [
                "Node_Event_1",
                "Node_Event_2"
            ]
        },
        "Responder": {
            "Action_List": [
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_2",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 5
                },
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_4",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 2
                }
            ],
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "COUNT"
        },
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [
            "Start_SIA_2",
            "Start_SIA_4"
        ],
        "class": "SurveillanceEventCoordinator"
    },
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "class": "CampaignEvent"
}

Event_Type

enum

NA

NA

INDIVIDUAL

The type of event to be broadcast. Possible values are:

  • COORDINATOR

  • INDIVIDUAL

  • NODE

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "ACF_Counter",
        "Duration": 30,
        "Incidence_Counter": {
            "Counter_Event_Type": "NODE",
            "Counter_Period": 14,
            "Counter_Type": "PERIODIC",
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Trigger_Condition_List": [
                "Node_Event_1",
                "Node_Event_2"
            ]
        },
        "Responder": {
            "Action_List": [
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_2",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 5
                },
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_4",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 2
                }
            ],
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "COUNT"
        },
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [
            "Start_SIA_2",
            "Start_SIA_4"
        ],
        "class": "SurveillanceEventCoordinator"
    },
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "class": "CampaignEvent"
}

Incidence_Counter

array of JSON objects

NA

NA

NA

List of JSON objects for specifying the conditions and parameters that must be met for an incidence to be counted. Some of the values are specified in the following parameters: Counter_Type, Counter_Period, Counter_Event_Type, Trigger_Condition_List, Node_Property_Restrictions, Property_Restrictions_Within_Node.

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "ACF_Counter",
        "Duration": 30,
        "Incidence_Counter": {
            "Counter_Event_Type": "NODE",
            "Counter_Period": 14,
            "Counter_Type": "PERIODIC",
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Trigger_Condition_List": [
                "Node_Event_1",
                "Node_Event_2"
            ]
        },
        "Responder": {
            "Action_List": [
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_2",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 5
                },
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_4",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 2
                }
            ],
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "COUNT"
        },
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [
            "Start_SIA_2",
            "Start_SIA_4"
        ],
        "class": "SurveillanceEventCoordinator"
    },
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "class": "CampaignEvent"
}

Node_Property_Restrictions

array of JSON objects

NA

NA

NA

A list of the NodeProperty key:value pairs, as defined in the demographics file, that nodes must have to be targeted by the intervention. See NodeProperties and IndividualProperties parameters for more information.

You can specify AND and OR combinations of key:value pairs with this parameter.

The following example restricts the intervention to nodes that are urban and medium risk or rural and low risk.

{
    "Node_Property_Restrictions": [
        {
            "Place": "URBAN",
            "Risk": "MED"
        },
        {
            "Place": "RURAL",
            "Risk": "LOW"
        }
    ]
}

Percentage_Events_To_Count

array of strings

NA

NA

NA

When Threshold_Type equals PERCENTAGE_EVENTS then Percentage_Events_To_Count lists the events that will be counted for the denominator which will then be used with the specified event for Trigger_Condition_List counted for the numerator.

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "ACF_Counter",
        "Duration": -1,
        "Incidence_Counter": {
            "Counter_Event_Type": "NODE",
            "Counter_Period": 30,
            "Counter_Type": "PERIODIC",
            "Demographic_Coverage": 1,
            "Node_Property_Restrictions": [],
            "Property_Restrictions_Within_Node": [],
            "Target_Demographic": "Everyone",
            "Trigger_Condition_List": [
                "Positive_Event_Node"
            ]
        },
        "Responder": {
            "Action_List": [
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_2",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 0.5
                }
            ],
            "Percentage_Events_To_Count": [
                "Negative_Event_Node",
                "Positive_Event_Node"
            ],
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "PERCENTAGE_EVENTS"
        },
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [],
        "class": "SurveillanceEventCoordinator"
    },
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "class": "CampaignEvent"
}

Property_Restrictions

array of JSON objects

NA

NA

NA

A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information.

To specify AND and OR combinations of key:value pairs, use Property_Restrictions_Within_Node. You cannot use both of these parameters in the same event coordinator.

 
{
    "Property_Restrictions": [
        "Risk:HIGH"
    ]
}

Property_Restrictions_Within_Node

array of JSON objects

NA

NA

NA

A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information.

This parameter allows you to specify AND and OR combinations of key:value pairs. You may specify individual property restrictions using either this parameter or Property_Restrictions, but not both.

The following example restricts the intervention to individuals who are urban and high risk or urban and medium risk.

{
    "Property_Restrictions_Within_Node": [
        {
            "Geographic": "URBAN",
            "Risk": "HIGH"
        },
        {
            "Geographic": "URBAN",
            "Risk": "MEDIUM"
        }
    ]
}

Responded_Event

string

NA

NA

“”

A coordinator event, defined in Custom_Coordinator_Events, that is broadcast if Responder responded. More specifically, at the completion of a counting period, if an action is selected, the action events are broadcast and then the Responded_Event is also broadcast. This allows other event coordinators to react to the action events being broadcast.

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "ACF_Counter",
        "Duration": 30,
        "Incidence_Counter": {
            "Counter_Event_Type": "NODE",
            "Counter_Period": 14,
            "Counter_Type": "PERIODIC",
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Trigger_Condition_List": [
                "Node_Event_1",
                "Node_Event_2"
            ]
        },
        "Responder": {
            "Action_List": [
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_2",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 5
                },
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_4",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 2
                }
            ],
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "COUNT"
        },
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [
            "Start_SIA_2",
            "Start_SIA_4"
        ],
        "class": "SurveillanceEventCoordinator"
    },
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "class": "CampaignEvent"
}

Responder

array of JSON objects

NA

NA

NA

List of JSON objects for specifying the threshold type, values, and the actions to take when the specified conditions and parameters have been met, as configured in Incidence_Counter. Some of the values are specified in the following parameters:

  • Action_List

  • Event_To_Broadcast

  • Threshold_Type

  • Threshold

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "ACF_Counter",
        "Duration": 30,
        "Incidence_Counter": {
            "Counter_Event_Type": "NODE",
            "Counter_Period": 14,
            "Counter_Type": "PERIODIC",
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Trigger_Condition_List": [
                "Node_Event_1",
                "Node_Event_2"
            ]
        },
        "Responder": {
            "Action_List": [
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_2",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 5
                },
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_4",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 2
                }
            ],
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "COUNT"
        },
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [
            "Start_SIA_2",
            "Start_SIA_4"
        ],
        "class": "SurveillanceEventCoordinator"
    },
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "class": "CampaignEvent"
}

Start_Trigger_Condition_List

array of strings

NA

NA

NA

The trigger event list, as specified in the Custom_Coordinator_Events config parameter, that will start Incidence_Counter counting events. The surveillance event coordinator will keep counting and responding until it gets a stop event, as defined in Stop_Trigger_Condition_List, or the duration of the surveillance event coordinator has expired, as defined in Duration. The list cannot be empty.

 
{
    "class": "CampaignEvent",
    "Start_Day": 1,
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Event_Coordinator_Config": {
        "class": "SurveillanceEventCoordinator",
        "Coordinator_Name": "ACF_Counter",
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [
            "Start_SIA_2",
            "Start_SIA_4"
        ],
        "Duration": 30,
        "Incidence_Counter": {
            "Counter_Type": "PERIODIC",
            "Counter_Period": 14,
            "Counter_Event_Type": "NODE",
            "Trigger_Condition_List": [
                "Node_Event_1",
                "Node_Event_2"
            ],
            "Target_Demographic": "Everyone",
            "Demographic_Coverage": 1
        },
        "Responder": {
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "COUNT",
            "Action_List": [
                {
                    "Threshold": 5,
                    "Event_Type": "COORDINATOR",
                    "Event_To_Broadcast": "Ind_Start_SIA_2"
                },
                {
                    "Threshold": 2,
                    "Event_Type": "COORDINATOR",
                    "Event_To_Broadcast": "Ind_Start_SIA_4"
                }
            ]
        }
    }
}

Stop_Trigger_Condition_List

array of strings

NA

NA

NA

The broadcast event list, as specified in the Custom_Coordinator_Events config parameter, that will stop Incidence_Counter counting events. The coordinator can start counting again if it receives a start trigger event. The list can be empty.

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "ACF_Counter",
        "Duration": 30,
        "Incidence_Counter": {
            "Counter_Event_Type": "NODE",
            "Counter_Period": 14,
            "Counter_Type": "PERIODIC",
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Trigger_Condition_List": [
                "Node_Event_1",
                "Node_Event_2"
            ]
        },
        "Responder": {
            "Action_List": [
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_2",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 5
                },
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_4",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 2
                }
            ],
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "COUNT"
        },
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [
            "Start_SIA_2",
            "Start_SIA_4"
        ],
        "class": "SurveillanceEventCoordinator"
    },
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "class": "CampaignEvent"
}

Target_Age_Max

float

0

9.3228e+35

9.3228e+35

The upper end of ages targeted for an intervention, in years. Used when Target_Demographic is set to ExplicitAgeRanges or ExplicitAgeRangesAndGender.

 
{
    "Target_Age_Max": 20,
    "Target_Age_Min": 10,
    "Target_Demographic": "ExplicitAgeRanges"
}

Target_Age_Min

float

0

9.3228e+35

0

The lower end of ages targeted for an intervention, in years. Used when Target_Demographic is set to ExplicitAgeRanges or ExplicitAgeRangesAndGender.

 
{
    "Target_Age_Max": 20,
    "Target_Age_Min": 10,
    "Target_Demographic": "ExplicitAgeRanges"
}

Target_Demographic

enum

NA

NA

Everyone

The target demographic group. Possible values are:

  • Everyone

  • ExplicitAgeRanges

  • ExplicitAgeRangesAndGender

  • ExplicitGender

  • ExplicitDiseaseState

 
{
    "Target_Age_Max": 20,
    "Target_Age_Min": 10,
    "Target_Demographic": "ExplicitAgeRanges"
}

Target_Gender

enum

NA

NA

All

Specifies the gender restriction for the intervention. Possible values are:

  • Male

  • Female

  • All

 
{
    "Target_Gender": "Female"
}

Target_Residents_Only

boolean

0

1

0

When set to true (1), only individuals that currently reside in their ‘home’ node will be counted.

 
{
    "Target_Residents_Only": 1
}

Threshold

float

0

3.40282e+38

0

The COUNT or PERCENTAGE, as configured with Threshold_Type, threshold value that must be met before and action from Action_List will be considered.

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "ACF_Counter",
        "Duration": 30,
        "Incidence_Counter": {
            "Counter_Event_Type": "NODE",
            "Counter_Period": 14,
            "Counter_Type": "PERIODIC",
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Trigger_Condition_List": [
                "Node_Event_1",
                "Node_Event_2"
            ]
        },
        "Responder": {
            "Action_List": [
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_2",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 5
                },
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_4",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 2
                }
            ],
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "COUNT"
        },
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [
            "Start_SIA_2",
            "Start_SIA_4"
        ],
        "class": "SurveillanceEventCoordinator"
    },
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "class": "CampaignEvent"
}

Threshold_Type

enum

NA

NA

COUNT

Threshold type to indicate how Responder handles the count of events from Incidence_Counter and the thresholds in Action_List. Possible values are:

COUNT

A raw count of events. Also, with COUNT, setting the x_Base_Population configuration parameter can affect the number count by changing the population but it could be very indirect.

PERCENTAGE

Counts the number of individuals that meet the restrictions and then to divide the total number of events by this number. Note that it is possible for an individual to emit an event that might not be counted in the denominator if their demographic restriction attributes changed between the time of the emitted event and the time the denominator was counted.

PERCENTAGE_EVENTS

Percentage_Events_To_Count lists the events that will be counted for the denominator, which will then be used with the specified event for Trigger_Condition_List to be counted for the numerator.

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "ACF_Counter",
        "Duration": 30,
        "Incidence_Counter": {
            "Counter_Event_Type": "NODE",
            "Counter_Period": 14,
            "Counter_Type": "PERIODIC",
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Trigger_Condition_List": [
                "Node_Event_1",
                "Node_Event_2"
            ]
        },
        "Responder": {
            "Action_List": [
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_2",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 5
                },
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_4",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 2
                }
            ],
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "COUNT"
        },
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [
            "Start_SIA_2",
            "Start_SIA_4"
        ],
        "class": "SurveillanceEventCoordinator"
    },
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "class": "CampaignEvent"
}

Trigger_Condition_List

array of strings

NA

NA

NA

The list of events to count.The list cannot be empty. The type of events contained in the list is determined by Counter_Event_Type. Depending upon the type, the events must be defined in one of the following configuration parameters:

  • Custom_Coordinator_Events

  • Custom_Individual_Events

  • Custom_Node_Events

 
{
    "Event_Coordinator_Config": {
        "Coordinator_Name": "ACF_Counter",
        "Duration": 30,
        "Incidence_Counter": {
            "Counter_Event_Type": "NODE",
            "Counter_Period": 14,
            "Counter_Type": "PERIODIC",
            "Demographic_Coverage": 1,
            "Target_Demographic": "Everyone",
            "Trigger_Condition_List": [
                "Node_Event_1",
                "Node_Event_2"
            ]
        },
        "Responder": {
            "Action_List": [
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_2",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 5
                },
                {
                    "Event_To_Broadcast": "Ind_Start_SIA_4",
                    "Event_Type": "COORDINATOR",
                    "Threshold": 2
                }
            ],
            "Responded_Event": "Respond_To_Surveillance",
            "Threshold_Type": "COUNT"
        },
        "Start_Trigger_Condition_List": [
            "Start_ACF"
        ],
        "Stop_Trigger_Condition_List": [
            "Start_SIA_2",
            "Start_SIA_4"
        ],
        "class": "SurveillanceEventCoordinator"
    },
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 1,
    "class": "CampaignEvent"
}

Targeting_Config

JSON object

NA

NA

NA

Be more selective of individuals by using the Targeting_Config classes.

 
{
    "Targeting_Config": {
        "class": "HasIntervention",
        "Is_Equal_To": 0,
        "Intervention_Name": "MyVaccine"
    }
}

{
    "Events": [

        {
            "comment": "Broadcast Event to start Surveillance",
            "class": "CampaignEvent",
            "Start_Day": 2,
            "Nodeset_Config": { "class": "NodeSetAll" },
            "Event_Coordinator_Config": {
                "class": "BroadcastCoordinatorEvent",
                "Coordinator_Name" : "Coordnator_1",
                "Cost_To_Consumer" : 10,
                "Broadcast_Event" : "Start_ACF"
            }
        },
        {
            "comment": "Triggered by Broadcast_Event, stops itself by broadcasting Start_SIA_X Event",
            "class": "CampaignEvent",
            "Start_Day": 1,
            "Nodeset_Config": { "class": "NodeSetAll" },
            "Event_Coordinator_Config": {
                "class": "SurveillanceEventCoordinator",
                "Coordinator_Name" : "ACF_Counter",
                "Start_Trigger_Condition_List" : [ "Start_ACF" ],
                "Stop_Trigger_Condition_List" : [ 
                    "Start_SIA_2", 
                    "Start_SIA_4"
                ],
                "Duration" : 30,
                "Incidence_Counter" : {
                    "Counter_Type" : "PERIODIC",
                    "Counter_Period" : 14,
                    "Counter_Event_Type" : "NODE",
                    "Trigger_Condition_List" : ["Node_Event_1","Node_Event_2"],
                    "Node_Property_Restrictions" : [],
                    "Property_Restrictions_Within_Node" : [],
                    "Target_Demographic": "Everyone",
                    "Demographic_Coverage" : 1.0
                },
                "Responder" : {
                    "Responded_Event" : "Respond_To_Surveillance",
                    "Threshold_Type" : "COUNT",
                    "Action_List" :
                    [
                        {
                            "Threshold" : 5,
                            "Event_Type" : "COORDINATOR",
                            "Event_To_Broadcast" : "Ind_Start_SIA_2"
                        },
                        {
                            "Threshold" : 2,
                            "Event_Type" : "COORDINATOR",
                            "Event_To_Broadcast" : "Ind_Start_SIA_4"
                        }
                    ]
                }
            }            
        },
        {
            "class": "CampaignEvent",
            "Start_Day": 3,
            "Nodeset_Config": { "class": "NodeSetAll" },
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Intervention_Config": {
                    "class": "BroadcastNodeEvent",
                    "Cost_To_Consumer" : 25,
                    "Broadcast_Event" : "Node_Event_1"
                }
            }
        },
               {
            "class": "CampaignEvent",
            "Start_Day": 3,
            "Nodeset_Config": { "class": "NodeSetAll" },
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Intervention_Config": {
                    "class": "BroadcastNodeEvent",
                    "Cost_To_Consumer" : 25,
                    "Broadcast_Event" : "Node_Event_1"
                }
            }
        },
        {
            "class": "CampaignEvent",
            "Start_Day": 4,
            "Nodeset_Config": { "class": "NodeSetAll" },
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Intervention_Config": {
                    "class": "BroadcastNodeEvent",
                    "Cost_To_Consumer" : 25,
                    "Broadcast_Event" : "Node_Event_2"
                }
            }
        }
    ],
    "Use_Defaults": 1
}
TriggeredEventCoordinator

Note

This campaign class and associated parameters are currently in beta release and have not yet been fully tested.

The TriggeredEventCoordinator coordinator class listens for trigger events, begins a series of repetitions of intervention distributions, and then broadcasts an event upon completion. This campaign event is typically used with other classes that broadcast and distribute events, such as BroadcastCoordinatorEvent, DelayEventCoordinator, and SurveillanceEventCoordinator.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Completion_Event

array of strings

NA

NA

NA

The completion event list that will be broadcast every time the triggered event coordinator completes a set of repetitions. The events contained in the list are defined in Custom_Coordinator_Events in the simulation configuration file.

 
{
    "Event_Coordinator_Config": {
        "class": "TriggeredEventCoordinator",
        "Coordinator_Name": "1n2_Vaccinator",
        "Start_Trigger_Condition_List": [
            "Start_Vaccinating_1n2"
        ],
        "Stop_Trigger_Condition_List": [],
        "Number_Repetitions": 1,
        "Timesteps_Between_Repetitions": 1,
        "Duration": -1,
        "Target_Demographic": "Everyone",
        "Demographic_Coverage": 0.05,
        "Intervention_Config": {
            "class": "SimpleVaccine",
            "Cost_To_Consumer": 1,
            "Vaccine_Take": 1,
            "Vaccine_Type": "AcquisitionBlocking",
            "Waning_Config": {
                "class": "WaningEffectBox",
                "Initial_Effect": 0.59,
                "Box_Duration": 730
            }
        },
        "Completion_Event": "Done_Vaccinating_1n2"
    }
}

Coordinator_Name

string

NA

NA

TriggeredEventCoordinator

The unique identifying coordinator name used to identify the different coordinators in reports.

 
{
    "Event_Coordinator_Config": {
        "Completion_Event": "Done_Vaccinating_1n2",
        "Coordinator_Name": "1n2_Vaccinator",
        "Demographic_Coverage": 0.05,
        "Duration": -1,
        "Intervention_Config": {
            "Cost_To_Consumer": 1,
            "Vaccine_Take": 1,
            "Vaccine_Type": "AcquisitionBlocking",
            "Waning_Config": {
                "Box_Duration": 730,
                "Initial_Effect": 0.59,
                "class": "WaningEffectBox"
            },
            "class": "SimpleVaccine"
        },
        "Number_Repetitions": 1,
        "Start_Trigger_Condition_List": [
            "Start_Vaccinating_1n2"
        ],
        "Stop_Trigger_Condition_List": [],
        "Target_Demographic": "Everyone",
        "Timesteps_Between_Repetitions": 1,
        "class": "TriggeredEventCoordinator"
    }
}

Duration

float

-1

3.40282e+38

-1

The time period (in days) that the triggered event coordinator is active before it expires. Once the specified duration has been reached the coordinator will expire whether or not it is in the middle of a set of repetitions. The value of -1 (default) equals to never expire.

 
{
    "Event_Coordinator_Config": {
        "Completion_Event": "Done_Vaccinating_1n2",
        "Coordinator_Name": "1n2_Vaccinator",
        "Demographic_Coverage": 0.05,
        "Duration": -1,
        "Intervention_Config": {
            "Cost_To_Consumer": 1,
            "Vaccine_Take": 1,
            "Vaccine_Type": "AcquisitionBlocking",
            "Waning_Config": {
                "Box_Duration": 730,
                "Initial_Effect": 0.59,
                "class": "WaningEffectBox"
            },
            "class": "SimpleVaccine"
        },
        "Number_Repetitions": 1,
        "Start_Trigger_Condition_List": [
            "Start_Vaccinating_1n2"
        ],
        "Stop_Trigger_Condition_List": [],
        "Target_Demographic": "Everyone",
        "Timesteps_Between_Repetitions": 1,
        "class": "TriggeredEventCoordinator"
    }
}

Intervention_Config

JSON object

NA

NA

NA

The nested JSON of the intervention to be distributed by this event coordinator.

 
{
    "Event_Coordinator_Config": {
        "Completion_Event": "Done_Vaccinating_1n2",
        "Coordinator_Name": "1n2_Vaccinator",
        "Demographic_Coverage": 0.05,
        "Duration": -1,
        "Intervention_Config": {
            "Cost_To_Consumer": 1,
            "Vaccine_Take": 1,
            "Vaccine_Type": "AcquisitionBlocking",
            "Waning_Config": {
                "Box_Duration": 730,
                "Initial_Effect": 0.59,
                "class": "WaningEffectBox"
            },
            "class": "SimpleVaccine"
        },
        "Number_Repetitions": 1,
        "Start_Trigger_Condition_List": [
            "Start_Vaccinating_1n2"
        ],
        "Stop_Trigger_Condition_List": [],
        "Target_Demographic": "Everyone",
        "Timesteps_Between_Repetitions": 1,
        "class": "TriggeredEventCoordinator"
    }
}

Node_Property_Restrictions

array of JSON objects

NA

NA

NA

A list of the NodeProperty key:value pairs, as defined in the demographics file, that nodes must have to be targeted by the intervention. See NodeProperties and IndividualProperties parameters for more information. You can specify AND and OR combinations of key:value pairs with this parameter.

The following configuration restrictions the intervention to nodes that are urban and high risk or have had the first round treatment and are low risk.

{
    "Event_Coordinator_Config": {
        "Completion_Event": "Done_Vaccinating_3n4",
        "Coordinator_Name": "3n4_Vaccinator",
        "Demographic_Coverage": 0.05,
        "Duration": -1,
        "Intervention_Config": {
            "Cost_To_Consumer": 2,
            "Vaccine_Take": 1,
            "Vaccine_Type": "AcquisitionBlocking",
            "Waning_Config": {
                "Box_Duration": 730,
                "Initial_Effect": 0.59,
                "class": "WaningEffectBox"
            },
            "class": "SimpleVaccine"
        },
        "Node_Property_Restrictions": [
            {
                "Place": "Urban",
                "Risk": "High"
            },
            {
                "InterventionStatus": "FirstRound",
                "Risk": "Low"
            }
        ],
        "Number_Repetitions": 3,
        "Property_Restrictions_Within_Node": [],
        "Start_Trigger_Condition_List": [
            "Start_Vaccinating_3n4"
        ],
        "Stop_Trigger_Condition_List": [
            "Stop_Vaccinating_3n4"
        ],
        "Target_Demographic": "Everyone",
        "Timesteps_Between_Repetitions": 10,
        "class": "TriggeredEventCoordinator"
    }
}

Number_Repetitions

integer

-1

10000

1

The number of times an intervention is given, used with Timesteps_Between_Repetitions. A value of -1 implies an infinite number of repetitions.

 
{
    "Event_Coordinator_Config": {
        "Completion_Event": "Done_Vaccinating_3n4",
        "Coordinator_Name": "3n4_Vaccinator",
        "Demographic_Coverage": 0.05,
        "Duration": -1,
        "Intervention_Config": {
            "Cost_To_Consumer": 2,
            "Vaccine_Take": 1,
            "Vaccine_Type": "AcquisitionBlocking",
            "Waning_Config": {
                "Box_Duration": 730,
                "Initial_Effect": 0.59,
                "class": "WaningEffectBox"
            },
            "class": "SimpleVaccine"
        },
        "Number_Repetitions": 3,
        "Start_Trigger_Condition_List": [
            "Start_Vaccinating_3n4"
        ],
        "Stop_Trigger_Condition_List": [
            "Stop_Vaccinating_3n4"
        ],
        "Target_Demographic": "Everyone",
        "Timesteps_Between_Repetitions": 10,
        "class": "TriggeredEventCoordinator"
    }
}

Property_Restrictions

array of JSON objects

NA

NA

NA

A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information.

To specify AND and OR combinations of key:value pairs, use Property_Restrictions_Within_Node. You cannot use both of these parameters in the same event coordinator.

 
{
    "Property_Restrictions": [
        "Risk:HIGH"
    ]
}

Property_Restrictions_Within_Node

array of JSON objects

NA

NA

NA

A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information.

This parameter allows you to specify AND and OR combinations of key:value pairs. You may specify individual property restrictions using either this parameter or Property_Restrictions, but not both.

The following example restricts the intervention to individuals who are urban and high risk or urban and medium risk.

{
    "Property_Restrictions_Within_Node": [
        {
            "Geographic": "URBAN",
            "Risk": "HIGH"
        },
        {
            "Geographic": "URBAN",
            "Risk": "MEDIUM"
        }
    ]
}

Start_Trigger_Condition_List

array of strings

NA

NA

NA

The trigger condition event list that when heard will start a new set of repetitions for the triggered event coordinator. The list cannot be empty. The events contained in the list are defined in Custom_Coordinator_Events in the simulation configuration file.

 
{
    "Event_Coordinator_Config": {
        "Completion_Event": "Done_Vaccinating_1n2",
        "Coordinator_Name": "1n2_Vaccinator",
        "Demographic_Coverage": 0.05,
        "Duration": -1,
        "Intervention_Config": {
            "Cost_To_Consumer": 1,
            "Vaccine_Take": 1,
            "Vaccine_Type": "AcquisitionBlocking",
            "Waning_Config": {
                "Box_Duration": 730,
                "Initial_Effect": 0.59,
                "class": "WaningEffectBox"
            },
            "class": "SimpleVaccine"
        },
        "Number_Repetitions": 1,
        "Start_Trigger_Condition_List": [
            "Start_Vaccinating_1n2"
        ],
        "Stop_Trigger_Condition_List": [],
        "Target_Demographic": "Everyone",
        "Timesteps_Between_Repetitions": 1,
        "class": "TriggeredEventCoordinator"
    }
}

Stop_Trigger_Condition_List

array of strings

NA

NA

NA

The trigger condition event list that when heard will stop any repetitions for the triggered event coordinator until a start trigger condition event list is received. The list can be empty. The events contained in the list are defined in Custom_Coordinator_Events in the simulation configuration file.

 
{
    "Event_Coordinator_Config": {
        "Completion_Event": "Done_Vaccinating_1n2",
        "Coordinator_Name": "1n2_Vaccinator",
        "Demographic_Coverage": 0.05,
        "Duration": -1,
        "Intervention_Config": {
            "Cost_To_Consumer": 1,
            "Vaccine_Take": 1,
            "Vaccine_Type": "AcquisitionBlocking",
            "Waning_Config": {
                "Box_Duration": 730,
                "Initial_Effect": 0.59,
                "class": "WaningEffectBox"
            },
            "class": "SimpleVaccine"
        },
        "Number_Repetitions": 1,
        "Start_Trigger_Condition_List": [
            "Start_Vaccinating_1n2"
        ],
        "Stop_Trigger_Condition_List": [],
        "Target_Demographic": "Everyone",
        "Timesteps_Between_Repetitions": 1,
        "class": "TriggeredEventCoordinator"
    }
}

Target_Age_Max

float

0

9.3228e+35

9.3228e+35

The upper end of ages targeted for an intervention, in years. Used when Target_Demographic is set to ExplicitAgeRanges or ExplicitAgeRangesAndGender.

 
{
    "Target_Age_Max": 20,
    "Target_Age_Min": 10,
    "Target_Demographic": "ExplicitAgeRanges"
}

Target_Age_Min

float

0

9.3228e+35

0

The lower end of ages targeted for an intervention, in years. Used when Target_Demographic is set to ExplicitAgeRanges or ExplicitAgeRangesAndGender.

 
{
    "Target_Age_Max": 20,
    "Target_Age_Min": 10,
    "Target_Demographic": "ExplicitAgeRanges"
}

Target_Demographic

enum

NA

NA

Everyone

The target demographic group. Possible values are:

  • Everyone

  • ExplicitAgeRanges

  • ExplicitAgeRangesAndGender

  • ExplicitGender

  • ExplicitDiseaseState

 
{
    "Target_Age_Max": 20,
    "Target_Age_Min": 10,
    "Target_Demographic": "ExplicitAgeRanges"
}

Target_Gender

enum

NA

NA

All

Specifies the gender restriction for the intervention. Possible values are:

  • Male

  • Female

  • All

 
{
    "Target_Gender": "Female"
}

Target_Residents_Only

boolean

0

1

0

When set to true (1), the intervention is only distributed to individuals that began the simulation in the node (i.e. those that claim the node as their residence).

 
{
    "Target_Residents_Only": 1
}

Timesteps_Between_Repetitions

integer

-1

10000

-1

The repetition interval.

 
{
    "Event_Coordinator_Config": {
        "Completion_Event": "Done_Vaccinating_1n2",
        "Coordinator_Name": "1n2_Vaccinator",
        "Demographic_Coverage": 0.05,
        "Duration": -1,
        "Intervention_Config": {
            "Cost_To_Consumer": 1,
            "Vaccine_Take": 1,
            "Vaccine_Type": "AcquisitionBlocking",
            "Waning_Config": {
                "Box_Duration": 730,
                "Initial_Effect": 0.59,
                "class": "WaningEffectBox"
            },
            "class": "SimpleVaccine"
        },
        "Number_Repetitions": 1,
        "Start_Trigger_Condition_List": [
            "Start_Vaccinating_1n2"
        ],
        "Stop_Trigger_Condition_List": [],
        "Target_Demographic": "Everyone",
        "Timesteps_Between_Repetitions": 1,
        "class": "TriggeredEventCoordinator"
    }
}

Targeting_Config

JSON object

NA

NA

NA

Be more selective of individuals by using the Targeting_Config classes.

 
{
    "Targeting_Config": {
        "class": "HasIntervention",
        "Is_Equal_To": 0,
        "Intervention_Name": "MyVaccine"
    }
}

{    
    "Events": [
        {
            "class": "CampaignEvent",
            "Start_Day": 1,
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Target_Demographic": "Everyone",
                "Demographic_Coverage": 0.25,
                "Intervention_Config": {
                    "class": "OutbreakIndividual",
                    "Incubation_Period_Override": 0,
                    "Antigen": 0,
                    "Genome": 0
                }
            }
        },
        {
            "class": "CampaignEvent",
            "Start_Day": 0,
            "Nodeset_Config": {
                "class": "NodeSetNodeList",
                "Node_List": [ 1, 2 ]
            },
            "Event_Coordinator_Config": {
                "class": "TriggeredEventCoordinator",
                "Coordinator_Name": "1n2_Vaccinator",
                "Start_Trigger_Condition_List": [ "Start_Vaccinating_1n2" ],
                "Stop_Trigger_Condition_List": [],
                "Number_Repetitions": 1,
                "Timesteps_Between_Repetitions": 1,
                "Duration": -1,
                "Target_Demographic": "Everyone",
                "Node_Property_Restrictions": [],
                "Property_Restrictions_Within_Node": [],
                "Demographic_Coverage": 0.05,
                "Intervention_Config": {
                   "class": "SimpleVaccine",
                   "Cost_To_Consumer": 1,
                   "Vaccine_Take": 1,
                   "Vaccine_Type": "AcquisitionBlocking",
                   "Waning_Config": {
                      "class": "WaningEffectBox",
                      "Initial_Effect": 0.59,
                      "Box_Duration": 730
                   }
                },
                "Completion_Event": "Done_Vaccinating_1n2"
            }
        },        
        {
            "class": "CampaignEvent",
            "Start_Day": 0,
            "Nodeset_Config": {
                "class": "NodeSetNodeList",
                "Node_List": [ 1, 2 ]
            },
            "Event_Coordinator_Config": {
                "class": "TriggeredEventCoordinator",
                "Coordinator_Name": "1n2_Party",
                "Start_Trigger_Condition_List": [ "Done_Vaccinating_1n2" ],
                "Stop_Trigger_Condition_List": [],
                "Number_Repetitions": 1,
                "Timesteps_Between_Repetitions": 1,
                "Duration": -1,
                "Target_Demographic": "Everyone",
                "Node_Property_Restrictions": [],
                "Property_Restrictions_Within_Node": [],
                "Demographic_Coverage": 0.05,
                "Intervention_Config": {
                   "class": "BroadcastEvent",
                   "Cost_To_Consumer": 1,
                   "Broadcast_Event" : "Individual_Celebrate_1n2"
                },
                "Completion_Event": "Done_Celebrating_1n2"
            }
        },        
        {
            "class": "CampaignEvent",
            "Start_Day": 0,
            "Nodeset_Config": {
                "class": "NodeSetNodeList",
                "Node_List": [ 3, 4 ]
            },
            "Event_Coordinator_Config": {
                "class": "TriggeredEventCoordinator",
                "Coordinator_Name": "3n4_Party",
                "Start_Trigger_Condition_List": [ "Done_Vaccinating_3n4" ],
                "Stop_Trigger_Condition_List": [],
                "Number_Repetitions": 1,
                "Timesteps_Between_Repetitions": 1,
                "Duration": -1,
                "Target_Demographic": "Everyone",
                "Node_Property_Restrictions": [],
                "Property_Restrictions_Within_Node": [],
                "Demographic_Coverage": 0.05,
                "Intervention_Config": {
                   "class": "BroadcastEvent",
                   "Cost_To_Consumer": 1,
                   "Broadcast_Event" : "Individual_Celebrate_3n4"
                },
                "Completion_Event": "Done_Celebrating_3n4"
            }
        },        
        {
            "class": "CampaignEvent",
            "Start_Day": 0,
            "Nodeset_Config": {
                "class": "NodeSetNodeList",
                "Node_List": [ 3,4 ]
            },
            "Event_Coordinator_Config": {
                "class": "TriggeredEventCoordinator",
                "Coordinator_Name": "3n4_Vaccinator",
                "Start_Trigger_Condition_List": [ "Start_Vaccinating_3n4" ],
                "Stop_Trigger_Condition_List": ["Stop_Vaccinating_3n4"],
                "Number_Repetitions": 1,
                "Timesteps_Between_Repetitions": 11,
                "Duration": -1,
                "Target_Demographic": "Everyone",
                "Node_Property_Restrictions": [],
                "Property_Restrictions_Within_Node": [],
                "Demographic_Coverage": 0.05,
                "Intervention_Config": {
                   "class": "SimpleVaccine",
                   "Cost_To_Consumer": 2,
                   "Vaccine_Take": 1,
                   "Vaccine_Type": "AcquisitionBlocking",
                   "Waning_Config": {
                      "class": "WaningEffectBox",
                      "Initial_Effect": 0.59,
                      "Box_Duration": 730
                   }
                },
                "Completion_Event": "Done_Vaccinating_3n4"
            }
        },        
        {
            "class": "CampaignEvent",
            "Start_Day": 5,
            "Nodeset_Config": {
                "class": "NodeSetNodeList",
                "Node_List": [ 1, 2 ]
            },
            "Event_Coordinator_Config": {
                "class": "BroadcastCoordinatorEvent",
                "Coordinator_Name" : "Start_Coordnator_1n2",
                "Cost_To_Consumer" : 100,
                "Broadcast_Event" : "Start_Vaccinating_1n2"
            }
        },
        {
            "class": "CampaignEvent",
            "Start_Day": 20,
            "Nodeset_Config": {
                "class": "NodeSetNodeList",
                "Node_List": [ 3, 4 ]
            },
            "Event_Coordinator_Config": {
                "class": "BroadcastCoordinatorEvent",
                "Coordinator_Name" : "Start_Coordnator_3n4",
                "Cost_To_Consumer" : 200,
                "Broadcast_Event" : "Start_Vaccinating_3n4"
            }
        }
    ],
    "Use_Defaults": 1
}
Node-level interventions

Node-level interventions determine what will be distributed to each node to reduce the spread of a disease. For example, spraying larvicide in a village to kill mosquito larvae is a node-level malaria intervention. Sometimes this can be an intermediate intervention that schedules another intervention. Node-level disease outbreaks are also configured as “interventions”. In the schema, these are labeled as NodeTargeted.

It is also possible (but not required) to configure why a particular intervention is distributed by adding trigger conditions to the intervention. For example, interventions can be triggered by notifications broadcast after some an event, such as Births, NewInfectionEvent, and more. It’s also possible to have one intervention trigger another intervention by asking the first intervention to broadcast a unique string, and having the second intervention be triggered upon receipt of that string. See Event list.

Vector control

The following node-level interventions are commonly used for vector control.

Intervention

Target life stage

Target biting preference

Target biting location

Effect

AnimalFeedKill

node

feeding cycle

animal

killing

ArtificialDiet

feeding cycle

human

all

blocking

Larvicides

larva

all

all

killing, reduction

MosquitoRelease

OutdoorRestKill

feeding cycle

human

outdoor

killing

OvipositionTrap

feeding cycle

all

all

killing

ScaleLarvalHabitat

larva

all

all

reduction

SpaceSpraying

feeding cycle

human

outdoor

killing

SpatialRepellent

feeding cycle

all

outdoor

blocking

SugarTrap

adults

all

all

killing
Summary table of node-level interventions

The following table provides an at-a-glance overview of the node-level interventions.

Intervention

Short description

Able to be serialized?

Uses insecticides

Time-based expiration?

Purge existing?

Vector killing contributes to:

Vector effects

AnimalFeedKill

Kill vectors when feeding on animals

Y

Y

Die Before Attempting Human Feed

Killing

ArtificialDiet

Kill vectors when feeding on artificial diet

Y

Attract

BroadcastNodeEvent

Send a node event to node

IndoorSpaceSpraying

IRS for people within a node

Y

Y

Indoor Die After Feeding

Killing

InputEIR

Deliver infectious bites without vectors

Y

Larvicides

Kill larva in node

Y

Y

Larva

Larval killing

MalariaChallenge

Deliver infectious bites or sporozoites with out vectors

MigrateFamily

Schedule trip for family groups in a node

MosquitoRelease

Add vectors to a node

MultiInsecticideIndoorSpaceSpraying

Node IRS involving multiple insecticides

Y

Y

Indoor Die After Feeding

Killing

MultiInsecticideSpaceSpraying

Outdoor spraying involving multiple insecticides

Y

Y

Die Without Attempting To Feed, Die Before Attempting Human Feed

Killing

MultiNodeInterventionDistributor

Distribute multiple interventions instead of one

Y/N

NLHTIVNode

Distribute node intervention on node event

Y/N

NodeLevelHealthTriggeredIV

Distribute individual intervention on individual event

Y

NodePropertyValueChanger

Change the NodeProperty of a node

Outbreak

Not tested with Malaria

OutdoorRestKill

Kill vector after feeding outdoors

Y

Outdoor Die After Feeding

Killing

OvipositionTrap

Kill vector attempting to lay eggs

Y

Die Laying Eggs

Killing

ScaleLarvalHabitat

Modify capacity of larval habitat

Sort of

SpaceSpraying

Outdoor insecticide spraying

Y

Y

Die Without Attempting To Feed, Die Before Attempting Human Feed

Killing

SpatialRepellent

Block vectors before they can attempt to feed on humans

Y

Y

Repelling

SugarTrap

kill vectors when sugar feeding

Y

Y

Y

Emerging, Trap Feeding

Killing
AnimalFeedKill

The AnimalFeedKill intervention class imposes node-targeted mortality to a vector that feeds on animals.

At a glance:

  • Distributed to: Nodes

  • Serialized: No, it needs to be redistributed when starting from a serialized file.

  • Uses insecticides: Yes, can be used to target sub-groups using genomes.

  • Time-based expiration: No

  • Purge existing: Yes. If a new intervention is added to to the node, the existing intervention of the same name is removed when the new one is added.

  • Vector killing contributes to: Die Before Attempting to Feed

  • Vector effects: Killing

  • Vector sexes affected: Females only

  • Vector life stage affected: Adult

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Cost_To_Consumer

float

0

999999

10

The unit cost per vector control (unamortized).

 
{
    "Cost_To_Consumer": 8
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of NodeProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to nodes with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Insecticide_Name

string

NA

NA

UNINITIALIZED STRING

The name of the insecticide defined in <config.Insecticides> for this intervention. If insecticides are being used, this must be defined as one of those values; if they are not being used this can be empty. It cannot have a value if you did not configure <config.Insecticides>.

 
{
    "Insecticide_Name": "carbamate"
}

Intervention_Name

string

NA

NA

AnimalFeedKill

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Name": "Diagnostic_Sample"
}

Killing_Config

JSON object

NA

NA

NA

The configuration of killing efficacy of the targeted stage. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Killing_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0.53429,
        "class": "WaningEffectBox"
    }
}

New_Property_Value

string

NA

NA

NA

An optional NodeProperty key:value pair that will be assigned when the intervention is first updated. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

{
    "Events": [{
        "class": "CampaignEvent",
        "Start_Day": 120,
        "Nodeset_Config": {
            "class": "NodeSetAlld"
        },
        "Event_Coordinator_Config": {
            "class": "NodeEventCoordinator",
            "Intervention_Config": {
                "class": "AnimalFeedKill",
                "Cost_To_Consumer": 10.0,
                "Killing_Config": {
                    "Box_Duration": 100,
                    "Decay_Time_Constant": 150,
                    "Initial_Effect": 0.2,
                    "class": "WaningEffectBoxExponential"
                }
            }
        }
    }],
    "Use_Defaults": 1
}
ArtificialDiet

The ArtificialDiet intervention class is used to include feeding stations for vectors within a node in a simulation. This is a node-targeted intervention and takes effect at the broad village level rather than at the individual level. For individual-level effects, use HumanHostSeekingTrap instead. An artificial diet diverts some of the vectors seeking to blood feed that day, resulting in a two-fold benefit:

  1. The uninfected mosquitoes avoid biting infected humans some of the time, therefore decreasing the amount of human-to-vector transmission.

  2. Infectious vectors are diverted to feed on the artificial diet instead of the humans, therefore decreasing the amount of vector-to-human transmission.

At a glance:

  • Distributed to: Nodes

  • Serialized: No, it needs to be redistributed when starting from a serialized file.

  • Uses insecticides: No

  • Time-based expiration: No

  • Purge existing: Yes. If a new intervention is added to to the node, the existing intervention of the same name is removed when the new one is added.

  • Vector killing contributes to: No killing

  • Vector effects: Artificial Diet Feed instead of Human or Animal Feed

  • Vector sexes affected: Females only

  • Vector life stage affected: Adult

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Artificial_Diet_Target

enum

NA

NA

AD_WithinVillage

The targeted deployment of artificial diet. This parameter is required. Possible values are:

  • AD_WithinVillage, which is impacted by both SpaceSpraying and SpatialRepellent

  • AD_OutsideVillage, which is only impacted by SpaceSpraying

 
{
    "Artificial_Diet_Target": "AD_WithinVillage"
}

Attraction_Config

JSON object

NA

NA

NA

The fraction of vector feeds attracted to the artificial diet. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Attraction_Config": {
        "Initial_Effect": 0.5,
        "Box_Duration": 100,
        "Decay_Time_Constant": 150,
        "class": "WaningEffectBoxExponential"
    }
}

Cost_To_Consumer

float

0

999999

10

The unit cost per vector control (unamortized).

 
{
    "Cost_To_Consumer": 8
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of NodeProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to nodes with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Intervention_Name

string

NA

NA

ArtificialDiet

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "ArtificialDiet",
        "Intervention_Name": "Feeding station in node 7"
    }
}

New_Property_Value

string

NA

NA

NA

An optional NodeProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

{
    "Events": [{
        "class": "CampaignEvent",
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 120,
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Intervention_Config": {
                "class": "ArtificialDiet",
                "Artificial_Diet_Target": "AD_WithinVillage",
                "Attraction_Config": {
                    "Initial_Effect": 0.5,
                    "Box_Duration": 100,
                    "Decay_Time_Constant": 150,
                    "class": "WaningEffectBoxExponential"
                },
                "Cost_To_Consumer": 10.0
            }
        }
    }],
    "Use_Defaults": 1
}
BroadcastNodeEvent

Note

This campaign class and associated parameters are currently in beta release and have not yet been fully tested.

The BroadcastNodeEvent node intervention class broadcasts node events. This can be used with the campaign class, SurveillanceEventCoordinator, that can monitor and listen for events received from BroadcastNodeEvent and then perform an action based on the broadcasted event. You can also use this for the reporting of the broadcasted events by setting the configuraton parameters, Report_Node_Event_Recorder and Report_Surveillance_Event_Recorder, which listen to events to be recorded. You must use this coordinator class with listeners that are operating on the same core. You can also use NLHTIVNode. For more information, see Simulation core components.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Broadcast_Event

string

NA

NA

UNINITIALIZED STRING

The name of the node event to broadcast. This event must be set in the Custom_Node_Events configuration parameter. Note that there are no built-in node events for malaria.

 
{
    "class": "CampaignEvent",
    "Start_Day": 5,
    "Nodeset_Config": {
        "class": "NodeSetNodeList",
        "Node_List": [
            1
        ]
    },
    "Event_Coordinator_Config": {
        "class": "StandardInterventionDistributionEventCoordinator",
        "Intervention_Config": {
            "class": "BroadcastNodeEvent",
            "Cost_To_Consumer": 10,
            "Broadcast_Event": "Node_Event_1"
        }
    }
}

Cost_To_Consumer

float

0

999999

0

The unit cost of the intervention campaign that will be assigned to the specified nodes, as configured under Nodeset_Config.

 
{
    "class": "CampaignEvent",
    "Start_Day": 5,
    "Nodeset_Config": {
        "class": "NodeSetNodeList",
        "Node_List": [
            1
        ]
    },
    "Event_Coordinator_Config": {
        "class": "StandardInterventionDistributionEventCoordinator",
        "Intervention_Config": {
            "class": "BroadcastNodeEvent",
            "Cost_To_Consumer": 10,
            "Broadcast_Event": "Node_Event_1"
        }
    }
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of NodeProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Intervention_Name

string

NA

NA

BroadcastNodeEvent

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "BroadcastNodeEvent",
        "Intervention_Name": "Mosquito coil repellent"
    }
}

New_Property_Value

string

NA

NA

NA

An optional NodeProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

{
    "Events": [
        {
            "class": "CampaignEvent",
            "Start_Day": 1,
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Target_Demographic": "Everyone",
                "Demographic_Coverage": 0.5,
                "Intervention_Config": {
                    "class": "OutbreakIndividual",
                    "Incubation_Period_Override": 0,
                    "Antigen": 0,
                    "Genome": 0
                }
            }
        },
        {
            "class": "CampaignEvent",
            "Start_Day": 5,
            "Nodeset_Config": {
                "class": "NodeSetNodeList",
                "Node_List": [ 1 ]
            },
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Intervention_Config": {
                    "class": "BroadcastNodeEvent",
                    "Cost_To_Consumer" : 10,
                    "Broadcast_Event" : "Node_Event_1"
                }
            }
        },
        {
            "class": "CampaignEvent",
            "Start_Day": 15,
            "Nodeset_Config": {
                "class": "NodeSetNodeList",
                "Node_List": [ 2,3 ]
            },
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Intervention_Config": {
                    "class": "BroadcastNodeEvent",
                    "Cost_To_Consumer" : 20,
                    "Broadcast_Event" : "Node_Event_2"
                }
            }
        },
        {
            "class": "CampaignEvent",
            "Start_Day": 25,
            "Nodeset_Config": { "class": "NodeSetAll" },
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Intervention_Config": {
                    "class": "BroadcastNodeEvent",
                    "Cost_To_Consumer" : 25,
                    "Broadcast_Event" : "Node_Event_3"
                }
            }
        }
    ],
    "Use_Defaults": 1
}
IndoorSpaceSpraying

The IndoorSpaceSpraying intervention class is a node-level vector control mechanism that works by spraying insecticides indoors. This class is similar to to IRSHousingModification but IRSHousingModification is an individual-level intervention that uses both killing and blocking effects and IndoorSpaceSpraying is a node-level intervention that uses only a killing effect. Do not use these two interventions together.

At a glance:

  • Distributed to: Nodes

  • Serialized: No, it needs to be redistributed when starting from a serialized file.

  • Uses insecticides: Yes. It can target subgroups using genomes, especially when targeting certain species.

  • Time-based expiration: No

  • Purge existing: Yes. If a new intervention is added to to the node, the existing intervention of the same name is removed when the new one is added.

  • Vector killing contributes to: Indoor Die After Feeding

  • Vector effects: Killing

  • Vector sexes affected: Females only

  • Vector life stage affected: Adult

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Cost_To_Consumer

float

0

999999

10

The unit cost per trap (unamortized).

 
{
    "Cost_To_Consumer": 4.5
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Insecticide_Name

string

NA

NA

UNINITIALIZED STRING

The name of the insecticide defined in <config.Insecticides> for this intervention. If insecticides are being used, this must be defined as one of those values; if they are not being used this can be empty. It cannot have a value if you did not configure <config.Insecticides>.

 
{
    "Insecticide_Name": "carbamate"
}

Intervention_Name

string

NA

NA

IndoorSpaceSpraying

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "Indoor Spraying",
        "Intervention_Name": "Trap to lure and kill mosquitoes"
    }
}

Killing_Config

JSON object

NA

NA

NA

The configuration of killing efficacy and waning for human host-seeking trap. Only vectors attracted to the trap by the Attract_Config settings are targeted by the Killing_Config. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Killing_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0.9,
        "class": "WaningEffectBox"
    }
}

New_Property_Value

string

NA

NA

UNINITIALIZED STRING

An optional IndividualProperty key:value pair that will be assigned when the intervention is first updated. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Spray_Coverage

float

0

1

0

The proportion of the node that has been sprayed. This value is multiplied by the current efficacy of the WaningEffect.

 
{
    "Cost_To_Consumer": 4.5
}

{
    "class": "IndoorSpaceSpraying",
    "Insecticide_Name": "pyrethroid",
    "Spray_Coverage" : 0.9,
    "Killing_Config": {
        "class": "WaningEffectBoxExponential",
        "Box_Duration": 100,
        "Decay_Time_Constant": 150,
        "Initial_Effect": 1
    }
}
InputEIR

The InputEIR intervention class enables the Entomological Inoculation Rate (EIR) to be configured for each month of the year in a particular node. The EIR is the number of infectious mosquito bites received in a night. This number can be calculated by taking the number of mosquito bites received per night and multiplying them by the proportion of those bites that are positive for sporozoites.

This intervention class can be used instead of including vectors in the model, as it will distribute infections to people (it does not model vector biting). However, if vectors are included when this class is implemented, it will add the EIR specified for that month in addition to the EIR provided by the vectors. Note that the Daily EIR channel in the InsetChart will not be impacted by this intervention. When distributing InputEIR to a node that already has an existing InputEIR intervention, the existing intervention of the same name will be purged and replaced by the new one.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Age_Dependence

enum

NA

NA

OFF

Determines how InputEIR depends on the age of the target. Possible values are:

OFF

Age has no effect.

LINEAR

The biting risk for newborns is 20% of the adult exposure and rises linearly to 100% at age 20.

SURFACE_AREA_DEPENDENT

The biting risk rises linearly from 7% to 23% over the first two years of life and then rises with a shallower linear slope to the adult value of 100% at age 20.

 
{
    "Age_Dependence": "SURFACE_AREA_DEPENDENT"
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of NodeProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to nodes with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Intervention_Name

string

NA

NA

InputEIR

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "InputEIR",
        "Intervention_Name": "Entomological Inoculation Rate config"
    }
}

Monthly_EIR

array of floats

0

1000

NA

An array of exactly 12 elements that contain an entomological inoculation rate (EIR) for each month. Each value should be between 0 and 1000, there is no default. Note that the ‘current month’ is based on the time since the intervention was distributed, and there will be a 12-month repeating pattern from the day it’s distributed.

 
{
    "Monthly_EIR": [
        0.39,
        0.19,
        0.77,
        0,
        0,
        0,
        6.4,
        2.2,
        4.7,
        3.9,
        0.87,
        0.58
    ]
}

New_Property_Value

string

NA

NA

NA

An optional NodeProperty key:value pair that will be assigned when the intervention is first updated. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

{
     "Use_Defaults": 1,
     "Campaign_Name": "Constant EIR challenge",
     "Events": [{
          "class": "CampaignEvent",
          "Event_Name": "Input EIR intervention",
          "Start_Day": 0,
          "Nodeset_Config": {
               "class": "NodeSetAll"
          },
          "Event_Coordinator_Config": {
               "class": "StandardInterventionDistributionEventCoordinator",
               "Number_Repetitions": 1,
               "Intervention_Config": {
                    "class": "InputEIR",
                    "Age_Dependence": "SURFACE_AREA_DEPENDENT",
                    "Monthly_EIR": [0.39, 0.19, 0.77, 0, 0, 0, 6.4, 2.2, 4.7, 3.9, 0.87, 0.58]
               }
          }
     }]
}
Larvicides

The Larvicides intervention class is a node-level intervention that configures habitat reduction for mosquito breeding sites that are poisoned by larvicides. This intervention accommodates sprayed areas and vector-transported larvicides where resting surfaces may be treated not just with IRSHousingModification to kill mosquitoes, but also with particles that can be dispersed by mosquitoes to poison larval development sites. In particular, a relatively small fraction of resting events may result in a relatively large fraction of larval habitat.

At a glance:

  • Distributed to: Nodes

  • Serialized: No, it needs to be redistributed when starting from a serialized file.

  • Uses insecticides: Yes. The vector genome can be used to target specific genders.

  • Time-based expiration: No. It will continue to exist even if efficacy is zero.

  • Purge existing: Yes. If a new intervention is added to to the node, the existing intervention of the same name is removed when the new one is added.

  • Vector killing contributes to: Combines with competition and rainfall to kill larvae every time step.

  • Vector effects: Killing

  • Vector sexes affected: Both males and females

  • Vector life stage affected: Larval

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Cost_To_Consumer

float

0

999999

10

The unit cost per vector control (unamortized).

 
{
    "Cost_To_Consumer": 8
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of NodeProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to nodes with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Habitat_Target

enum

NA

NA

NONE

The larval habitat type targeted for larvicide. This is a required parameter. NONE is the default to ensure the user selects it. If not ALL_HABITATS, then the type must be one of those configured in Vector_Species_Params, Habitats. See Larval habitat parameters for more information. Possible values are:

  • NONE - Default but not allowed; it must be defined.

  • TEMPORARY_RAINFALL

  • WATER_VEGETATION

  • HUMAN_POPULATION

  • CONSTANT

  • BRACKISH_SWAMP

  • LINEAR_SPLINE

  • ALL_HABITATS

 
{
    "Habitat_Target": "WATER_VEGETATION"
}

Insecticide_Name

string

NA

NA

UNINITIALIZED STRING

The name of the insecticide defined in the configuration parameter Insecticides for this intervention. If insecticides are being used, this must be defined as one of those values; if they are not being used this can be empty. It cannot have a value if you did not configure the configuration parameter Insecticides.

 
{
    "Insecticide_Name": "carbamate"
}

Intervention_Name

string

NA

NA

Larvicides

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "Larvicides",
        "Intervention_Name": "Themiphos application"
    }
}

Larval_Killing_Config

JSON object

NA

NA

NA

The configuration of larval killing efficacy and waning for targeted stage. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Larval_Killing_Config": {
        "Box_Duration": 100,
        "Decay_Time_Constant": 150,
        "Initial_Effect": 0.2,
        "class": "WaningEffectBoxExponential"
    }
}

New_Property_Value

string

NA

NA

NA

An optional NodeProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Spray_Coverage

float

0

1

1

The portion of the node that has been sprayed. This value is multiplied by the current efficacy of the WaningEffect.

 
{
    "Spray_Coverage": 0.6
}

{
    "Events": [{
        "class": "CampaignEvent",
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 140,
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Intervention_Config": {
                "Cost_To_Consumer": 3.75,
                "Spray_Coverage": 0.6,
                "Habitat_Target": "ALL_HABITATS",
                "Larval_Killing_Config": {
                    "Box_Duration": 3650,
                    "Initial_Effect": 0.1,
                    "class": "WaningEffectBox"
                },
                "class": "Larvicides"
            }
        }
    }],
    "Use_Defaults": 1
}
MalariaChallenge

The MalariaChallenge intervention class is a node-level intervention similar to Outbreak. However, instead of distributing infections, it distributes malaria challenges by either tracking numbers of sporozoites or infectious mosquito bites.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Challenge_Type

enum

NA

NA

NA

The type of malaria challenge. Possible values are:

InfectiousBites

If this value is selected, use with the corresponding Infectious_Bite_Count parameter.

Sporozoites

If this value is selected, use with the corresponding Sporozoite_Count parameter.

 
{
    "Challenge_Type": "InfectiousBites"
}

Coverage

float

0

1

1

The fraction of individuals receiving an intervention.

 
{
    "Coverage": 1.0
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Infectious_Bite_Count

integer

0

1000

1

The number of infectious bites per person. Use when Challenge_Type is set to InfectiousBites.

 
{
    "Infectious_Bite_Count": 2
}

Intervention_Name

string

NA

NA

MalariaChallenge

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "MalariaChallenge",
        "Intervention_Name": "Mosquito challenge by sporozoites"
    }
}

New_Property_Value

string

NA

NA

NA

An optional NodeProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Sporozoite_Count

integer

0

1000

1

The number of sporozoites per person. Use when Challenge_Type is set to Sporozoites.

 
{
    "Sporozoite_Count": 3
}

{
    "Use_Defaults": 1,
    "Events": [{
        "class": "CampaignEvent",
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 40,
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Intervention_Config": {
                "class": "MalariaChallenge",
                "Challenge_Type": "InfectiousBites",
                "Coverage": 1.0,
                "Infectious_Bite_Count": 2,
                "Sporozoite_Count": 3
            }
        }
    }]
}
MigrateFamily

The MigrateFamily intervention class tells family groups of residents of the targeted node to go on a round trip migration (“family trip”). The duration of time residents wait before migration and the time spent at the destination node can be configured; the pre-migration waiting timer does not start until all residents are at the home node.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Disqualifying_Properties

array of strings

NA

NA

[]

A list of NodeProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to nodes with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Duration_At_Node_Constant

float

0

3.40282E+38

-1

The duration at destination node, in days, to use for all families when Duration_At_Node_Distribution is set to CONSTANT_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "CONSTANT_DISTRIBUTION",
    "Duration_At_Node_Constant": 8
}

Duration_At_Node_Distribution

enum

NA

NA

NOT_INITIALIZED

The distribution type to use for assigning the duration of time an individual or family spends at a destination node after intervention-based migration. Each assigned value is a random draw from the distribution.

Possible values are:

NOT_INITIALIZED

No distribution set.

CONSTANT_DISTRIBUTION

Use the same value for each individual. Set Duration_At_Node_Constant.

UNIFORM_DISTRIBUTION

Use a uniform distribution with a given minimum and maximum. Set Duration_At_Node_Max and Duration_At_Node_Min.

GAUSSIAN_DISTRIBUTION

The distribution is Gaussian (or normal). Values are resampled to ensure >= 0. Set Duration_At_Node_Gaussian_Mean and Duration_At_Node_Gaussian_Std_Dev.

EXPONENTIAL_DISTRIBUTION

The distribution is exponential with a given mean. Set Duration_At_Node_Exponential.

WEIBULL_DISTRIBUTION

Use a Weibull distribution with a given shape and scale. Set Duration_At_Node_Kappa and Duration_At_Node_Lambda.

LOG_NORMAL_DISTRIBUTION

Use a log-normal distribution with a given mean and standard deviation of the natural log. Set Duration_At_Node_Log_Normal_Mu and Duration_At_Node_Log_Normal_Sigma.

POISSON_DISTRIBUTION

Use a Poisson distribution with a given mean. Set Duration_At_Node_Poisson_Mean.

DUAL_CONSTANT_DISTRIBUTION

Use a distribution where some individuals are set to a value of zero and the rest to a given value. Set Duration_At_Node_Proportion_0 and Peak_2_Value. This distribution does not use the parameters set for CONSTANT_DISTRIBUTION.

DUAL_EXPONENTIAL_DISTRIBUTION

Use two exponential distributions with given means. Set Duration_At_Node_Mean_1, Duration_At_Node_Mean_2, and Duration_At_Node_Proportion_1. This distribution does not use the parameters set for EXPONENTIAL_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "WEIBULL_DISTRIBUTION",
    "Duration_At_Node_Kappa": 0.9,
    "Duration_At_Node_Lambda": 1.5
}

Duration_At_Node_Exponential

float

0

3.40282E+38

-1

The mean of the duration at the destination node after migration when Duration_At_Node_Distribution is set to EXPONENTIAL_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "EXPONENTIAL_DISTRIBUTION",
    "Duration_At_Node_Exponential": 4.25
}

Duration_At_Node_Gaussian_Mean

float

0

3.40282E+38

-1

The mean of the duration at the destination node after migration, in days, when Duration_At_Node_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Duration_At_Node_Gaussian_Mean": 8,
    "Duration_At_Node_Gaussian_Std_Dev": 1.5
}

Duration_At_Node_Gaussian_Std_Dev

float

1.17549E-38

3.40282E+38

-1

The standard deviation of the duration at the destination node after migration, in days, when Duration_At_Node_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Duration_At_Node_Gaussian_Mean": 8,
    "Duration_At_Node_Gaussian_Std_Dev": 1.5
}

Duration_At_Node_Kappa

float

1.17549E-38

3.40282E+38

-1

The shape value for the duration at the destination node after migration, in days, when Duration_At_Node_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "WEIBULL_DISTRIBUTION",
    "Duration_At_Node_Kappa": 0.9,
    "Duration_At_Node_Lambda": 1.5
}

Duration_At_Node_Lambda

float

1.17549E-38

3.40282E+38

-1

The scale value for the duration at the destination node after migration, in days, when Duration_At_Node_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "WEIBULL_DISTRIBUTION",
    "Duration_At_Node_Kappa": 0.9,
    "Duration_At_Node_Lambda": 1.5
}

Duration_At_Node_Log_Normal_Mu

float

-3.40282e+38

1.70141e+38

3.40282E+38

The mean of the natural log of the duration at the destination node after migration, in days, when Duration_At_Node_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Duration_At_Node_Log_Normal_Mu": 9,
    "Duration_At_Node_Log_Normal_Sigma": 2
}

Duration_At_Node_Log_Normal_Sigma

float

-3.40282e+38

1.70141e+38

3.40282E+38

The standard deviation of the natural log of the duration at the destination node after migration, in days, when Duration_At_Node_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Duration_At_Node_Log_Normal_Mu": 9,
    "Duration_At_Node_Log_Normal_Sigma": 2
}

Duration_At_Node_Max

float

0

3.40282E+38

-1

The maximum duration at the destination node after migration, in days, when Duration_At_Node_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "UNIFORM_DISTRIBUTION",
    "Duration_At_Node_Min": 2,
    "Duration_At_Node_Max": 7
}

Duration_At_Node_Mean_1

float

1.17549E-38

3.40282E+38

-1

The mean of the first exponential distribution, in days, when Duration_At_Node_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Duration_At_Node_Mean_1": 4,
    "Duration_At_Node_Mean_2": 12,
    "Duration_At_Node_Proportion_1": 0.2
}

Duration_At_Node_Mean_2

float

1.17549E-38

3.40282E+38

-1

The mean of the second exponential distribution, in days, when Duration_At_Node_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Duration_At_Node_Mean_1": 4,
    "Duration_At_Node_Mean_2": 12,
    "Duration_At_Node_Proportion_1": 0.2
}

Duration_At_Node_Min

float

0

3.40282E+38

-1

The minimum duration at the destination node after migration, in days, when Duration_At_Node_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "UNIFORM_DISTRIBUTION",
    "Duration_At_Node_Min": 2,
    "Duration_At_Node_Max": 7
}

Duration_At_Node_Peak_2_Value

float

0

3.40282E+38

-1

The duration at node value, in days, to assign to the remaining families when Duration_At_Node_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Duration_At_Node_Proportion_0": 0.25,
    "Duration_At_Node_Peak_2_Value": 5
}

Duration_At_Node_Poisson_Mean

float

0

3.40282E+38

-1

The mean of the duration at the destination node after migration, in days, when Duration_At_Node_Distribution is set to POISSON_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "POISSON_DISTRIBUTION",
    "Duration_At_Node_Poisson_Mean": 5
}

Duration_At_Node_Proportion_0

float

0

1

-1

The proportion of families to assign a value of zero days at node when Duration_At_Node_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Duration_At_Node_Proportion_0": 0.25,
    "Duration_At_Node_Peak_2_Value": 5
}

Duration_At_Node_Proportion_1

float

0

1

-1

The proportion of families in the first exponential distribution when Duration_At_Node_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Duration_At_Node_Mean_1": 4,
    "Duration_At_Node_Mean_2": 12,
    "Duration_At_Node_Proportion_1": 0.2
}

Duration_Before_Leaving_Constant

float

0

3.40282E+38

-1

The duration before leaving to use for every family, in days, when Duration_Before_Leaving_Distribution is set to CONSTANT_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "CONSTANT_DISTRIBUTION",
    "Duration_Before_Leaving_Constant": 8
}

Duration_Before_Leaving_Distribution

enum

NA

NA

NOT_INITIALIZED

The distribution type to use for assigning the duration of time an individual or family waits before migrating to the destination node after all residents are home. Each assigned value is a random draw from the distribution.

Possible values are:

NOT_INITIALIZED

No distribution set.

CONSTANT_DISTRIBUTION

Use the same value for each individual. Set Duration_Before_Leaving_Constant.

UNIFORM_DISTRIBUTION

Use a uniform distribution with a given minimum and maximum. Set Duration_Before_Leaving_Max and Duration_Before_Leaving_Min.

GAUSSIAN_DISTRIBUTION

The distribution is Gaussian (or normal). Values are resampled to ensure >= 0. Set Duration_Before_Leaving_Gaussian_Mean and Duration_Before_Leaving_Gaussian_Std_Dev.

EXPONENTIAL_DISTRIBUTION

The distribution is exponential with a given mean. Set Duration_Before_Leaving_Exponential.

WEIBULL_DISTRIBUTION

Use a Weibull distribution with a given shape and scale. Set Duration_Before_Leaving_Kappa and Duration_Before_Leaving_Lambda.

LOG_NORMAL_DISTRIBUTION

Use a log-normal distribution with a given mean and standard deviation of the natural log. Set Duration_Before_Leaving_Log_Normal_Mu and Duration_Before_Leaving_Log_Normal_Sigma.

POISSON_DISTRIBUTION

Use a Poisson distribution with a given mean. Set Duration_Before_Leaving_Poisson_Mean.

DUAL_CONSTANT_DISTRIBUTION

Use a distribution where some individuals are set to a value of zero and the rest to a given value. Set Duration_Before_Leaving_Proportion_0 and Peak_2_Value. This distribution does not use the parameters set for CONSTANT_DISTRIBUTION.

DUAL_EXPONENTIAL_DISTRIBUTION

Use two exponential distributions with given means. Set Duration_Before_Leaving_Mean_1, Duration_Before_Leaving_Mean_2, and Duration_Before_Leaving_Proportion_1. This distribution does not use the parameters set for EXPONENTIAL_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "UNIFORM_DISTRIBUTION",
    "Duration_Before_Leaving_Min": 2,
    "Duration_Before_Leaving_Max": 7
}

Duration_Before_Leaving_Exponential

float

0

3.40282E+38

-1

The mean of the duration before leaving the home node, in days, when Duration_Before_Leaving_Distribution is set to EXPONENTIAL_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "EXPONENTIAL_DISTRIBUTION",
    "Duration_Before_Leaving_Exponential": 4.25
}

Duration_Before_Leaving_Gaussian_Mean

float

0

3.40282E+38

-1

The mean of the duration before leaving the home node, in days, when Duration_Before_Leaving_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Duration_Before_Leaving_Gaussian_Mean": 8,
    "Duration_Before_Leaving_Gaussian_Std_Dev": 1.5
}

Duration_Before_Leaving_Gaussian_Std_Dev

float

1.17549E-38

3.40282E+38

1

The standard deviation of the duration before leaving the home node, in days, when Duration_Before_Leaving_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Duration_Before_Leaving_Gaussian_Mean": 8,
    "Duration_Before_Leaving_Gaussian_Std_Dev": 1.5
}

Duration_Before_Leaving_Kappa

float

1.17549E-38

3.40282E+38

-1

The shape value for the duration before leaving the home node, in days, when Duration_Before_Leaving_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "WEIBULL_DISTRIBUTION",
    "Duration_Before_Leaving_Kappa": 0.9,
    "Duration_Before_Leaving_Lambda": 1.5
}

Duration_Before_Leaving_Lambda

float

1.17549E-38

3.40282E+38

-1

The scale value for the duration before leaving the home node, in days, when Duration_Before_Leaving_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "WEIBULL_DISTRIBUTION",
    "Duration_Before_Leaving_Kappa": 0.9,
    "Duration_Before_Leaving_Lambda": 1.5
}

Duration_Before_Leaving_Log_Normal_Mu

float

-3.40282e+38

1.70141e+38

3.40282E+38

The mean of the natural log of the duration before leaving the home node, in days, when Duration_Before_Leaving_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Duration_Before_Leaving_Log_Normal_Mu": 9,
    "Duration_Before_Leaving_Log_Normal_Sigma": 2
}

Duration_Before_Leaving_Log_Normal_Sigma

float

-3.40282e+38

1.70141e+38

3.40282E+38

The standard deviation of the natural log of the duration before leaving the home node, in days, when Duration_Before_Leaving_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Duration_Before_Leaving_Log_Normal_Mu": 9,
    "Duration_Before_Leaving_Log_Normal_Sigma": 2
}

Duration_Before_Leaving_Max

float

0

3.40282E+38

-1

The maximum duration before leaving the home node, in days, when Duration_Before_Leaving_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "UNIFORM_DISTRIBUTION",
    "Duration_Before_Leaving_Min": 2,
    "Duration_Before_Leaving_Max": 7
}

Duration_Before_Leaving_Mean_1

float

1.17549E-38

3.40282E+38

-1

The mean of the first exponential distribution, in days, when Duration_Before_Leaving_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Duration_Before_Leaving_Mean_1": 4,
    "Duration_Before_Leaving_Mean_2": 12,
    "Duration_Before_Leaving_Proportion_1": 0.2
}

Duration_Before_Leaving_Mean_2

float

1.17549E-38

3.40282E+38

-1

The mean of the second exponential distribution, in days, when Duration_Before_Leaving_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Duration_Before_Leaving_Mean_1": 4,
    "Duration_Before_Leaving_Mean_2": 12,
    "Duration_Before_Leaving_Proportion_1": 0.2
}

Duration_Before_Leaving_Min

float

0

3.40282E+38

-1

The minimum duration before leaving the home node, in days, when Duration_Before_Leaving_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "UNIFORM_DISTRIBUTION",
    "Duration_Before_Leaving_Min": 2,
    "Duration_Before_Leaving_Max": 7
}

Duration_Before_Leaving_Peak_2_Value

float

0

3.40282E+38

-1

The duration before leaving the home node to assign to the remaining families, in days, when Duration_Before_Leaving_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Duration_Before_Leaving_Proportion_0": 0.25,
    "Duration_Before_Leaving_Peak_2_Value": 5
}

Duration_Before_Leaving_Poisson_Mean

float

0

3.40282E+38

-1

The mean of the duration before leaving the home node, in days, when Duration_Before_Leaving is set to POISSON_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "POISSON_DISTRIBUTION",
    "Duration_Before_Leaving_Poisson_Mean": 5
}

Duration_Before_Leaving_Proportion_0

float

0

1

-1

The proportion of families to assign a value of zero days before leaving the home node when Duration_Before_Leaving_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Duration_Before_Leaving_Proportion_0": 0.25,
    "Duration_Before_Leaving_Peak_2_Value": 5
}

Duration_Before_Leaving_Proportion_1

float

0

1

-1

The proportion of families in the first exponential distribution when Duration_Before_Leaving_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Duration_Before_Leaving_Mean_1": 4,
    "Duration_Before_Leaving_Mean_2": 12,
    "Duration_Before_Leaving_Proportion_1": 0.2
}

Intervention_Name

string

NA

NA

MigrateFamily

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "MigrateFamily",
        "Intervention_Name": "Move family for seasonal work"
    }
}

Is_Moving

boolean

NA

NA

0

Set to true (1) to indicate the individual is permanently moving to a new home node for intervention-based migration.

 
{
    "Is_Moving": 1
}

New_Property_Value

string

NA

NA

NA

An optional NodeProperty key:value pair that will be assigned when the intervention is first updated. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

NodeID_To_Migrate_To

integer

0

4.29E+09

0

The destination node ID for intervention-based migration.

 
{
    "NodeID_To_Migrate_To": 26
}

{
  "Use_Defaults": 1,
  "Events": [
    {
      "class": "CampaignEvent",
      "Start_Day": 1,
      "Nodeset_Config": {
        "class": "NodeSetAll"
      },
      "Event_Coordinator_Config": {
        "class": "StandardInterventionDistributionEventCoordinator",
        "Intervention_Config": {
          "class": "NodeLevelHealthTriggeredIV",
          "Trigger_Condition_List": [
            "NewInfectionEvent"
          ],
          "Demographic_Coverage": 1.0,
          "Actual_NodeIntervention_Config": {
            "class": "MigrateFamily",
            "NodeID_To_Migrate_To": 4,
            "Duration_Before_Leaving_Distribution": "CONSTANT_DISTRIBUTION",
            "Duration_At_Node_Distribution": "CONSTANT_DISTRIBUTION",
            "Is_Moving": 0,
            "Duration_Before_Leaving_Constant": 0,
            "Duration_At_Node_Constant": 10
          }
        }
      }
    }
  ]
}
MosquitoRelease

The MosquitoRelease intervention class adds mosquito release vector control programs to the simulation. Mosquito release is a key vector control mechanism that allows the release of sterile males, genetically modified mosquitoes, or even Wolbachia-infected mosquitoes. See Vector control configuration parameters for more information.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Cost_To_Consumer

float

0

999999

0

The cost of each mosquito release.

 
{
    "Cost_To_Consumer": 1.5
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of NodeProperty* key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to nodes with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Intervention_Name

string

NA

NA

MosquitoRelease

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "MosquitoRelease",
        "Intervention_Name": "Sterile male mosquito release"
    }
}

New_Property_Value

string

NA

NA

NA

An optional NodeProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Released_Fraction

float

0

1

0

The fraction of the current population of mosquitoes to relEase (the population from the previous time step) The population depends on the gender of the mosquitoes being released. This depends on Released_Type being set to FRACTION.

 
{
    "Released_type": "FRACTION",
    "Released_Fraction": 0.4
}

Released_Genome

array of strings

NA

NA

[]

This defines the alleles of the genome of the vectors to be released. It must define all of the alleles including the gender gene. ‘*’ is not allowed. See Vector_Species_Params for more information.

 
{
    "Released_Genome" : [
                    ["X", "X"], ["b0", "b0"]
                ]
}

Released_Infectious

float

0

1

0

The fraction of vectors released that are to be infectious. You can only use this feature when Malaria_Model is NOT set to MALARIA_MECHANISTIC_MODEL_WITH_PARASITE_GENETICS.

 
{
    "Released_Infectious": 0.6
}

Released_Number

integer

1

100000000.0

10000

The number of mosquitoes released in the intervention.

 
{
    "Released_Number": 5000
}

Released_Species

string

NA

NA

NA

The name of the released mosquito species, such as arabiensis. The simulation configuration parameter, Vector_Species_Params, needs to contain that specific mosquito species.

 
{
    "Released_Species": "gambiae"
}

Released_Type

enum

NA

NA

FIXED_NUMBER

Specifies the number of mosquitoes to release. Possible values are FRACTION and FIXED_NUMBER. When set to FRACTION, this specifies the fraction of the current population to release. When set to FIXED_NUMBER, specifies an exact count of mosquitoes to release.

 
{
    "Released_Type": "FIXED_NUMBER"
}

Released_Wolbachia

enum

NA

NA

WOLBACHIA_FREE

The Wolbachia type of released mosquitoes. Possible values are:

  • WOLBACHIA_FREE

  • VECTOR_WOLBACHIA_A

  • VECTOR_WOLBACHIA_B

  • VECTOR_WOLBACHIA_AB

 
{
    "Released_Wolbachia": "VECTOR_WOLBACHIA_A"
}

{
    "Events": [
        {
            "Event_Coordinator_Config": {
                "Intervention_Config": {
                    "class": "MosquitoRelease",
                    "Cost_To_Consumer": 200,
                    "Released_Type": "FRACTION",
                    "Released_Fraction": 0.5,
                    "Released_Infectious": 0.5,
                    "Released_Species": "SillySkeeter",
                    "Released_Wolbachia": "VECTOR_WOLBACHIA_FREE",
                    "Released_Genome" : [
                        ["X", "Y" ], ["a1", "a1"]
                    ]
                },
                "class": "StandardInterventionDistributionEventCoordinator"
            },
            "Event_Name": "MosquitoRelease",
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 5,
            "class": "CampaignEvent"
        },
        {
            "Event_Coordinator_Config": {
                "Intervention_Config": {
                    "class": "MosquitoRelease",
                    "Cost_To_Consumer": 200,
                    "Released_Type": "FIXED_NUMBER",
                    "Released_Number": 35000,
                    "Released_Species": "SillySkeeter",
                    "Released_Wolbachia": "VECTOR_WOLBACHIA_A",
                    "Released_Genome" : [
                        ["X", "X"], ["b0", "b0"]
                    ]
                },
                "class": "StandardInterventionDistributionEventCoordinator"
            },
            "Event_Name": "MosquitoRelease",
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 125,
            "class": "CampaignEvent"
        }
    ],
    "Use_Defaults": 1
}
MultiInsecticideIndoorSpaceSpraying

The MultiInsecticideIndoorSpaceSpraying intervention class is a node-level intervention that uses Indoor Residual Spraying (IRS) with multiple insecticides. It builds on the IndoorSpaceSpraying class by allowing for multiple insecticides, each with their own specified killing efficacy.

The effectiveness of the intervention is combined using the following equation:

Total efficacy = 1.0 – (1.0 – efficacy_1) * (1.0 – efficacy_2) * … * (1.0 – efficacy_n)

At a glance:

  • Distributed to: Nodes

  • Serialized: No, it needs to be redistributed when starting from a serialized file.

  • Uses insecticides: Yes. It can target subgroups using genomes, especially when targeting certain species.

  • Time-based expiration: No

  • Purge existing: Yes. If a new intervention is added to to the node, the existing intervention of the same name is removed when the new one is added.

  • Vector killing contributes to: Indoor Die After Feeding

  • Vector effects: Killing

  • Vector sexes affected: Females only

  • Vector life stage affected: Adult

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Cost_To_Consumer

float

0

999999

10

The unit cost per trap (unamortized).

 
{
    "Cost_To_Consumer": 4.5
}

Insecticide_Name

string

NA

NA

UNINITIALIZED STRING

The name of the insecticide defined in the configuration parameter Insecticides for this intervention. If insecticides are being used, this must be defined as one of those values; if they are not being used this can be empty. It cannot have a value if you did not configure the configuration parameter Insecticides.

 
{
    "Insecticide_Name": "carbamate"
}

Insecticides

array of JSON objects

NA

NA

[]

An array of objects defining the blocking and killing efficacy of each insecticide.

 
{
    "Insecticides" : [
        {
            "Insecticide_Name": "pyrethroid",
            "Repelling_Config": {
                "Box_Duration": 300,
                "Initial_Effect": 0.25,
                "class": "WaningEffectBox"
            },
            "Killing_Config": {
                "Box_Duration": 300,
                "Initial_Effect": 1.0,
                "class": "WaningEffectBox"
            }
        },
        {
            "Insecticide_Name": "carbamate",
            "Repelling_Config": {
                "Box_Duration": 300,
                "Initial_Effect": 0.25,
                "class": "WaningEffectBox"
            },
            "Killing_Config": {
                "Box_Duration": 300,
                "Initial_Effect": 1.0,
                "class": "WaningEffectBox"
             }
        }
    ]
}

Killing_Config

JSON object

NA

NA

NA

The configuration of killing efficacy and waning for human host-seeking trap. Only vectors attracted to the trap by the Attract_Config settings are targeted by the Killing_Config. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Killing_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0.9,
        "class": "WaningEffectBox"
    }
}

Spray_Coverage

float

0

1

0

The proportion of the node that has been sprayed. This value is multiplied by the current efficacy of the WaningEffect.

 
{
    "Cost_To_Consumer": 4.5
}

{
    "class": "MultiInsecticideIndoorSpaceSpraying",
    "Cost_To_Consumer": 1.0,
    "Spray_Coverage": 1.0,
    "Insecticides": [{
            "Insecticide_Name": "pyrethroid_homo",
            "Killing_Config": {
                "Box_Duration": 100,
                "Decay_Time_Constant": 150,
                "Initial_Effect": 0.95,
                "class": "WaningEffectBoxExponential"
            }
        },
        {
            "Insecticide_Name": "carbamate_homo",
            "Killing_Config": {
                "Box_Duration": 100,
                "Decay_Time_Constant": 150,
                "Initial_Effect": 0.95,
                "class": "WaningEffectBoxExponential"
            }
        }
    ]
}
MultiInsecticideSpaceSpraying

The MultiInsecticideSpaceSpraying intervention class is a node-level intervention that models the application of a multi-insecticide outdoor spray. As a spray, this kills male and female adult and immature mosquitoes. Mosquitoes have a daily probability of dying; feeding status does not impact the probability of death for adult female mosquitoes.

The effectiveness of the intervention is combined using the following equation:

Total efficacy = 1.0 – (1.0 – efficacy_1)* (1.0 – efficacy_2) * … * (1.0 – efficacy_n)

At a glance:

  • Distributed to: Nodes

  • Serialized: No, it needs to be redistributed when starting from a serialized file.

  • Uses insecticides: Yes. It can target subgroups using genomes, especially when targeting certain species.

  • Time-based expiration: No

  • Purge existing: Yes. If a new intervention is added to to the node, the existing intervention of the same name is removed when the new one is added.

  • Vector killing contributes to: Die Without Attempting to Feed & Die Before Attempting Human Feed

  • Vector effects: Killing

  • Vector sexes affected: Both males and females

  • Vector life stage affected: Adult and immature

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Cost_To_Consumer

float

0

999999

10

The unit cost per vector control (unamortized).

 
{
    "Cost_To_Consumer": 8
}

Insecticide_Name

string

NA

NA

Uninitialized string

The name of the insecticide defined in <config.Insecticides> for this intervention. If insecticides are being used, this must be defined as one of those values; if they are not being used this can be empty. It cannot have a value if you did not configure <config.Insecticides>.

 
{
    "Insecticide_Name": "carbamate"
}

Insecticides

array of JSON objects

NA

NA

[]

An array of objects defining the blocking and killing efficacy of each insecticide.

 
{
    "Insecticides" : [
        {
            "Insecticide_Name": "pyrethroid",
            "Repelling_Config": {
                "Box_Duration": 300,
                "Initial_Effect": 0.25,
                "class": "WaningEffectBox"
            },
            "Killing_Config": {
                "Box_Duration": 300,
                "Initial_Effect": 1.0,
                "class": "WaningEffectBox"
            }
        },
        {
            "Insecticide_Name": "carbamate",
            "Repelling_Config": {
                "Box_Duration": 300,
                "Initial_Effect": 0.25,
                "class": "WaningEffectBox"
            },
            "Killing_Config": {
                "Box_Duration": 300,
                "Initial_Effect": 1.0,
                "class": "WaningEffectBox"
             }
        }
    ]
}

Killing_Config

JSON object

NA

NA

NA

The configuration of killing efficacy and waning for space spaying. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Killing_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0.5,
        "class": "WaningEffectBox"
    }
}

Spray_Coverage

float

0

1

0

The portion of the node that has been sprayed. This value is multiplied by the current efficacy of the WaningEffect.

 
{
    "Spray_Coverage": 0.8
}

{
    "Events": [{
        "Event_Coordinator_Config": {
            "Intervention_Config": {
                "class": "MultiInsecticideSpaceSpraying",
                "Cost_To_Consumer": 1.0,
                "Spray_Coverage": 1.0,
                "Insecticides": [{
                        "Insecticide_Name": "pyrethroid_homo",
                        "Killing_Config": {
                            "Box_Duration": 100,
                            "Decay_Time_Constant": 150,
                            "Initial_Effect": 0.1,
                            "class": "WaningEffectBoxExponential"
                        }
                    },
                    {
                        "Insecticide_Name": "carbamate_homo",
                        "Killing_Config": {
                            "Box_Duration": 100,
                            "Decay_Time_Constant": 150,
                            "Initial_Effect": 0.1,
                            "class": "WaningEffectBoxExponential"
                        }
                    }
                ]
            },
            "class": "StandardInterventionDistributionEventCoordinator"
        },
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 100,
        "class": "CampaignEvent"
    }],
    "Use_Defaults": 1
}
MultiNodeInterventionDistributor

The MultiNodeInterventionDistributor intervention class is a node-level intervention that distributes multiple other node-level interventions when the distributor only allows specifying one intervention. This class can be thought of as an “adapter,” where it can adapt interventions or coordinators that were designed to distribute one intervention to instead distribute many.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Disqualifying_Properties

array of strings

NA

NA

[]

A list of NodeProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Intervention_Name

string

NA

NA

MultiNodeInterventionDistributor

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "MultiNodeInterventionDistributor",
        "Intervention_Name": "Country-wide vaccination"
    }
}

New_Property_Value

string

NA

NA

NA

An optional NodeProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Node_Intervention_List

array of JSON objects

NA

NA

NA

A list of nested JSON objects for the multi-node-level interventions to be distributed by this intervention.

 
{
    "Intervention_Config": {
        "class": "MultiNodeInterventionDistributor",
        "Node_Intervention_List": [
            {
                "class": "SpaceSpraying",
                "Cost_To_Consumer": 1.0,
                "Habitat_Target": "ALL_HABITATS",
                "Spray_Kill_Target": "SpaceSpray_Indoor",
                "Killing_Config": {
                    "class": "WaningEffectExponential",
                    "Initial_Effect": 1.0,
                    "Decay_Time_Constant": 90
                },
                "Reduction_Config": {
                    "class": "WaningEffectExponential",
                    "Initial_Effect": 1.0,
                    "Decay_Time_Constant": 90
                }
            },
            {
                "class": "NodePropertyValueChanger",
                "Target_NP_Key_Value": "InterventionStatus:RECENT_SPRAY",
                "Daily_Probability": 1.0,
                "Maximum_Duration": 0,
                "Revert": 90
            }
        ]
    }
}

{
    "Intervention_Config": {
        "class": "MultiNodeInterventionDistributor",
        "Node_Intervention_List": [
            {
                "class": "SpaceSpraying",
                "Cost_To_Consumer": 1.0, 
                "Habitat_Target": "ALL_HABITATS", 
                "Spray_Kill_Target": "SpaceSpray_Indoor",
                "Killing_Config": {
                    "class": "WaningEffectExponential",
                    "Initial_Effect": 1.0,
                    "Decay_Time_Constant": 90
                        }, 
                "Reduction_Config": {
                    "class": "WaningEffectExponential",
                    "Initial_Effect": 1.0,
                    "Decay_Time_Constant": 90
                        }
            }, 
            {
                "class": "NodePropertyValueChanger",
                "Target_NP_Key_Value": "InterventionStatus:RECENT_SPRAY", 
                "Daily_Probability": 1.0, 
                "Maximum_Duration": 0, 
                "Revert": 90
            }
        ]
    }
}
NLHTIVNode

The NLHTIVNode intervention class distributes node-level interventions to nodes when a specific user-defined node event occurs. For example, NLHTIVNode can be configured to have SurveillanceEventCoordinator set to listen for NewInfectionEvents, and then broadcast a node event when a certain number of events is reached, such as distributing IndoorSpaceSpraying to a node with a high number of new infections.

NLHTIVNode is similar to NodeLevelHealthTriggeredIV but NLHTIVNode is focused on node interventions and events while NodeLevelHealthTriggeredIV is focused on individual interventions and events.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Actual_NodeInterventionConfig

JSON object

NA

NA

NA

The configuration of the actual node-level intervention sought. This parameter selects a class for the intervention and configures the parameters specific for that intervention class.

 
{
    "Actual_NodeInterventionConfig": {
        "class": "SpaceSpraying"
        }
}

Blackout_Event_Trigger

string

NA

NA

“

The node event to broadcast if an intervention cannot be distributed due to the Blackout_Period. Custom events can be added; currently the only built-in value is SheddingComplete.

 
{
    "Blackout_Event_Trigger": "SheddingComplete"
}

Blackout_On_First_Occurence

boolean

NA

NA

0

If set to true (1), individuals enter the blackout period after the first occurence of a node event in the Trigger_Condition_List.

 
{
    "Blackout_On_First_Occurence": 0
}

Blackout_Period

float

0

3.40E+38

0

The time in days after the initial intervention distribution to wait before distributing the intervention again. If the intervention is not distributable due to the blackout period, it broadcasts the user-defined Blackout_Event_Trigger.

 
{
    "Blackout_Period": 100.0
}

Duration

float

-1

3.40E+38

-1

The number of days to continue this intervention. A value of -1 keeps the intervention running indefinitely.

 
{
    "Duration": 2723
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Event_Coordinator_Config": {
        "Demographic_Coverage": 1,
        "Intervention_Config": {
            "Base_Sensitivity": 1,
            "Base_Specificity": 1,
            "Cost_To_Consumer": 0,
            "Days_To_Diagnosis": 0,
            "Disqualifying_Properties": [
                "InterventionStatus:LostForever"
            ],
            "Negative_Diagnosis_Event": "TestedNegative_IamSusceptible",
            "Positive_Diagnosis_Event": "TestedPositive_IamImmune",
            "Positive_Threshold_AcquisitionImmunity": 0.99,
            "Treatment_Fraction": 1,
            "class": "ImmunityBloodTest"
        }
    }
}

Intervention_Name

string

NA

NA

NLHTIVNode

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Event_Coordinator_Config": {
        "Demographic_Coverage": 1,
        "Intervention_Config": {
            "Base_Sensitivity": 1,
            "Base_Specificity": 1,
            "Cost_To_Consumer": 0,
            "Days_To_Diagnosis": 0,
            "Event_Or_Config": "Event",
            "Intervention_Name": "Immunity Blood Test - Series 1",
            "Negative_Diagnosis_Event": "TestedNegative_IamSusceptible",
            "Positive_Diagnosis_Event": "TestedPositive_IamImmune",
            "Positive_Threshold_AcquisitionImmunity": 0.99,
            "Treatment_Fraction": 1,
            "class": "NLHTIVNode"
        }
    }
}

New_Property_Value

string

NA

NA

NA

An optional IndividualProperty key:value pair that will be assigned when the intervention is first updated. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Node_Property_Restrictions

JSON object

NA

NA

NA

A list of the NodeProperty key:value pairs, as defined in the demographics file, that are required for nodes to be targeted by the intevention.

 
{
    "Node_Property_Restrictions": [
    {
            "Place": "URBAN",
            "Risk": "MED"
        },
        {
            "Place": "RURAL",
            "Risk": "LOW"
        }
    ]
}

Trigger_Condition_List

array of strings

NA

NA

[ ]

A list of node events that tigger a health-seeking intervention. Currently the only built-in value is SheddingComplete.

 
{
    "Trigger_Condition_List": [
        "SheddingComplete"
    ]
}

{
    "Events": [{
        "comment": "No infections, Negative_Event_Node",
        "class": "CampaignEvent",
        "Start_Day": 1,
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Intervention_Config": {
                "class": "NLHTIVNode",
                "Trigger_Condition_List": ["SheddingComplete"],
                "Duration": 1000,
                "Blackout_Event_Trigger": "Blackout",
                "Blackout_Period": 100.0,
                "Blackout_On_First_Occurrence": 0,
                "Actual_NodeIntervention_Config": {
                    "class": "EnvironmentalDiagnostic",
                    "Sample_Threshold": 0.0,
                    "Environment_IP_Key_Value": "Risk:High",
                    "Base_Specificity": 1.0,
                    "Base_Sensitivity": 1.0,
                    "Negative_Diagnostic_Event": "Negative_Event_Node",
                    "Positive_Diagnostic_Event": "Positive_Event_Node"
                }
            }
        }
    }]
}
NodeLevelHealthTriggeredIV

The NodeLevelHealthTriggeredIV intervention class is a node-level intervention that distributes an intervention to individuals when a specific event occurs to those individuals. NodeLevelHealthTriggeredIV monitors for event triggers from individuals, and when found, will distribute the intervention. For example, NodeLevelHealthTriggeredIV can be configured such that all individuals will be given a diagnostic intervention when they transition from susceptible to infectious. During the simulation, when individuals become infected, they broadcast the NewInfectionEvent trigger and NodeLevelHealthTriggeredIV distributes the diagnostic intervention to them.

Notes and tips for this intervention:

  • This is the main tool for distributing an intervention to a person when an event happens to that person. Note that the intervention is distributed to all individuals in the node which experience the triggering event.

  • This is a node-level intervention and is not serialized. If interventions were distributed prior to serialization, in order to have those interventions continue after starting from the serialized file, they must be added to the new campaign file.

  • This can be used to distribute other node-level interventions. For example, it can be used to distribute SpaceSpraying to the node when someone becomes infected (e.g. by listening for for NewInfectionEvent or an event from a diagnostic).

  • A powerful feature of this intervention is that it can target specific groups of individuals who broadcast the event. Individuals, and subgroups of individuals, can be targeted by age, gender, and Individual Property.

  • Note that when distributing a node-level intervention parameters associated with targeting an individual (such as Target_Demographic, Target_Gender, Property_Restriction_Within_Node, etc.) do not apply.

  • Blackout_Period is a feature that can be useful when monitoring an event from the individual in a node. It enables reaction to some individuals experiencing an event but ignoring subsequent events for a period of time. For example, SpaceSpraying could be distributed to the node on the first occurrence of NewInfectionEvent, but after spraying has occurred all other infection events can be ignored for a specific period of time. Without Blackout_Period, each infection event would trigger another round of spraying.

  • The Distribute_On_Return_Home feature causes NodeLevelHealthTriggeredIV to keep track of residents when they leave the node and then return. If a person leaves the node and an intervention is distributed while the person is gone, NodeLevelHealthTriggeredIV gives the person the intervention (such as a vaccine dose) when they return to the node.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Actual_IndividualIntervention_Config

JSON object

NA

NA

NA

The configuration of an actual individual intervention to be distributed on the trigger. Selects a class for the intervention and configures the parameters specific for that intervention class.

 
{
    "Actual_IndividualIntervention_Config": {
        "class": "OutbreakIndividual",
        "Antigen": 0,
        "Genome": 0,
        "Outbreak_Source": "PrevalenceIncrease"
    }
}

Actual_NodeIntervention_Config

JSON object

NA

NA

NA

The configuration of the actual node-level intervention sought. This parameter selects a class for the intervention and configures the parameters specific for that intervention class.

 
{
    "Actual_NodeIntervention_Config": {
        "class": "OutbreakIndividual",
        "Antigen": 0,
        "Genome": 0,
        "Outbreak_Source": "PrevalenceIncrease"
    }
}

Blackout_Event_Trigger

enum

NA

NA

NA

The event to broadcast if an intervention cannot be distributed due to the Blackout_Period. See Event list for possible values.

 
{
    "Blackout_Event_Trigger": "Blackout"
}

Blackout_On_First_Occurrence

boolean

NA

NA

0

If set to true (1), individuals will enter the blackout period after the first occurrence of an event in the Trigger_Condition_List.

 
{
    "Blackout_On_First_Occurrence": 0
}

Blackout_Period

float

0

3.40E+38

0

After the initial intervention distribution, the time, in days, to wait before distributing the intervention again. If it cannot distribute due to the blackout period, it will broadcast the user-defined Blackout_Event_Trigger.

 
{
    "Blackout_Period": 0.0
}

Demographic_Coverage

float

0

1

1

The fraction of individuals in the target demographic that will receive this intervention.

 
{
    "Demographic_Coverage": 1
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of NodeProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to nodes with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Distribute_On_Return_Home

boolean

NA

NA

0

When set to true (1), individuals will receive an intervention upon returning home if that intervention was originally distributed while the individual was away.

 
{
    "Distribute_On_Return_Home": 1
}

Duration

float

-1

3.40282e+38

-1

The number of days the NLHTIV will exist and listen for events/triggers.

Note

Specifying a value of -1 results in indefinite persistence of the intervention.

 
{
    "Duration": -1
}

Intervention_Name

string

NA

NA

NodeLevelHealthTriggeredIV

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "NodeLevelHealthTriggeredIV",
        "Intervention_Name": "Treat individuals when infectious"
    }
}

New_Property_Value

string

NA

NA

NA

An optional NodeProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Node_Property_Restrictions

array of JSON objects

NA

NA

NA

A list of the NodeProperty key:value pairs, as defined in the demographics file, that nodes must have to be targeted by the intervention. See NodeProperties and IndividualProperties parameters for more information.

You can specify AND and OR combinations of key:value pairs with this parameter.

The following example restricts the intervention to nodes that are urban and medium risk or rural and low risk.

{
    "Node_Property_Restrictions": [
        {
            "Place": "URBAN",
            "Risk": "MED"
        },
        {
            "Place": "RURAL",
            "Risk": "LOW"
        }
    ]
}

Property_Restrictions

array of JSON objects

NA

NA

NA

A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information.

To specify AND and OR combinations of key:value pairs, use Property_Restrictions_Within_Node. You cannot use both of these parameters in the same event coordinator.

 
{
    "Property_Restrictions": [
        "Risk:HIGH"
    ]
}

Property_Restrictions_Within_Node

array of JSON objects

NA

NA

NA

A list of the IndividualProperty key:value pairs, as defined in the demographics file, that individuals must have to be targeted by this intervention. See NodeProperties and IndividualProperties parameters for more information.

This parameter allows you to specify AND and OR combinations of key:value pairs. You may specify individual property restrictions using either this parameter or Property_Restrictions, but not both.

The following example restricts the intervention to individuals who are urban and high risk or urban and medium risk.

{
    "Property_Restrictions_Within_Node": [
        {
            "Risk": "HIGH",
            "Geographic": "URBAN"
        },
        {
            "Risk": "MEDIUM",
            "Geographic": "URBAN"
        }
    ]
}

Target_Age_Max

float

0

9.32E+35

9.32E+35

The upper end of ages targeted for an intervention, in years. Used when Target_Demographic is set to ExplicitAgeRanges or ExplicitAgeRangesAndGender.

 
{
    "Target_Age_Max": 20,
    "Target_Age_Min": 10,
    "Target_Demographic": "ExplicitAgeRanges"
}

Target_Age_Min

float

0

9.32E+35

0

The lower end of ages targeted for an intervention, in years. Used when Target_Demographic is set to ExplicitAgeRanges or ExplicitAgeRangesAndGender.

 
{
    "Target_Age_Max": 20,
    "Target_Age_Min": 10,
    "Target_Demographic": "ExplicitAgeRanges"
}

Target_Demographic

enum

NA

NA

Everyone

The target demographic group. Possible values are:

  • Everyone

  • ExplicitAgeRanges

  • ExplicitAgeRangesAndGender

  • ExplicitGender

  • ExplicitDiseaseState

  • PossibleMothers

 
{
    "Target_Age_Max": 20,
    "Target_Age_Min": 10,
    "Target_Demographic": "ExplicitAgeRanges"
}

Target_Gender

enum

NA

NA

All

Specifies the gender restriction for the intervention. Possible values are:

  • Male

  • Female

  • All

 
{
    "Target_Gender": "Male"
}

Target_Residents_Only

boolean

NA

NA

0

When set to true (1), the intervention is only distributed to individuals that began the simulation in the node (i.e. those that claim the node as their residence).

 
{
    "Target_Residents_Only": 1
}

Trigger_Condition_List

array of strings

NA

NA

NA

An array of events that will trigger an intervention. The events contained in the list can be built-in events (see Event list for possible events) or the custom events defined in Custom_Individual_Events in the simulation configuration file.

 
{
    "Trigger_Condition_List": [
        "OnART3"
    ]
}

Targeting_Config

JSON object

NA

NA

NA

Be more selective of individuals by using the Targeting_Config classes.

 
{
    "Targeting_Config": {
        "class": "HasIntervention",
        "Is_Equal_To": 0,
        "Intervention_Name": "MyVaccine"
    }
}

{
    "Use_Defaults": 1,
    "Events": [{
        "class": "CampaignEvent",
        "Start_Day": 1,
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Intervention_Config": {
                "class": "NodeLevelHealthTriggeredIV",
                "Trigger_Condition_List": ["HappyBirthday"],
                "Demographic_Coverage": 1.0,
                "Target_Age_Max": 99,
                "Target_Age_Min": 21,
                "Target_Demographic": "ExplicitAgeRanges",
                "Target_Residents_Only": 1,
                "Actual_IndividualIntervention_Config": {
                    "class": "OutbreakIndividual",
                    "Antigen": 0,
                    "Genome": 0,
                    "Outbreak_Source": "PrevalenceIncrease"
                }
            }
        }
    }]
}
NodePropertyValueChanger

The NodePropertyValueChanger intervention class sets a given node property to a new value. You can also define a duration in days before the node property reverts back to its original value, the probability that a node will change its node property to the target value, and the number of days over which nodes will attempt to change their individual properties to the target value. This node-level intervention functions in a similar manner as the individual-level intervention, PropertyValueChanger.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Daily_Probability

float

0

1

1

The daily probability that the node’s property value changes to the Target_NP_Key_Value.

 
{
    "Intervention_Config": {
        "class": "PropertyValueChanger",
        "Disqualifying_Properties": [],
        "New_Property_Value": "",
        "Target_Property_Key": "Risk",
        "Target_Property_Value": "LOW",
        "Daily_Probability": 1.0,
        "Maximum_Duration": 0,
        "Revert": 0
    }
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of NodeProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to nodes with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Intervention_Name

string

NA

NA

NodePropertyValueChanger

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Name": "Diagnostic_Sample"
}

Maximum_Duration

float

-1

3.40E+38

3.40E+38

The maximum amount of time in days nodes have to update the property value. This timing works in conjunction with Daily_Probability.

 
{
    "Intervention_Config": {
        "class": "PropertyValueChanger",
        "Disqualifying_Properties": [],
        "New_Property_Value": "",
        "Target_Property_Key": "Risk",
        "Target_Property_Value": "LOW",
        "Daily_Probability": 1.0,
        "Maximum_Duration": 0,
        "Revert": 0
    }
}

New_Property_Value

string

NA

NA

NA

An optional NodeProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Revert

float

0

10000

0

The number of days before the node’s property value reverts back to the initial node property value.

 
{
    "Intervention_Config": {
        "class": "PropertyValueChanger",
        "Disqualifying_Properties": [],
        "New_Property_Value": "",
        "Target_Property_Key": "Risk",
        "Target_Property_Value": "LOW",
        "Daily_Probability": 1.0,
        "Maximum_Duration": 0,
        "Revert": 0
    }
}

Target_NP_Key_Value

string

NA

NA

NA

The NodeProperty key:value pair, as defined in the demographics file, to assign to the node.

 
{
    "Target_NP_Key_Value": "InterventionStatus:NONE"
}

{
    "Use_Defaults": 1,
    "Events": [{
        "class": "CampaignEvent",
        "Start_Day": 40,
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Node_Property_Restrictions": [{
                "InterventionStatus": "VACCINATING"
            }],
            "Intervention_Config": {
                "class": "NodePropertyValueChanger",
                "Target_NP_Key_Value": "InterventionStatus:STOP_VACCINATING",
                "Daily_Probability": 1.0,
                "Maximum_Duration": 0,
                "Revert": 0
            }
        }
    }]
}
Outbreak

The Outbreak class allows the introduction of a disease outbreak event by the addition of new infected or susceptible individuals to a node. Outbreak is a node-level intervention; to distribute an outbreak to specific categories of existing individuals within a node, use OutbreakIndividual.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Clade

integer

0

10

0

The clade ID of the outbreak infection. Together with the genome ID (Genome) they are a unitary object representing a strain of infection, which allows for differentiation among infections. Values for Clade may range from 0 to Number_of_Clades-1 (set in the configuration file).

 
{
    "Clade": 1
}

Genome

integer

-1

1.67E+07

0

The genome ID of the outbreak infection. Together with the clade ID (Clade) they represent an infection as a unitary object. Values for Genome may range from -1 to 2Log2_Number_of_Genomes_per_Clade-1. Set this to -1 to create a random number generator.

Intervention distribution example:

{
    "Enable_Strain_Tracking": 1,
    "Events": [
        {
            "Event_Coordinator_Config": {
                "Demographic_Coverage": 0.001,
                "Intervention_Config": {
                    "Clade": 1,
                    "Genome": 3,
                    "IgnoreImmunity": 1,
                    "class": "Outbreak"
                },
                "Target_Demographic": "Everyone",
                "class": "StandardInterventionDistributionEventCoordinator"
            },
            "Event_Name": "Outbreak",
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 30,
            "class": "CampaignEvent"
        }
    ]
}

Import_Age

float

0

43800

365

The age (in days) of imported individuals.

 
{
    "Import_Age": 10000
}

Number_Cases_Per_Node

integer

0

2.14E+0

1

The number of new imported individuals.

Note

This will increase the population with demographics of 50/50 male/female and user-defined ages.

 
{
    "Intervention_Config": {
        "Clade": 0,
        "Genome": 0,
        "Outbreak_Source": "ImportCases",
        "Number_Cases_Per_Node": 10,
        "class": "Outbreak"
    }
}

Probability_Of_Infection

float

0

1

1

The probability that new individuals are infected. 1.0 implies all new individuals are infected while 0.0 adds all of the people as susceptible individuals.

 
{
    "Probability_Of_Infection": 0.5
}

{
  "Events": [
    {
      "Event_Coordinator_Config": {
        "Demographic_Coverage": 0.001,
        "Intervention_Config": {
          "Clade": 1,
          "Genome": 3,
          "Import_Age": 365,
          "Number_Cases_Per_Node": 10,
          "Probability_Of_Infection": 0.7,
          "class": "Outbreak"
        },
        "Target_Demographic": "Everyone",
        "class": "StandardInterventionDistributionEventCoordinator"
      },
      "Event_Name": "Outbreak",
      "Nodeset_Config": {
        "class": "NodeSetAll"
      },
      "Start_Day": 30,
      "class": "CampaignEvent"
    }
  ]
}
OutdoorRestKill

The OutdoorRestKill intervention class imposes node-targeted mortality to a vector that is at rest in an outdoor environment after an outdoor feed on a human.

At a glance:

  • Distributed to: Nodes

  • Serialized: No. It will need to be redistributed when starting from a serialized file.

  • Uses insecticides: Yes. It can target sub-groups using genomes or specific sexes.

  • Time-based expiration: No. It will continue to exist even if efficacy is zero.

  • Purge existing: Yes. If a new intervention is added to to the node, the existing intervention of the same name is removed when the new one is added.

  • Vector killing contributes to: Outdoor Die After Feeding. Note that it is a node-level intervention but does not impact the other node-level probabilities.

  • Vector effects: Killing

  • Vector sexes affected: Males and females

  • Vector life stage affected: Adults

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Cost_To_Consumer

float

0

999999

10

The unit cost per vector control (unamortized).

 
{
    "Cost_To_Consumer": 8
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of NodeProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to nodes with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Insecticide_Name

string

NA

NA

UNINITIALIZED STRING

The name of the insecticide defined in the configuration parameter Insecticides for this intervention. If insecticides are being used, this must be defined as one of those values; if they are not being used this can be empty. It cannot have a value if you did not configure the configuration parameter Insecticides.

 
{
    "Insecticide_Name": "carbamate"
}

Intervention_Name

string

NA

NA

OutdoorRestKill

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "OutdoorRestKill",
        "Intervention_Name": "Insecticide post-feeding"
    }
}

Killing_Config

JSON object

NA

NA

NA

The configuration for the effects of killing of the targeted stage. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Killing_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0.5,
        "class": "WaningEffectBox"
    }
}

New_Property_Value

string

NA

NA

NA

An optional NodeProperty key:value pair that will be assigned when the intervention is first updated. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

{
    "Events": [{
        "class": "CampaignEvent",
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 120,
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Intervention_Config": {
                "class": "OutdoorRestKill",
                "Cost_To_Consumer": 1.0,
                "Insecticide_Name": "carbamate",
                "Killing_Config": {
                    "Box_Duration": 100,
                    "Decay_Time_Constant": 150,
                    "Initial_Effect": 0.75,
                    "class": "WaningEffectBoxExponential"
                }
            }
        }
    }],
    "Use_Defaults": 1
}
OvipositionTrap

The OvipositionTrap intervention class utilizes an oviposition trap to collect host-seeking mosquitoes, and is based upon imposing a mortality to egg hatching from oviposition. This is a node-targeted intervention and affects all mosquitoes living and feeding at a given node. This trap requires the use of individual mosquitoes in the simulation configuration file (Vector_Sampling_Type must be set to TRACK_ALL_VECTORS or SAMPLE_IND_VECTORS), rather than the cohort model. See Sampling configuration parameters for more information.

Notes and tips for this intervention:

  • It calculates a habitat-weighted average based on the current capacity of the habitat. It then uses this average to determine if the vector dies while laying eggs.

  • A vector only lays eggs on the day she feeds.

  • In the individual model, each vector has its own timer indicating when it should feed. If the number of days between feeds is configured to be temperature dependent (using the configuration parameter Temperate_Dependent_Feeding_Cycle), this duration can be different for each feed.

At a glance:

  • Distributed to: Nodes

  • Serialized: No, it needs to be redistributed when starting from a serialized file.

  • Uses insecticides: No

  • Time-based expiration: No. It will continue to exist even if the efficacy is zero.

  • Purge existing: Yes. If a new intervention is added to to the node, the existing intervention of the same name is removed when the new one is added.

  • Vector killing contributes to: Die Laying Eggs

  • Vector effects: Killing

  • Vector sexes affected: Females only

  • Vector life stage affected: Adult

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Cost_To_Consumer

float

0

999999

10

The unit cost per vector control (unamortized).

 
{
    "Cost_To_Consumer": 8
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of NodeProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to nodes with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Habitat_Target

enum

NA

NA

NONE

The oviposition habitat type targeted by oviposition traps. This is a required parameter. See Larval habitat parameters for more information. Possible values are:

  • NONE

  • TEMPORARY_RAINFALL

  • WATER_VEGETATION

  • HUMAN_POPULATION

  • CONSTANT

  • BRACKISH_SWAMP

  • LINEAR_SPLINE

  • ALL_HABITATS

 
{
    "Habitat_Target": "WATER_VEGETATION"
}

Intervention_Name

string

NA

NA

OvipositionTrap

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "OvipositionTrap",
        "Intervention_Name": "Cedrol traps"
    }
}

Killing_Config

JSON object

NA

NA

NA

The configuration of the killing effects for the fraction of oviposition cycles that end in the female mosquito’s death. If there is skip oviposition, this does not configure the mortality per skip, but instead configures the effective net mortality per gonotrophic cycle over all skips. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Killing_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0.53429,
        "class": "WaningEffectBox"
    }
}

New_Property_Value

string

NA

NA

NA

An optional NodeProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

{
    "Events": [{
        "class": "CampaignEvent",
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 140,
        "Event_Coordinator_Config": {
            "Target_Demographic": "Everyone",
            "class": "StandardInterventionDistributionEventCoordinator",
            "Intervention_Config": {
                "class": "OvipositionTrap",
                "Cost_To_Consumer": 3.75,
                "Habitat_Target": "WATER_VEGETATION",
                "Killing_Config": {
                    "class": "WaningEffectExponential",
                    "Decay_Time_Constant": 2190,
                    "Initial_Effect": 0.95
                },
                "Reduction": 0
            }
        }
    }],
    "Use_Defaults": 1
}
ScaleLarvalHabitat

The ScaleLarvalHabitat intervention class is a node-level intervention that enables species-specific habitat modification within shared habitat types. This intervention has a similar function to the demographic parameter ScaleLarvalMultiplier, but enables habitat availability to be modified at any time or at any location during the simulation, as specified in the campaign event.

To reset the multiplier, you must either replace the existing one with a new intervention where the multiplier/factor is 1.0 or use the Disqualifying_Properties to cause the intervention to abort.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Disqualifying_Properties

array of strings

NA

NA

[]

A list of NodeProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to nodes with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Factor

float

0

3.40E+38

1

The multiplier by which to scale the larval habitat availability.

 
{
    "LarvalHabitatMultiplier": [
        {
            "Factor": 0.01,
            "Habitat": "TEMPORARY_RAINFALL",
            "Species": "gambiae"
        }
    ]
}

Habitat

enum

NA

NA

NONE

The name of the habitat for which to specify a larval habitat multiplier. See Larval habitat parameters for more information. Possible values are:

  • NONE (default but not allowed; it must be configured to a habitat type)

  • TEMPORARY_RAINFALL

  • WATER_VEGETATION

  • MARSHY_STREAM

  • HUMAN_POPULATION

  • CONSTANT

  • BRACKISH_SWAMP

  • LINEAR_SPLINE

  • ALL_HABITATS

 
{
    "Habitat": "BRACKISH_SWAMP"
}

Intervention_Name

string

NA

NA

ScaleLarvalHabitat

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "ScaleLarvalHabitat",
        "Intervention_Name": "Reduce habitat for Aedes aegypti"
    }
}

Larval_Habitat_Multiplier

array of JSON objects

NA

NA

NA

The value by which to scale the larval habitat availability (specified in Habitats in the configuration file) per intervention, across all habitat types, for specific habitat types, or for specific mosquito species within each habitat type. See Larval habitat parameters for more information.

The following example scales the larval habitat equally across all habitat types and mosquito species.

{
    "Larval_Habitat_Multiplier": {
        "LarvalHabitatMultiplier": [
            {
                "Factor": 0.02,
                "Habitat": "ALL_HABITATS",
                "Species": "ALL_SPECIES"
            }
        ]
    }
}

The following example scales the larval habitat only in the temporary rainfall habitat for all mosquito species.

{
    "Larval_Habitat_Multiplier": {
        "LarvalHabitatMultiplier": [
            {
                "Factor": 0.02,
                "Habitat": "TEMPORARY_RAINFALL",
                "Species": "ALL_SPECIES"
            }
        ]
    }
}

The following example scales the larval habitat independently for A. gambiae in the temporary rainfall habitat and for A. arabiensis in the brackish swamp habitat.

{
    "Larval_Habitat_Multiplier": {
        "LarvalHabitatMultiplier": [
            {
                "Factor": 0.01,
                "Habitat": "TEMPORARY_RAINFALL",
                "Species": "gambiae"
            },
            {
                "Factor": 0.02,
                "Habitat": "BRACKISH_SWAMP",
                "Species": "arabiensis"
            }
        ]
    }
}

New_Property_Value

string

NA

NA

NA

An optional NodeProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Species

string

NA

NA

ALL_SPECIES

The name of the species for which to specify a larval habitat multiplier. This must be one of the names specified in the configuration parameter Vector_Species_Params or set to ALL_SPECIES.

 
{
    "Species": "arabiensis"
}

{
  "Use_Defaults": 1,
  "Events": [
    {
      "class": "CampaignEvent",
      "Nodeset_Config": {
        "class": "NodeSetAll"
      },
      "Start_Day": 730,
      "Event_Coordinator_Config": {
        "Intervention_Config": {
          "Larval_Habitat_Multiplier": {
            "LarvalHabitatMultiplier": [
              {
                "Factor": 0.05,
                "Habitat": "CONSTANT",
                "Species": "Gambiae"
              },
              {
                "Factor": 0.05,
                "Habitat": "TEMPORARY_RAINFALL",
                "Species": "ALL_SPECIES"
              }
            ]
          },
          "class": "ScaleLarvalHabitat"
        },
        "class": "StandardInterventionDistributionEventCoordinator"
      }
    }
  ]
}
SpaceSpraying

The SpaceSpraying intervention class implements node-level vector control by spraying pesticides outdoors. This intervention targets specific habitat types, and imposes a mortality rate on all targeted (both immature and adult male and female) mosquitoes. All mosquitoes have daily mortality rates; mortality for adult females due to spraying is independent of whether or not they are feeding.

At a glance:

  • Distributed to: Nodes

  • Serialized: No. You need to redistribute when starting from a serialized file.

  • Uses insecticides: Yes. It can target sub-groups using genomes, especially if you want to target specific sexes.

  • Time-based expiration: No

  • Purge existing: Yes. If a new intervention is added to to the node, the existing intervention of the same name is removed when the new one is added.

  • Vector killing contributes to: Die Without Attempting To Feed & Die Before Attempting Human Feed

  • Vector effects: Killing

  • Vector sexes affected: Males and females

  • Vector life stage affected: Adult and immature

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Cost_To_Consumer

float

0

999999

10

The unit cost per vector control (unamortized).

 
{
    "Cost_To_Consumer": 8
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of NodeProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to nodes with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Insecticide_Name

string

NA

NA

Uninitialized string

The name of the insecticide defined in <config.Insecticides> for this intervention. If insecticides are being used, this must be defined as one of those values; if they are not being used this can be empty. It cannot have a value if you did not configure <config.Insecticides>.

 
{
    "Insecticide_Name": "carbamate"
}

Intervention_Name

string

NA

NA

SpaceSpraying

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "SpaceSpraying",
        "Intervention_Name": "Outdoor DDT spraying"
    }
}

Killing_Config

JSON object

NA

NA

NA

The configuration of killing efficacy and waning for space spaying. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Killing_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0.5,
        "class": "WaningEffectBox"
    }
}

New_Property_Value

string

NA

NA

NA

An optional NodeProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Spray_Coverage

float

0

1

0

The portion of the node that has been sprayed. This value is multiplied by the current efficacy of the WaningEffect.

 
{
    "Spray_Coverage": 0.8
}

{
    "Use_Defaults": 1,
    "Campaign_Name": "SpaceSpraying",
    "Events": [{
        "class": "CampaignEvent",
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 4,
        "Event_Coordinator_Config": {
            "class": "NodeEventCoordinator",
            "Intervention_Config": {
                "class": "SpaceSpraying",
                "Cost_To_Consumer": 0,
                "Killing_Config": {
                    "class": "WaningEffectBoxExponential",
                    "Box_Duration": 100,
                    "Decay_Time_Constant": 150,
                    "Initial_Effect": 1
                },
                "Insecticide_Name": "carbamate",
                "Spray_Coverage": 0.9
            }
        }
    }]
}
SpatialRepellent

The SpatialRepellent intervention class implements node-level spatial repellents exclusively against outdoor-biting mosquitoes.

At a glance:

  • Distributed to: Nodes

  • Serialized: No, it needs to be redistributed when starting from a serialized file.

  • Uses insecticides: Yes. It can target subgroups using genomes.

  • Time-based expiration: No

  • Purge existing: Yes. If a new intervention is added to to the node, the existing intervention of the same name is removed when the new one is added.

  • Vector killing contributes to: No killing but Survive Without Successful Feed

  • Vector effects: Repelling

  • Vector sexes affected: Females only

  • Vector life stage affected: Adult

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Cost_To_Consumer

float

0

999999

10

The unit cost per vector control (unamortized).

 
{
    "Cost_To_Consumer": 8
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Insecticide_Name

string

NA

NA

UNINITIALIZED STRING

The name of the insecticide defined in <config.Insecticides> for this intervention. If insecticides are being used, this must be defined as one of those values; if they are not being used this can be empty. It cannot have a value if you did not configure <config.Insecticides>.

 
{
    "Insecticide_Name": "carbamate"
}

Intervention_Name

string

NA

NA

SpatialRepellent

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "SpatialRepellent",
        "Intervention_Name": "Mosquito coil repellent"
    }
}

New_Property_Value

string

NA

NA

NA

An optional NodeProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Repelling_Config

JSON object

NA

NA

NA

Defines the configuration of efficacy and waning for spatial repellent. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Repelling_Config": {
        "Box_Duration": 100,
        "Decay_Time_Constant": 150,
        "Initial_Effect": 0.4,
        "class": "WaningEffectBoxExponential"
    }
}

Spray_Coverage

float

0

1

1

The portion of the node that has been sprayed. This value is multiplied by the current efficacy of the WaningEffect.

 
{
    "Spray_Coverage": 0.6
}

{
    "Events": [{
        "class": "CampaignEvent",
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 120,
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Intervention_Config": {
                "class": "SpatialRepellent",
                "Repelling_Config": {
                    "Box_Duration": 100,
                    "Decay_Time_Constant": 150,
                    "Initial_Effect": 0.4,
                    "class": "WaningEffectBoxExponential"
                },
                "Cost_To_Consumer": 1.0,
                "Spray_Coverage": 0.6
            }
        }
    }],
    "Use_Defaults": 1
}
SugarTrap

The SugarTrap intervention class implements a vector sugar-baited trap to collect/kill sugar-feeding mosquitoes and is sometimes called an attractive toxic sugar bait (ATSB). This intervention affects all mosquitoes living and feeding at a given node. Male vectors sugar-feed daily and female vectors sugar-feed once per blood meal (or upon emergence), so these traps can impact male survival on a daily basis. Efficacy can be modified using per-sex insecticide resistance.

The impact of sugar-baited traps will depend on the sugar-feeding behavior specified in the configuration file via Vector_Sugar_Feeding_Frequency, whether there is no sugar feeding, sugar feeding occurs once at emergence, sugar feeding occurs once per blood meal, or sugar feeding occurs every day. Note that if it is set to VECTOR_SUGAR_FEEDING_NONE, this intervention will have no effect. See Vector control configuration parameters for more information.

At a glance:

  • Distributed to: Nodes

  • Serialized: No. It will need to be redistributed when starting from a serialized file.

  • Uses insecticides: Yes. It can target sub-groups using genomes or specific sexes.

  • Time-based expiration: Yes. Expiration time can be specified using specific distributions.

  • Purge existing: Yes. If a new intervention is added to to the node, the existing intervention of the same name is removed when the new one is added.

  • Vector killing contributes to: Sugar Trap Killing

  • Vector effects: Killing

  • Vector sexes affected: Males and females

  • Vector life stage affected: Adult and immature when they are emerging (if configured)

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Cost_To_Consumer

float

0

999999

10

The unit cost per vector control (unamortized).

 
{
    "Cost_To_Consumer": 9.0
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of NodeProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to nodes with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Expiration_Constant

float

0

3.4E+38

6

Each instance will receive this constant/fixed value, in days, for expiration of the trap when Expiration_Distribution is set to CONSTANT_DISTRIBUTION.

 
{
    "Expiration_Distribution": "CONSTANT_DISTRIBUTION",
    "Expiration_Constant": 8
}

Expiration_Distribution

enum

NA

NA

NOT_INTIIALIZED

The distribution type to use for setting the expiration of the trap. Each assigned value is a random draw from the distribution. Possible values are:

NOT_INITIALIZED

No distribution set.

CONSTANT_DISTRIBUTION

Use the same value for each individual. Set Expiration_Constant.

UNIFORM_DISTRIBUTION

Use a uniform distribution with a given minimum and maximum. Set Expiration_Max and Expiration_Min.

GAUSSIAN_DISTRIBUTION

The distribution is Gaussian (or normal). Values are resampled to ensure >= 0. Set Expiration_Gaussian_Mean and Expiration_Gaussian_Std_Dev.

EXPONENTIAL_DISTRIBUTION

The distribution is exponential with a given mean. Set Expiration_Exponential.

WEIBULL_DISTRIBUTION

Use a Weibull distribution with a given shape and scale. Set Expiration_Kappa and Expiration_Lambda.

LOG_NORMAL_DISTRIBUTION

Use a log-normal distribution with a given mean and standard deviation of the natural log. Set Expiration_Log_Normal_Mu and Expiration_Log_Normal_Sigma.

POISSON_DISTRIBUTION

Use a Poisson distribution with a given mean. Set Expiration_Poisson_Mean.

DUAL_CONSTANT_DISTRIBUTION

Use a distribution where some individuals are set to a value of zero and the rest to a given value. Set Expiration_Proportion_0 and Expiration_Peak_2_Value. This distribution does not use the parameters set for CONSTANT_DISTRIBUTION.

DUAL_EXPONENTIAL_DISTRIBUTION

Use two exponential distributions with given means. Set Expiration_Mean_1, Expiration_Mean_2, and Expiration_Proportion_1. This distribution does not use the parameters set for EXPONENTIAL_DISTRIBUTION.

 
{
    "Expiration_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Expiration_Gaussian_Mean": 4,
    "Expiration_Gaussian_Std_Dev": 1
}

Expiration_Exponential

float

0

3.4E+38

6

The mean expiration time of the trap, in days, when Expiration_Distribution is set to EXPONENTIAL_DISTRIBUTION.

 
{
    "Expiration_Distribution": "EXPONENTIAL_DISTRIBUTION",
    "Expiration_Exponential": 4.25
}

Expiration_Guassian_Mean

float

0

3.4E+38

6

The mean expiration time of the trap, in days, when Expiration_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Expiration_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Expiration_Gaussian_Mean": 4,
    "Expiration_Gaussian_Std_Dev": 1
}

Expiration_Gaussian_Std_Dev

float

1.18E-38

3.4E+38

1

The standard deviation of the expiration time of the trap, in days, when Expiration_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Expiration_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Expiration_Gaussian_Mean": 4,
    "Expiration_Gaussian_Std_Dev": 1
}

Expiration_Kappa

float

1.18E-38

3.4E+38

1

The shape value for the expiration time of the trap, in days, when Expiration_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Expiration_Distribution": "WEIBULL_DISTRIBUTION",
    "Expiration_Kappa": 0.9,
    "Expiration_Lambda": 1.5
}

Expiration_Lambda

float

1.18E-38

3.4E+38

1

The scale value for the expriation time of the trap, in days, when Expiration_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Expiration_Distribution": "WEIBULL_DISTRIBUTION",
    "Expiration_Kappa": 0.9,
    "Expiration_Lambda": 1.5
}

Expiration_Log_Normal_Mu

float

-3.40E+38

3.4E+38

6

The mean of the natural log of the expiration time of the trap, in days, when Expiration_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Expiration_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Expiration_Log_Normal_Mu": 9,
    "Expiration_Log_Normal_Sigma": 2
}

Expiration_Log_Normal_Sigma

float

-3.40E+38

3.4E+38

1

The standard deviation of the natural log of the expiration time of the trap, in days, when Expiration_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Expiration_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Expiration_Log_Normal_Mu": 9,
    "Expiration_Log_Normal_Sigma": 2
}

Expiration_Max

float

0

3.4E+38

1

The maximum time to expiration of the trap, in days, when Expiration_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Expiration_Distribution": "UNIFORM_DISTRIBUTION",
    "Expiration_Min": 2,
    "Expiration_Max": 7
}

Expiration_Mean_1

float

1.18E-38

3.4E+38

1

The mean of the first exponential distribution in days, when Exponential_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Expiration_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Expiration_Mean_1": 4,
    "Expiration_Mean_2": 12,
    "Expiration_Proportion_1": 0.2
}

Expiration_Mean_2

float

1.18E-38

3.4E+38

1

The mean of the second exponential distribution in days, when Exponential_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Expiration_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Expiration_Mean_1": 4,
    "Expiration_Mean_2": 12,
    "Expiration_Proportion_1": 0.2
}

Expiration_Min

float

0

3.4E+38

0

The minimum time to expiration of the trap, in days, when Expiration_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Expiration_Distribution": "UNIFORM_DISTRIBUTION",
    "Expiration_Min": 2,
    "Expiration_Max": 7
}

Expiration_Peak_2_Value

float

0

3.4E+38

1

The expiration value, in days, to assign to the remaining traps when Expiration_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Expiration_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Expiration_Proportion_0": 0.25,
    "Expiration_Peak_2_Value": 5
}

Expiration_Poisson_Mean

float

0

3.4E+38

6

The mean of the expiration time of the trap, in days, when Expiration_Distribution is set to POISSON_DISTRIBUTION.

 
{
    "Expiration_Distribution": "POISSON_DISTRIBUTION",
    "Expiration_Poisson_Mean": 5
}

Expiration_Proportion_0

float

0

1

1

The proportion of traps to assign a value of zero days to expiration when Expiration_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Expiration_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Expiration_Proportion_0": 0.25,
    "Expiration_Peak_2_Value": 5
}

Expiration_Proportion_1

float

0

1

1

The proportion of traps in the first exponential distribution when Expiration_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Expiration_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Expiration_Mean_1": 4,
    "Expiration_Mean_2": 12,
    "Expiration_Proportion_1": 0.2
}

Insecticide_Name

string

NA

NA

UNINITIALIZED STRING

The name of the insecticide defined in <config.Insecticides> for this intervention. If insecticides are being used, this must be defined as one of those values; if they are not being used this can be empty. It cannot have a value if you did not configure <config.Insecticides>.

 
{
    "Insecticide_Name": "carbamate"
}

Intervention_Name

string

NA

NA

SugarTrap

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "SugarTrap",
        "Intervention_Name": "Mosquito sugar feeding, nationwide campaign"
    }
}

Killing_Config

JSON object

NA

NA

NA

The configuration for the effects of killing of the targeted stage. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Killing_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0.5,
        "class": "WaningEffectBox"
    }
}

New_Property_Value

string

NA

NA

NA

An optional NodeProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

{
    "Events": [{
        "class": "CampaignEvent",
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 1460,
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Target_Demographic": "Everyone",
            "Intervention_Config": {
                "class": "SugarTrap",
                "Cost_To_Consumer": 3.75,
                "Killing_Config": {
                    "class": "WaningEffectExponential",
                    "Decay_Time_Constant": 2190,
                    "Initial_Effect": 0.1
                },
                "Expiration_Distribution": "CONSTANT_DISTRIBUTION",
                "Expiration_Constant": 360
            }
        }
    }],
    "Use_Defaults": 1
}
Individual-level interventions

Individual-level interventions determine what will be distributed to individuals to reduce the spread of a disease. For example, distributing vaccines or drugs are individual-level interventions. In the schema, these are labeled as IndividualTargeted.

It is also possible (but not required) to configure why a particular intervention is distributed by adding trigger conditions to the intervention. For example, interventions can be triggered by notifications broadcast after some an event, such as Births (the individual’s own birth), GaveBirth, NewInfectionEvent, and more. It’s also possible to have one intervention trigger another intervention by asking the first intervention to broadcast a unique string, and having the second intervention be triggered upon receipt of that string. See Event list.

Individual-level interventions can be used as part of configuring a cascade of care along with the individual properties set in the demographics file. Use Disqualifying_Properties to disqualify individuals who would otherwise receive the intervention and New_Property_Value to assign a new value when the intervention is received. For example, you can assign a property value after receiving the first-line treatment for a disease and prevent anyone from receiving the second-line treatment unless they have that property value and are still symptomatic.

Vector control

The following individual-level interventions are commonly used for vector control.

Intervention

Target life stage

Target biting preference

Target biting location

Effect

HumanHostSeekingTrap

feeding cycle

human

killing

IRSHousingModification

feeding cycle

human

indoor

killing, blocking

Ivermectin

feeding cycle

human

all

killing

ScreeningHousingModification

feeding cycle

human

indoor

killing, blocking

SimpleBednet

feeding cycle

human

indoor

killing, blocking

SimpleIndividualRepellent

feeding cycle

human

all

blocking

SpatialRepellentHousingModification

feeding cycle

human

indoor

killing, blocking
Summary table of individual-level interventions

The following table provides an at-a-glance overview of the individual-level interventions.

Intervention

Short description

Able to be serialized?

Uses insecticides

Time-based expiration?

Purge existing?

Vector killing contributes to:

Vector effects

AdherentDrug

Model adherence with AntimalarialDrug

Y

Y/N

AntimalarialDrug

Distribute one drug to an individual

Y

Sort of

BitingRisk

Adjust an individual’s relative chance of getting bitten by a mosquito

Sort of

Sort of

BroadcastEvent

Send an event to an individual

BroadcastEventToOtherNodes

Send an event to individuals in other nodes

ControlledVaccine

Manage how an individual receives a vaccine

Y

Y

Controlled

DelayedIntervention

Wait before sending event or intervention

Y

Y

HumanHostSeekingTrap

Attracts and kills host-seeking mosquitoes

Y

Y

Indoor Die Before Feeding

Attracting, killing

ImmunityBloodTest

Check if an individual’s immunity meets a specified threshold

Y

IndividualImmunityChanger

Not tested with Malaria

Y

IRSHousingModification

Indoor Residual Spraying for an individual

Y

Y

Y

Indoor Die After Feeding

Repelling, killing

IVCalendar

Distribute an intervention to an indivdiual when they reach a specific age

Y

Sort of

Ivermectin

Kill vectors feeding on human

Y

Y

Sort of

Y

Indoor/Outdoor Die After Feeding

Killing

MalariaDiagnostic

Test if an individual is infected

Y/N

MigrateIndividuals

Schedule a trip for a single individual

Y/N

MultiEffectBoosterVaccine

Not tested with Malaria

Y

MultiEffectVaccine

Not tested with Malaria

Y

Wanning Effect Expiration

MultiInsecticideIRSHousingModification

Individual IRS involving multiple insecticides

Y

Y

Y

Indoor Die Before/After Feeding

Repelling, killing

MultiInsecticideUsageDependentBednet

ITN treated with multiple insecticides

Y

Y

Y

By Name

Indoor Die Before/After Feeding

Repelling, blocking, killing

MultiInterventionDistributor

Distribute multiple interventions instead of one

Y/N

MultiPackComboDrug

Control doses of multiple drugs

Y

OutbreakIndividual

Infect individuals

N

OutbreakIndividualMalariaGenetics

Infect individuals with a specific parasite genome

N

OutbreakIndividualMalariaVarGenes

Infect individuals with specific antigen values

N

PropertyValueChanger

Change an individual’s IndividualProperty value

Y

RTSSVaccine

Boost CSP antibody

Y

ScreeningHousingModification

Modify an individual’s house with screens that kill and block vectors

Y

Y

Y

Indoor- Die Before/After Feeding

Repelling, killing

SimpleBednet

Insecticide Treated Net (ITN)

Y

Y

Wanning Effect Expiration

By Name

Indoor- Die Before/After Feeding

Repelling, blocking, killing

SimpleBoosterVaccine

Not tested with Malaria

Y

SimpleDiagnostic

Test if an individual is infected using sensitivity and specificity diagnostics

Y/N

SimpleHealthSeekingBehavior

Randomly distribute interventions or events to individuals

Y/N

SimpleHousingModification

Block & kill vectors at an individual’s house

Y

Y

Y

Indoor Die Before/After Feeding

Repelling, killing

SimpleIndividualRepellent

Block vectors from biting an individual

Y

Y

Repelling

SimpleVaccine

Modify the acquisition, transmission, and mortality effects by distributing a vaccine

Y

Wanning Effect Expiration

SpatialRepellentHousingModification

Block vectors from an individual’s house

Y

Y

Y

Repelling

StandardDiagnostic

Test if individuals are infected using sensitivity and specificity diagnostics

Y/N

UsageDependentBednet

Control when individuals use bednets (ITN)

Y

Y

Y

By Name

Indoor Die Before/After Feeding

Repelling, blocking, killing
AdherentDrug

The AdherentDrug class is an individual-level intervention that extends AntimalarialDrug class and allows for incorporating different patterns of adherence for taking packs of anti-malarial drugs. Non-adherence means that the drugs will not be taken on the prescribed schedule; this will lengthen the time taken to clear parasites from the person’s system, and can lengthen the duration that a feeding mosquito may become infected.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Adherence_Config

JSON object

NA

NA

NA

Configuration for the probability of taking a dose of the specified anti-malarial. Use a waning effect class, Waning effect classes, to specify how this probability changes over time.

 
{
    "Adherence_Config": {
        "class": "WaningEffectMapCount",
        "Initial_Effect": 1.0,
        "Durability_Map": {
            "Times": [
                1.0,
                2.0,
                3.0,
                4.0
            ],
            "Values": [
                0.1,
                0.1,
                0.1,
                0.1
            ]
        }
    }
}

Cost_To_Consumer

float

0

99999

10

The unit cost per drug (unamortized).

 
{
    "Cost_To_Consumer": 10
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to nodes with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Dose_Interval

float

0

1000

1

The number of days to wait between the doses, defined in the Doses parameter.

 
{
    "Dose_Interval": 10
}

Doses

array of strings

NA

NA

NA

This is a two dimensional array of the drug names defined in the config. parameter Malaria_Drug_Params. Each inner array defines the drugs to be taken for a single dose. The number of doses is determined by the number of inner arrays. An empty dose is allowed and counts as if the person did not take any doses that day. If multiple pills of the same drug are taken in the same dose, that drug name can be specified multiple times.

 
{
    "Doses": [
    ["TestDrugA", "TestDrugB"],
    ["TestDrugA"],
    ["TestDrugA", "TestDrugB"],
    ["TestDrugB"]
]
}

Intervention_Name

string

NA

NA

AdherentDrug

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "AdherentDrug",
        "Intervention_Name": "Isoniazid with mediocre adherence"
    }
}

Max_Dose_Consideration_Duration

float

1.0/24.0 (1hr)

3.40E+038

3.40E+038

The maximum number of days that an individual will consider taking the doses of the drug.

 
{
    "class": "AdherentDrug",
    "Cost_To_Consumer": 1,
    "Drug_Type": "TestDrug",
    "Dosing_Type": "FullTreatmentCourse",
    "Adherence_Config": {
        "class": "WaningEffectMapCount",
        "Initial_Effect": 1.0,
        "Durability_Map": {
            "Times": [
                1.0,
                2.0,
                3.0
            ],
            "Values": [
                1.0,
                0.0,
                1.0
            ]
        }
    },
    "Non_Adherence_Options": [
        "NEXT_DOSAGE_TIME"
    ],
    "Non_Adherence_Distribution": [
        1.0
    ],
    "Max_Dose_Consideration_Duration": 4,
    "Took_Dose_Event": "NewClinicalCase"
}

New_Property_Value

string

NA

NA

“

An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Non_Adherence_Distribution

array of floats

0

1

0

The non adherence probability value(s) assigned to the corresponding options in Non_Adherence_Options. The sum of non adherence distribution values must equal a total of 1.

 
{
    "Non_Adherence_Distribution": [
        0.7,
        0.3
    ]
}

Non_Adherence_Options

array of eunums

NA

NA

NEXT_UPDATE

Defines the action the person takes if they do not take a particular dose, are not adherent. Possible values are:

NEXT_UPDATE

The person does not take the dose during this update, but will consider taking during the next update (and may not take the dose then). If this person is infected, the time to the next update depends on Infection_Updates_Per_Timestep.

LOST_TAKE_NEXT

The person lost the dose and so takes the next dose in the pill pack (so the total number of pills is reduced).

NEXT_DOSAGE_TIME

The person missed a dose and waits until the next dosage time to take the pill, in order to remain on the prescribed dosage schedule.

STOP

The person decides to stop taking the pills (or has lost the pill pack). Pills taken prior to STOP will have an effect, no remaining pills will be taken.

 
{
    "AD_Non_Adherence_Options": [
        "NEXT_DOSAGE_TIME",
        "NEXT_UPDATE"
    ]
}

Took_Dose_Event

string

NA

NA

“”

This event is broadcast each time a person takes a dose from the pill pack.

 
{
    "Took_Dose_Event": "TakingDrug"
}

{
    "class": "AdherentDrug",
    "Dont_Allow_Duplicates": 1,
    "Cost_To_Consumer": 1,
    "Doses": [
        ["TestDrugA", "TestDrugB"],
        ["TestDrugA"],
        ["TestDrugA", "TestDrugB"],
        [],
        ["TestDrugB"]
    ],
    "Dose_Interval": 5,
    "Adherence_Config": {
        "class": "WaningEffectCombo",
        "Add_Effects": 0,
        "Expires_When_All_Expire": 0,
        "Effect_List": [{
                "class": "WaningEffectMapLinearAge",
                "Initial_Effect": 1.0,
                "Durability_Map": {
                    "Times": [0.0, 12.99999, 13.0, 125.0],
                    "Values": [0.0, 0.0, 1.0, 1.0]
                }
            },
            {
                "class": "WaningEffectMapCount",
                "Initial_Effect": 1.0,
                "Durability_Map": {
                    "Times": [1.0, 2.0, 3.0, 4.0, 5.0],
                    "Values": [0.1, 0.2, 0.3, 0.4, 0.5]
                }
            }
        ]
    },
    "Non_Adherence_Options": ["NEXT_UPDATE", "NEXT_DOSAGE_TIME", "LOST_TAKE_NEXT", "STOP"],
    "Non_Adherence_Distribution": [0.4, 0.3, 0.2, 0.1],
    "Max_Dose_Consideration_Duration": 40,
    "Took_Dose_Event": "TakingDrug"
}
AntimalarialDrug

The AntimalarialDrug intervention is used to apply drug-based control efforts to malaria simulations. When configuring this intervention, note that the configuration parameter Malaria_Drug_Params must be configured, as it governs how particular anti-malarial drugs will behave.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Cost_To_Consumer

float

0

999999

10

The unit cost per drug (unamortized).

 
{
    "Intervention_Config": {
        "Cost_To_Consumer": 3.75,
        "Dosing_Type": "FullTreatmentWhenSymptom",
        "Drug_Type": "Chloroquine",
        "class": "AntimalarialDrug"
    }
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Drug_Type

enum

NA

NA

UNINITIALIZED STRING

The type of drug to distribute in a drugs intervention. This must be a value defined in the configuration parameter Malaria_Drug_Params.

 
{
    "Intervention_Config": {
        "Cost_To_Consumer": 3.75,
        "Dosing_Type": "FullTreatmentWhenSymptom",
        "Drug_Type": "Chloroquine",
        "class": "AntimalarialDrug"
    }
}

Intervention_Name

string

NA

NA

AntiMalarialDrug

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "AntiMalariaDrug",
        "Intervention_Name": "Doxycycline treatment"
    }
}

New_Property_Value

string

NA

NA

UNINITIALIZED STRING

An optional IndividualProperty key:value pair assigned when the intervention is first updated. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

{
    "Use_Defaults": 1,
    "Events": [{
        "class": "CampaignEvent",
         "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 270,
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Target_Demographic": "Everyone",
            "Demographic_Coverage": 0.8,
            "Intervention_Config": {
                "class": "AntimalarialDrug",
                "Cost_To_Consumer": 3.75,
                "Dont_Allow_Duplicates": 1,
                "Drug_Type": "Chloroquine"
            },
            "Number_Repetitions": 1,
            "Timesteps_Between_Repetitions": 0
        }
    }]
}
BitingRisk

The BitingRisk class allows you to adjust the relative risk that the person is bitten by a vector. As an intervention, it allows you to target specific groups at specific times during the simulation.

The relative biting rate can be initially set by setting Enable_Demographic_Risk to 1 and then configuring IndividualAttributes, RiskDistributionFlag, RiskDistributionParam1, and RiskDistributionParam2. This will give each new person their own relative risk.

The relative biting rate can be thought of as having two parts: the relative risk value and the age dependent value. Age dependence is set using the Age_Dependent_Biting_Risk_Type parameter. These two values (from age dependence and relative risk) are multiplied to get the resulting rate, which is then used to control how much contagion is deposited from an infectious individual and the probability that an infection is acquired.

This intervention expires. To reset it, distribute another BitingRisk intervention that sets it back to the original value. Note that this is a relative biting rate. For example, giving everyone a value of 10 is the same as giving everyone a value of 1. This intervention is used to indicate some individuals are more likely to be bitten than others.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Intervention_Name

string

NA

NA

BitingRisk

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "BitingRisk",
        "Intervention_Name": "Relative biting risk with bednet usage"
    }
}

New_Property_Value

string

NA

NA

UNINITIALIZED STRING

An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Risk_Constant

float

0

3.40282E+38

6

The risk to use for all individuals when Risk_Distribution is set to CONSTANT_DISTRIBUTION.

 
{
    "Risk_Distribution": "CONSTANT_DISTRIBUTION",
    "Risk_Constant": 8
}

Risk_Distribution

enum

NA

NA

NOT_INITIALIZED

The distribution type to use for assigning the relative risk of being bitten by a mosquito to each individual. Each assigned value is a random draw from the distribution.

Possible values are:

NOT_INITIALIZED

No distribution set.

CONSTANT_DISTRIBUTION

Use the same value for each individual. Set Risk_Constant.

UNIFORM_DISTRIBUTION

Use a uniform distribution with a given minimum and maximum. Set Risk_Max and Risk_Min.

GAUSSIAN_DISTRIBUTION

The distribution is Gaussian (or normal). Values are resampled to ensure >= 0. Set Risk_Gaussian_Mean and Risk_Gaussian_Std_Dev.

EXPONENTIAL_DISTRIBUTION

The distribution is exponential with a given mean. Set Risk_Exponential.

WEIBULL_DISTRIBUTION

Use a Weibull distribution with a given shape and scale. Set Risk_Kappa and Risk_Lambda.

LOG_NORMAL_DISTRIBUTION

Use a log-normal distribution with a given mean and width. Set Risk_Log_Normal_Mean and Risk_Log_Normal_Width.

POISSON_DISTRIBUTION

Use a Poisson distribution with a given mean. Set Risk_Poisson_Mean.

DUAL_CONSTANT_DISTRIBUTION

Use a distribution where some individuals are set to a value of zero and the rest to a given value. Set Risk_Proportion_0 and Peak_2_Value. This distribution does not use the parameters set for CONSTANT_DISTRIBUTION.

DUAL_EXPONENTIAL_DISTRIBUTION

Use two exponential distributions with given means. Set Risk_Mean_1, Risk_Mean_2, and Risk_Proportion_1. This distribution does not use the parameters set for EXPONENTIAL_DISTRIBUTION.

 
{
    "Risk_Distribution": "WEIBULL_DISTRIBUTION",
    "Risk_Kappa": 0.9,
    "Risk_Lambda": 1.5
}

Risk_Exponential

float

0

3.40282E+38

-1

The mean of the biting risk when Risk_Distribution is set to EXPONENTIAL_DISTRIBUTION.

 
{
    "Risk_Distribution": "EXPONENTIAL_DISTRIBUTION",
    "Risk_Exponential": 4.25
}

Risk_Gaussian_Mean

float

0

3.40282E+38

-1

The mean of the biting risk when Risk_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Risk_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Risk_Gaussian_Mean": 8,
    "Risk_Gaussian_Std_Dev": 1.5
}

Risk_Gaussian_Std_Dev

float

1.17549E-38

3.40282E+38

-1

The standard deviation of the biting risk when Risk_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Risk_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Risk_Gaussian_Mean": 8,
    "Risk_Gaussian_Std_Dev": 1.5
}

Risk_Kappa

float

1.17549E-38

3.40282E+38

-1

The shape value for the biting risk when Risk_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Risk_Distribution": "WEIBULL_DISTRIBUTION",
    "Risk_Kappa": 0.9,
    "Risk_Lambda": 1.5
}

Risk_Lambda

float

1.17549E-38

3.40282E+38

-1

The scale value for the biting risk when Risk_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Risk_Distribution": "WEIBULL_DISTRIBUTION",
    "Risk_Kappa": 0.9,
    "Risk_Lambda": 1.5
}

Risk_Log_Normal_Mu

float

-3.40282e+38

1.70141e+38

3.40282E+38

The mean of the biting risk when Risk_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Risk_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Risk_Log_Normal_Mu": 9,
    "Risk_Log_Normal_Sigma": 2
}

Risk_Log_Normal_Sigma

float

-3.40282e+38

1.70141e+38

3.40282E+38

The width of the biting risk when Risk_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Risk_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Risk_Log_Normal_Mu": 9,
    "Risk_Log_Normal_Sigma": 2
}

Risk_Max

float

0

3.40282E+38

-1

The maximum biting risk when Risk_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Risk_Distribution": "UNIFORM_DISTRIBUTION",
    "Risk_Min": 2,
    "Risk_Max": 7
}

Risk_Mean_1

float

1.17549E-38

3.40282E+38

-1

The mean of the first exponential distribution when Risk_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Risk_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Risk_Mean_1": 4,
    "Risk_Mean_2": 12,
    "Risk_Proportion_1": 0.2
}

Risk_Mean_2

float

1.17549E-38

3.40282E+38

-1

The mean of the second exponential distribution when Risk_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Risk_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Risk_Mean_1": 4,
    "Risk_Mean_2": 12,
    "Risk_Proportion_1": 0.2
}

Risk_Min

float

0

3.40282E+38

-1

The minimum biting risk when Risk_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Risk_Distribution": "UNIFORM_DISTRIBUTION",
    "Risk_Min": 2,
    "Risk_Max": 7
}

Risk_Peak_2_Value

float

0

3.40282E+38

-1

The biting risk value to assign to the remaining individuals when Risk_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Risk_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Risk_Proportion_0": 0.25,
    "Risk_Peak_2_Value": 5
}

Risk_Poisson_Mean

float

0

3.40282E+38

-1

The mean of the biting risk when Risk_Distribution is set to POISSON_DISTRIBUTION.

 
{
    "Risk_Distribution": "POISSON_DISTRIBUTION",
    "Risk_Poisson_Mean": 5
}

Risk_Proportion_0

float

0

1

-1

The proportion of individuals to assign a value of zero biting risk when Risk_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Risk_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Risk_Proportion_0": 0.25,
    "Risk_Peak_2_Value": 5
}

Risk_Proportion_1

float

0

1

-1

The proportion of individuals in the first exponential distribution when Risk_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Risk_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Risk_Mean_1": 4,
    "Risk_Mean_2": 12,
    "Risk_Proportion_1": 0.2
}

{
  "Intervention_Config": {
    "class": "BitingRisk",
    "Risk_Distribution": "CONSTANT_DISTRIBUTION",
    "Risk_Constant": 0.1
  }
}
BroadcastEvent

The BroadcastEvent intervention class is an individual-level class that immediately broadcasts the event trigger you specify. This campaign event is typically used with other classes that monitor for a broadcast event, such as NodeLevelHealthTriggeredIV or CommunityHealthWorkerEventCoordinator.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Broadcast_Event

string

NA

NA

UNINITIALIZED STRING

The name of the event that will trigger the intervention. See Event list for possible values.

 
{
    "Broadcast_Event": "HIVNeedsHIVTest"
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Intervention_Name

string

NA

NA

BroadcastEvent

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "BroadcastEvent",
        "Intervention_Name": "Broadcast that disease treatment was received"
    }
}

New_Property_Value

string

NA

NA

UNINITIALIZED STRING

An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

{
    "Event_Coordinator_Config": {
        "Demographic_Coverage": 0.03,
        "Intervention_Config": {
            "Intervention_List": [{
                    "Bednet_Type": "ITN",
                    "Blocking_Rate": 0.9,
                    "Cost_To_Consumer": 3.75,
                    "Durability_Time_Profile": "DECAYDURABILITY",
                    "Killing_Rate": 0.6,
                    "Primary_Decay_Time_Constant": 1460,
                    "Secondary_Decay_Time_Constant": 730,
                    "class": "SimpleBednet"
                },
                {
                    "Broadcast_Event": "Received_ITN",
                    "class": "BroadcastEvent"
                }
            ],
            "class": "MultiInterventionDistributor"
        },
        "Target_Age_Max": 5,
        "Target_Age_Min": 0,
        "Target_Demographic": "ExplicitAgeRanges",
        "class": "StandardInterventionDistributionEventCoordinator"
    },
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Start_Day": 0,
    "class": "CampaignEvent"
}
BroadcastEventToOtherNodes

The BroadcastEventToOtherNodes intervention class allows events to be sent from one node to another. For example, if an individual in one node has been diagnosed, drugs may be distributed to individuals in surrounding nodes.

When this intervention is updated, the event to be broadcast is cached to be distributed to the nodes. After the people have migrated, the event information is distributed to the nodes (i.e. it does support multi-core). During the next time step, the nodes will update their node-level interventions and then broadcast the events from other nodes to ALL the people in the node. This is different from interventions that only broadcast the event in the current node for the person who had the intervention. Distances between nodes use the Longitude and Latitude defined in the demographics file, and use the Haversine Formula for calculating the great-circle distance.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Event_Trigger

enum

NA

NA

NA

The name of the event to broadcast to selected nodes. See Event list for possible values.

 
{
    "Event_Trigger": "VaccinateNeighbors"
}

Include_My_Node

boolean

NA

NA

0

Set to true (1) to broadcast the event to the current node.

 
{
    "Actual_IndividualIntervention_Config": {
        "class": "BroadcastEventToOtherNodes",
        "Event_Trigger": "VaccinateNeighbors",
        "Include_My_Node": 1,
        "Node_Selection_Type": "DISTANCE_AND_MIGRATION",
        "Max_Distance_To_Other_Nodes_Km": 1
    }
}

Intervention_Name

string

NA

NA

BroadcastEventToOtherNodes

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "BroadcastEventToOtherNodes",
        "Intervention_Name": "Malaria diagnosis triggering reactive case detection"
    }
}

Max_Distance_To_Other_Nodes_Km

float

0

3.40E+38

3.40E+38

The maximum distance, in kilometers, to the destination node for the node to be selected. The location values used are those entered in the demographics file. Used only if Node_Selection_Type is either DISTANCE_ONLY or DISTANCE_AND_MIGRATION.

 
{
    "Actual_IndividualIntervention_Config": {
        "class": "BroadcastEventToOtherNodes",
        "Event_Trigger": "VaccinateNeighbors",
        "Include_My_Node": 1,
        "Node_Selection_Type": "DISTANCE_AND_MIGRATION",
        "Max_Distance_To_Other_Nodes_Km": 1
    }
}

New_Property_Value

string

NA

NA

UNITITIALIZED STRING

An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Node_Selection_Type

enum

NA

NA

DISTANCE_ONLY

The method by which to select nodes to receive the event. Possible values are:

DISTANCE_ONLY

Nodes located within the distance specified by Max_Distance_To_Other_Nodes_Km are selected.

MIGRATION_NODES_ONLY

Nodes that are local, regional, or connected in the migration file are selected.

DISTANCE_AND_MIGRATION

Nodes are selected using DISTANCE_ONLY and MIGRATION_NODES_ONLY criteria.

 
{
    "Actual_IndividualIntervention_Config": {
        "class": "BroadcastEventToOtherNodes",
        "Event_Trigger": "VaccinateNeighbors",
        "Include_My_Node": 1,
        "Node_Selection_Type": "DISTANCE_AND_MIGRATION",
        "Max_Distance_To_Other_Nodes_Km": 1
    }
}

{
    "Use_Defaults": 1,
    "Events": [{
            "Event_Name": "Broadcast to Other Households If Person Infected",
            "class": "CampaignEvent",
            "Start_Day": 0,
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Demographic_Coverage": 1,
                "Intervention_Config": {
                    "class": "NodeLevelHealthTriggeredIV",
                    "Trigger_Condition_List": ["NewClinicalCase"],
                    "Blackout_Event_Trigger": "Blackout",
                    "Blackout_Period": 0.0,
                    "Blackout_On_First_Occurrence": 0,
                    "Actual_IndividualIntervention_Config": {
                        "class": "BroadcastEventToOtherNodes",
                        "Event_Trigger": "VaccinateNeighbors",
                        "Include_My_Node": 1,
                        "Node_Selection_Type": "DISTANCE_AND_MIGRATION",
                        "Max_Distance_To_Other_Nodes_Km": 1
                    }
                }
            }
        },
        {
            "Event_Name": "Get Vaccinated If Neighbor Infected",
            "class": "CampaignEvent",
            "Start_Day": 0,
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Demographic_Coverage": 1,
                "Intervention_Config": {
                    "class": "NodeLevelHealthTriggeredIV",
                    "Trigger_Condition_List": ["VaccinateNeighbors"],
                    "Blackout_Event_Trigger": "Blackout",
                    "Blackout_Period": 0.0,
                    "Blackout_On_First_Occurrence": 0,
                    "Actual_IndividualIntervention_Config": {
                        "class": "AntimalarialDrug",
                        "Cost_To_Consumer": 10,
                        "Dosing_Type": "FullTreatmentParasiteDetect",
                        "Drug_Type": "Chloroquine",
                        "Dont_Allow_Duplicates": 1
                    }
                }
            }
        }
    ]
}
ControlledVaccine

The ControlledVaccine intervention class is a subclass of SimpleVaccine so it contains all functionality of SimpleVaccine, but provides more control over additional events and event triggers. This intervention can be configured so that specific events are broadcast when individuals receive an intervention or when the intervention expires. Further, individuals can be re-vaccinated, using a configurable wait time between vaccinations.

Note that one of the controls of this intervention is to not allow a person to receive an additional dose if they received a dose within a certain amount of time. This applies only to ControlledVaccine interventions with the same Intervention_Name, so people can be given multiple vaccines as long as each vaccine has a different value for Intervention_Name.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Cost_To_Consumer

float

0

999999

10

The unit cost per vaccine (unamortized).

 
{
    "Cost_To_Consumer": 10.0
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Distributed_Event_Trigger

enum

NA

NA

NA

The name of the event to be broadcast when the intervention is distributed to an individual. See Event list for possible values. Custom events can be distributed if they are defined in the config parameter Custom_Individual_Events.

 
{
    "Distributed_Event_Trigger": "Vaccinated"
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Duration_To_Wait_Before_Revaccination

float

0

3.40E+38

3.40E+38

The length of time, in days, to wait before revaccinating an individual. After this time has passed, the individual can be revaccinated. If the first vaccine has not expired, the individual can receive the effect from both doses of the vaccine.

 
{
    "Duration_To_Wait_Before_Revaccination": 240
}

Efficacy_Is_Multiplicative

boolean

NA

NA

1

The overall vaccine efficacy when individuals receive more than one vaccine. When set to true (1), the vaccine efficacies are multiplied together; when set to false (0), the efficacies are additive.

 
{
    "Intervention_Config": {
        "class": "SimpleVaccine",
        "Cost_To_Consumer": 10,
        "Vaccine_Type": "AcquisitionBlocking",
        "Vaccine_Take": 1,
        "Efficacy_Is_Multiplicative": 0,
        "Waning_Config": {
            "class": "WaningEffectConstant",
            "Initial_Effect": 0.3
        }
    }
}

Expired_Event_Trigger

enum

NA

NA

NA

The name of the event to be broadcast when the intervention is distributed to an individual. See Event list for possible values. Custom events can be distributed if they are defined in the config parameter Custom_Individual_Events.

 
{
    "Expired_Event_Trigger": "VaccineExpired"
}

Intervention_Name

string

NA

NA

NA

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "ControlledVaccine",
        "Intervention_Name": "Round of three measles vaccinations"
    }
}

New_Property_Value

string

NA

NA

NA

An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Vaccine_Take

float

0

1

1

The rate at which delivered vaccines will successfully stimulate an immune response and achieve the desired efficacy. For example, if it is set to 0.9, there will be a 90 percent chance that the vaccine will start with the specified efficacy, and a 10 percent chance that it will have no efficacy at all.

 
{
    "Intervention_Config": {
        "class": "SimpleVaccine",
        "Cost_To_Consumer": 10,
        "Vaccine_Type": "AcquisitionBlocking",
        "Vaccine_Take": 1,
        "Efficacy_Is_Multiplicative": 0,
        "Waning_Config": {
            "class": "WaningEffectConstant",
            "Initial_Effect": 0.3
        }
    }
}

Vaccine_Type

enum

NA

NA

Generic

The type of vaccine to distribute in a vaccine intervention. Possible values are:

Generic

The vaccine can reduce transmission, acquisition, and mortality.

TransmissionBlocking

The vaccine will reduce pathogen transmission.

AcquisitionBlocking

The vaccine will reduce the acquisition of the pathogen by reducing the force of infection experienced by the vaccinated individual.

MortalityBlocking

The vaccine reduces the disease-mortality rate of a vaccinated individual.

 
{
    "Intervention_Config": {
        "class": "SimpleVaccine",
        "Cost_To_Consumer": 10,
        "Vaccine_Type": "AcquisitionBlocking",
        "Vaccine_Take": 1,
        "Efficacy_Is_Multiplicative": 0,
        "Waning_Config": {
            "class": "WaningEffectConstant",
            "Initial_Effect": 0.3
        }
    }
}

Waning_Config

JSON object

NA

NA

NA

The configuration of how the intervention efficacy wanes over time. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Waning_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 1,
        "class": "WaningEffectBox"
    }
}

{
    "Use_Defaults": 1,
    "Events": [{
            "COMMENT": "Vaccinate Everyone with VaccineA",
            "class": "CampaignEvent",
            "Start_Day": 1,
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Target_Demographic": "Everyone",
                "Demographic_Coverage": 1.0,
                "Intervention_Config": {
                    "class": "ControlledVaccine",
                    "Intervention_Name": "VaccineA",
                    "Cost_To_Consumer": 1,
                    "Vaccine_Type": "AcquisitionBlocking",
                    "Vaccine_Take": 1.0,
                    "Waning_Config": {
                        "class": "WaningEffectMapLinear",
                        "Initial_Effect": 1.0,
                        "Expire_At_Durability_Map_End": 1,
                        "Durability_Map": {
                            "Times": [0, 25, 50],
                            "Values": [1.0, 1.0, 0.0]
                        }
                    },
                    "Distributed_Event_Trigger": "VaccinatedA",
                    "Expired_Event_Trigger": "VaccineExpiredA",
                    "Duration_To_Wait_Before_Revaccination": 40
                }
            }
        },
        {
            "COMMENT": "After the first round expires, distribute a different vaccine, VaccineB",
            "class": "CampaignEvent",
            "Start_Day": 60,
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Target_Demographic": "Everyone",
                "Demographic_Coverage": 1.0,
                "Intervention_Config": {
                    "class": "ControlledVaccine",
                    "Intervention_Name": "VaccineB",
                    "Cost_To_Consumer": 1,
                    "Vaccine_Type": "AcquisitionBlocking",
                    "Vaccine_Take": 1.0,
                    "Waning_Config": {
                        "class": "WaningEffectMapLinear",
                        "Initial_Effect": 1.0,
                        "Expire_At_Durability_Map_End": 1,
                        "Durability_Map": {
                            "Times": [0, 25, 50],
                            "Values": [1.0, 1.0, 0.0]
                        }
                    },
                    "Distributed_Event_Trigger": "VaccinatedB",
                    "Expired_Event_Trigger": "VaccineExpiredB",
                    "Duration_To_Wait_Before_Revaccination": 40
                }
            }
        }
    ]
}
DelayedIntervention

The DelayedIntervention intervention class introduces a delay between when the intervention is distributed to the individual and when they receive the actual intervention. This is due to the frequent occurrences of time delays as individuals seek care and receive treatment. This intervention allows configuration of the distribution type for the delay as well as the fraction of the population that receives the specified intervention.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Actual_IndividualIntervention_Configs

array of JSON objects

NA

NA

NA

An array of nested interventions to be distributed at the end of a delay period, to covered fraction of the population.

 
{
    "Actual_IndividualIntervention_Configs": [
        {
            "Base_Sensitivity": 1,
            "Base_Specificity": 1,
            "Event_Or_Config": "Event",
            "Negative_Diagnosis_Event": "HIVNeedsMaleCircumcision",
            "New_Property_Value": "InterventionStatus:None",
            "Positive_Diagnosis_Event": "NoTreatment",
            "class": "HIVSimpleDiagnostic"
        }
    ]
}

Coverage

float

0

1

1

The proportion of individuals who receive the DelayedIntervention that actually receive the configured interventions. This can be useful to target the intervention to a subset of the people that got the delay. To target the subgroup more specifically, distribute a BroadcastEvent and use a NodeLevelHealthTriggeredIV to distributes the desired intervention.

 
{
    "Coverage": 1.0
}

Delay_Period_Constant

float

0

3.40282E+38

-1

The delay period, in days, to use for all interventions when Delay_Period_Distribution is set to CONSTANT_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "CONSTANT_DISTRIBUTION",
    "Delay_Period_Constant": 8
}

Delay_Period_Distribution

enum

NA

NA

NOT_INITIALIZED

The distribution type to use for assigning the delay period for distributing interventions. Each assigned value is a random draw from the distribution.

Possible values are:

NOT_INITIALIZED

No distribution set.

CONSTANT_DISTRIBUTION

Use the same value for each individual. Set Delay_Period_Constant.

UNIFORM_DISTRIBUTION

Use a uniform distribution with a given minimum and maximum. Set Delay_Period_Max and Delay_Period_Min.

GAUSSIAN_DISTRIBUTION

The distribution is Gaussian (or normal). Values are resampled to ensure >= 0. Set Delay_Period_Gaussian_Mean and Delay_Period_Gaussian_Std_Dev.

EXPONENTIAL_DISTRIBUTION

The distribution is exponential with a given mean. Set Delay_Period_Exponential.

WEIBULL_DISTRIBUTION

Use a Weibull distribution with a given shape and scale. Set Delay_Period_Kappa and Delay_Period_Lambda.

LOG_NORMAL_DISTRIBUTION

Use a log-normal distribution with a given mean and standard deviation of the natural log. Set Delay_Period_Log_Normal_Mu and Delay_Period_Log_Normal_Sigma.

POISSON_DISTRIBUTION

Use a Poisson distribution with a given mean. Set Delay_Period_Poisson_Mean.

DUAL_CONSTANT_DISTRIBUTION

Use a distribution where some individuals are set to a value of zero and the rest to a given value. Set Delay_Period_Proportion_0 and Peak_2_Value.

DUAL_EXPONENTIAL_DISTRIBUTION

Use two exponential distributions with given means. Set Delay_Period_Mean_1, Delay_Period_Mean_2, and Delay_Period_Proportion_1.

 
{
    "Delay_Period_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Delay_Period_Gaussian_Mean": 8,
    "Delay_Period_Gaussian_Std_Dev": 1.5
}

Delay_Period_Exponential

float

0

3.40282E+38

-1

The mean of the delay period, in days, when Delay_Period_Distribution is set to EXPONENTIAL_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "EXPONENTIAL_DISTRIBUTION",
    "Delay_Period_Exponential": 4.25
}

Delay_Period_Gaussian_Mean

float

0

3.40282E+38

-1

The mean of the delay period, in days, when Delay_Period_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Delay_Period_Gaussian_Mean": 8,
    "Delay_Period_Gaussian_Std_Dev": 1.5
}

Delay_Period_Gaussian_Std_Dev

float

1.17549E-38

3.40282E+38

-1

The standard deviation of the delay period, in days, when Delay_Period_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Delay_Period_Gaussian_Mean": 8,
    "Delay_Period_Gaussian_Std_Dev": 1.5
}

Delay_Period_Kappa

float

1.17549E-38

3.40282E+38

-1

The shape value for the delay period, in days, when Delay_Period_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "WEIBULL_DISTRIBUTION",
    "Delay_Period_Kappa": 0.9,
    "Delay_Period_Lambda": 1.5
}

Delay_Period_Lambda

float

1.17549E-38

3.40282E+38

-1

The scale value for the delay period, in days, when Delay_Period_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "WEIBULL_DISTRIBUTION",
    "Delay_Period_Kappa": 0.9,
    "Delay_Period_Lambda": 1.5
}

Delay_Period_Log_Normal_Mu

float

-3.40282e+38

1.70141e+38

3.40282e+38

The mean of the natural log of the delay period, in days, when Delay_Period_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Delay_Period_Log_Normal_Mu": 9,
    "Delay_Period_Log_Normal_Sigma": 2
}

Delay_Period_Log_Normal_Sigma

float

-3.40282e+38

1.70141e+38

3.40282E+38

The standard deviation of the natural log of the delay period, in days, when Delay_Period_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Delay_Period_Log_Normal_Mu": 9,
    "Delay_Period_Log_Normal_Sigma": 2
}

Delay_Period_Max

float

0

3.40282E+38

-1

The maximum delay period, in days, when Delay_Period_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "UNIFORM_DISTRIBUTION",
    "Delay_Period_Min": 2,
    "Delay_Period_Max": 7
}

Delay_Period_Mean_1

float

1.17549E-38

3.40282E+38

-1

The mean of the first exponential distribution, in days, when Delay_Period_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Delay_Period_Mean_1": 4,
    "Delay_Period_Mean_2": 12,
    "Delay_Period_Proportion_1": 0.2
}

Delay_Period_Mean_2

float

1.17549E-38

3.40282E+38

-1

The mean of the second exponential distribution, in days, when Delay_Period_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Delay_Period_Mean_1": 4,
    "Delay_Period_Mean_2": 12,
    "Delay_Period_Proportion_1": 0.2
}

Delay_Period_Min

float

0

3.40282E+38

-1

The minimum delay period, in days, when Delay_Period_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "UNIFORM_DISTRIBUTION",
    "Delay_Period_Min": 2,
    "Delay_Period_Max": 7
}

Delay_Period_Peak_2_Value

float

0

3.40282E+38

-1

The delay period value to assign to the remaining interventions when Delay_Period_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Delay_Period_Proportion_0": 0.25,
    "Delay_Period_Peak_2_Value": 5
}

Delay_Period_Poisson_Mean

float

0

3.40282E+38

-1

The mean of the delay period when Delay_Period_Distribution is set to POISSON_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "POISSON_DISTRIBUTION",
    "Delay_Period_Poisson_Mean": 5
}

Delay_Period_Proportion_0

float

0

1

-1

The proportion of interventions to assign a value of zero delay when Delay_Period_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Delay_Period_Proportion_0": 0.25,
    "Delay_Period_Peak_2_Value": 5
}

Delay_Period_Proportion_1

float

0

1

-1

The proportion of interventions in the first exponential distribution when Delay_Period_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Delay_Period_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Delay_Period_Mean_1": 4,
    "Delay_Period_Mean_2": 12,
    "Delay_Period_Proportion_1": 0.2
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

0

1

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Intervention_Name

string

NA

NA

DelayedIntervention

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "DelayedIntervention",
        "Intervention_Name": "Treatment one week after diagnostic test"
    }
}

New_Property_Value

string

NA

NA

NA

An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

{
  "Campaign_Name": "Initial Seeding",
  "Events": [
    {
      "Event_Name": "Outbreak",
      "class": "CampaignEvent",
      "Nodeset_Config": {
        "class": "NodeSetAll"
      },
      "Start_Day": 1,
      "Event_Coordinator_Config": {
        "class": "StandardInterventionDistributionEventCoordinator",
        "Target_Demographic": "Everyone",
        "Demographic_Coverage": 1.0,
        "Intervention_Config": {
          "class": "DelayedIntervention",
          "Delay_Period_Distribution": "CONSTANT_DISTRIBUTION",
          "Delay_Period_Constant": 25,
          "Actual_IndividualIntervention_Configs": [
            {
              "Outbreak_Source": "PrevalenceIncrease",
              "class": "OutbreakIndividual"
            }
          ]
        }
      }
    },
    {
      "Event_Name": "Outbreak",
      "class": "CampaignEvent",
      "Nodeset_Config": {
        "class": "NodeSetAll"
      },
      "Start_Day": 50,
      "Event_Coordinator_Config": {
        "class": "StandardInterventionDistributionEventCoordinator",
        "Target_Demographic": "Everyone",
        "Demographic_Coverage": 1.0,
        "Intervention_Config": {
          "class": "DelayedIntervention",
          "Delay_Period_Distribution": "UNIFORM_DISTRIBUTION",
          "Delay_Period_Min": 15,
          "Delay_Period_Max": 30,
          "Actual_IndividualIntervention_Configs": [
            {
              "Outbreak_Source": "PrevalenceIncrease",
              "class": "OutbreakIndividual"
            }
          ]
        }
      }
    }
  ],
  "Use_Defaults": 1
}
HumanHostSeekingTrap

The HumanHostSeekingTrap intervention class applies a trap that attracts and kills host-seeking mosquitoes in the simulation. Human-host-seeking traps are individually-distributed interventions that have attraction and killing rates that decay in an analogous fashion to the blocking and killing rates of bednets.

An artificial diet diverts the vector from feeding on the human population, resulting in a two-fold benefit:

  1. The uninfected mosquitoes avoid biting infected humans some of the time, therefore decreasing the amount of human-to-vector transmission.

  2. Infectious vectors are diverted to feed on artificial diet instead of humans, therefore decreasing the amount of vector-to-human transmission.

At a glance:

  • Distributed to: Individuals

  • Serialized: Yes, if it has been distributed to a person.

  • Uses insecticides: No

  • Time-based expiration: No

  • Purge existing: Yes. If a new intervention is added to to the individual, the existing intervention of the same name is removed when the new one is added.

  • Vector killing contributes to: Indoor Die Before Feeding

  • Vector effects: Artificial Diet feed instead of Human or Animal Feed

  • Vector sexes affected: Females only

  • Vector life stage affected: Adult

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Attract_Config

JSON object

NA

NA

NA

The configuration of attraction efficacy and waning for human host-seeking trap. This determines what fraction of host-seeking vectors that day are attracted to the trap instead of finding a human to feed on. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Attract_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0.6,
        "class": "WaningEffectBox"
    }
}

Cost_To_Consumer

float

0

999999

3.75

The unit cost per trap (unamortized).

 
{
    "Cost_To_Consumer": 4.5
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Intervention_Name

string

NA

NA

HumanHostSeekingTrap

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "HumanHostSeekingTrap",
        "Intervention_Name": "Trap to lure and kill mosquitoes"
    }
}

Killing_Config

JSON object

NA

NA

NA

The configuration of killing efficacy and waning for human host-seeking trap. Only vectors attracted to the trap by the Attract_Config settings are targeted by the Killing_Config. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Killing_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0.9,
        "class": "WaningEffectBox"
    }
}

New_Property_Value

string

NA

NA

NA

An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

{
    "Events": [{
        "class": "CampaignEvent",
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 140,
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Target_Demographic": "Everyone",
            "Demographic_Coverage": 0.7,
            "Intervention_Config": {
                "class": "HumanHostSeekingTrap",
                "Cost_To_Consumer": 3.75,
                "Attract_Config": {
                    "Box_Duration": 3650,
                    "Initial_Effect": 0.6,
                    "class": "WaningEffectBox"
                },
                "Killing_Config": {
                    "Box_Duration": 3650,
                    "Initial_Effect": 0.9,
                    "class": "WaningEffectBox"
                }
            }
        }
    }],
    "Use_Defaults": 1
}
ImmunityBloodTest

The ImmunityBloodTest intervention class identifies whether an individual’s immunity meets a specified threshold (as set with the Positive_Threshold_AcquisitionImmunity campaign parameter) and then broadcasts an event based on the results; positive has immunity while negative does not. Note that Base_Sensitivity and Base_Specificity function whether or not the immunity is above the threshold.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Base_Sensitivity

float

0

1

1

The sensitivity of the diagnostic. This sets the proportion of the time that individuals with the condition being tested receive a positive diagnostic test. When set to zero, then individuals who have the condition always receive a false-negative diagnostic test.

 
{
    "Event_Coordinator_Config": {
        "Demographic_Coverage": 1,
        "Intervention_Config": {
            "Base_Sensitivity": 1,
            "Base_Specificity": 1,
            "Cost_To_Consumer": 0,
            "Days_To_Diagnosis": 0,
            "Positive_Diagnosis_Event": "TestedPositive_IamImmune",
            "Negative_Diagnosis_Event": "TestedNegative_IamSusceptible",
            "Treatment_Fraction": 1,
            "Positive_Threshold_AcquisitionImmunity": 0.99,
            "class": "ImmunityBloodTest"
        }
    }
}

Base_Specificity

float

0

1

1

The specificity of the diagnostic. This sets the proportion of the time that individuals without the condition being tested receive a negative diagnostic test. When set to 1, the diagnostic always accurately reflects the lack of having the condition. When set to zero, then individuals who do not have the condition always receive a false-positive diagnostic test.

 
{
    "Event_Coordinator_Config": {
        "Demographic_Coverage": 1,
        "Intervention_Config": {
            "Base_Sensitivity": 1,
            "Base_Specificity": 1,
            "Cost_To_Consumer": 0,
            "Days_To_Diagnosis": 0,
            "Negative_Diagnosis_Event": "TestedNegative_IamSusceptible",
            "Positive_Diagnosis_Event": "TestedPositive_IamImmune",
            "Positive_Threshold_AcquisitionImmunity": 0.99,
            "Treatment_Fraction": 1,
            "class": "ImmunityBloodTest"
        }
    }
}

Cost_To_Consumer

float

0

3.40282e+38

1

The unit ‘cost’ assigned to the diagnostic. Setting Cost_To_Consumer to zero for all other interventions, and to a non-zero amount for one intervention, provides a convenient way to track the number of times the intervention has been applied in a simulation.

 
{
    "Event_Coordinator_Config": {
        "Demographic_Coverage": 1,
        "Intervention_Config": {
            "Base_Sensitivity": 1,
            "Base_Specificity": 1,
            "Cost_To_Consumer": 0,
            "Days_To_Diagnosis": 0,
            "Negative_Diagnosis_Event": "TestedNegative_IamSusceptible",
            "Positive_Diagnosis_Event": "TestedPositive_IamImmune",
            "Positive_Threshold_AcquisitionImmunity": 0.99,
            "Treatment_Fraction": 1,
            "class": "ImmunityBloodTest"
        }
    }
}

Days_To_Diagnosis

float

0

3.40282e+38

0

The number of days from test until diagnosis.

 
{
    "Event_Coordinator_Config": {
        "Demographic_Coverage": 1,
        "Intervention_Config": {
            "Base_Sensitivity": 1,
            "Base_Specificity": 1,
            "Cost_To_Consumer": 0,
            "Days_To_Diagnosis": 0,
            "Negative_Diagnosis_Event": "TestedNegative_IamSusceptible",
            "Positive_Diagnosis_Event": "TestedPositive_IamImmune",
            "Positive_Threshold_AcquisitionImmunity": 0.99,
            "Treatment_Fraction": 1,
            "class": "ImmunityBloodTest"
        }
    }
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Event_Coordinator_Config": {
        "Demographic_Coverage": 1,
        "Intervention_Config": {
            "Base_Sensitivity": 1,
            "Base_Specificity": 1,
            "Cost_To_Consumer": 0,
            "Days_To_Diagnosis": 0,
            "Disqualifying_Properties": [
                "InterventionStatus:LostForever"
            ],
            "Negative_Diagnosis_Event": "TestedNegative_IamSusceptible",
            "Positive_Diagnosis_Event": "TestedPositive_IamImmune",
            "Positive_Threshold_AcquisitionImmunity": 0.99,
            "Treatment_Fraction": 1,
            "class": "ImmunityBloodTest"
        }
    }
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Event_Coordinator_Config": {
        "Demographic_Coverage": 1,
        "Intervention_Config": {
            "Base_Sensitivity": 1,
            "Base_Specificity": 1,
            "Cost_To_Consumer": 0,
            "Days_To_Diagnosis": 0,
            "Dont_Allow_Duplicates": 0,
            "Negative_Diagnosis_Event": "TestedNegative_IamSusceptible",
            "Positive_Diagnosis_Event": "TestedPositive_IamImmune",
            "Positive_Threshold_AcquisitionImmunity": 0.99,
            "Treatment_Fraction": 1,
            "class": "ImmunityBloodTest"
        }
    }
}

Enable_IsSymptomatic

boolean

NA

NA

0

If true (1), requires an infection to be symptomatic to return a positive test.

 
{
    "Enable_Is_Symptomatic": 1,
    "Base_Specificity": 0.85,
    "Base_Sensitivity": 0.92
}

Intervention_Name

string

NA

NA

ImmunityBloodTest

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Event_Coordinator_Config": {
        "Demographic_Coverage": 1,
        "Intervention_Config": {
            "Base_Sensitivity": 1,
            "Base_Specificity": 1,
            "Cost_To_Consumer": 0,
            "Days_To_Diagnosis": 0,
            "Intervention_Name": "Immunity Blood Test - Series 1",
            "Negative_Diagnosis_Event": "TestedNegative_IamSusceptible",
            "Positive_Diagnosis_Event": "TestedPositive_IamImmune",
            "Positive_Threshold_AcquisitionImmunity": 0.99,
            "Treatment_Fraction": 1,
            "class": "ImmunityBloodTest"
        }
    }
}

Negative_Diagnosis_Event

enum

NA

NA

“”

If an individual tests negative (does not have immunity), then an individual type event is broadcast; custom individual events can be defined in the config parameter Custom_Individual_Events. This may trigger another intervention when the event occurs.

 
{
    "Event_Coordinator_Config": {
        "Demographic_Coverage": 1,
        "Intervention_Config": {
            "Base_Sensitivity": 1,
            "Base_Specificity": 1,
            "Cost_To_Consumer": 0,
            "Days_To_Diagnosis": 0,
            "Negative_Diagnosis_Event": "TestedNegative_IamSusceptible",
            "Positive_Diagnosis_Event": "TestedPositive_IamImmune",
            "Positive_Threshold_AcquisitionImmunity": 0.99,
            "Treatment_Fraction": 1,
            "class": "ImmunityBloodTest"
        }
    }
}

New_Property_Value

string

NA

NA

NA

An optional IndividualProperty key:value pair that will be assigned when the intervention is first updated. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Positive_Diagnosis_Event

enum

NA

NA

“”

If the test is positive (has immunity), then an individual type event is broadcast; custom individual events can be defined in the config parameter Custom_Individual_Events. This may trigger another intervention when the event occurs.

 
{
    "Event_Coordinator_Config": {
        "Demographic_Coverage": 1,
        "Intervention_Config": {
            "Base_Sensitivity": 1,
            "Base_Specificity": 1,
            "Cost_To_Consumer": 0,
            "Days_To_Diagnosis": 0,
            "Negative_Diagnosis_Event": "TestedNegative_IamSusceptible",
            "Positive_Diagnosis_Event": "TestedPositive_IamImmune",
            "Positive_Threshold_AcquisitionImmunity": 0.99,
            "Treatment_Fraction": 1,
            "class": "ImmunityBloodTest"
        }
    }
}

Positive_Threshold_AcquisitionImmunity

float

0

1

1

Specifies the threshold for acquired immunity, where 1 equals 100% immunity and 0 equals 100% susceptible.

 
{
    "Event_Coordinator_Config": {
        "Demographic_Coverage": 1,
        "Intervention_Config": {
            "Base_Sensitivity": 1,
            "Base_Specificity": 1,
            "Cost_To_Consumer": 0,
            "Days_To_Diagnosis": 0,
            "Negative_Diagnosis_Event": "TestedNegative_IamSusceptible",
            "Positive_Diagnosis_Event": "TestedPositive_IamImmune",
            "Positive_Threshold_AcquisitionImmunity": 0.99,
            "Treatment_Fraction": 1,
            "class": "ImmunityBloodTest"
        }
    }
}

Treatment_Fraction

float

0

1

1

The fraction of positive diagnoses that are treated.

 
{
    "Event_Coordinator_Config": {
        "Demographic_Coverage": 1,
        "Intervention_Config": {
            "Base_Sensitivity": 1,
            "Base_Specificity": 1,
            "Cost_To_Consumer": 0,
            "Days_To_Diagnosis": 0,
            "Negative_Diagnosis_Event": "TestedNegative_IamSusceptible",
            "Positive_Diagnosis_Event": "TestedPositive_IamImmune",
            "Positive_Threshold_AcquisitionImmunity": 0.99,
            "Treatment_Fraction": 1,
            "class": "ImmunityBloodTest"
        }
    }
}

{
    "Events":[{
            "class":"CampaignEvent",
            "Start_Day":14,
            "Nodeset_Config":{
                "class":"NodeSetAll"
            },
            "Event_Coordinator_Config":{
                "class":"StandardInterventionDistributionEventCoordinator",
                "Target_Demographic":"Everyone",
                "Demographic_Coverage": 1.0,
                "Intervention_Config": {
                    "Base_Sensitivity": 1.0,
                    "Base_Specificity": 1.0,
                    "Cost_To_Consumer": 0,
                    "Days_To_Diagnosis": 0.0,
                    "Positive_Diagnosis_Event": "TestedPositive_IamImmune",
                    "Negative_Diagnosis_Event": "TestedNegative_IamSusceptible",
                    "Treatment_Fraction": 1.0,
                    "Positive_Threshold_AcquisitionImmunity": 0.99,
                    "class": "ImmunityBloodTest"
                }
            }
        }],
    "Use_Defaults":1
}
IndividualImmunityChanger

The IndividualImmunityChanger intervention class acts essentially as a MultiEffectVaccine, with the exception of how the behavior is implemented. Rather than attaching a persistent vaccine intervention object to an individual’s intervention list (as a campaign-individual-multieffectboostervaccine does), the IndividualImmunityChanger directly alters the immune modifiers of the individual’s susceptibility object and is then immediately disposed of. Any immune waning is not governed by Waning effect classes, as MultiEffectVaccine is, but rather by the immunity waning parameters in the configuration file.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Boost_Acquire

float

0

1

0

Specifies the boosting effect on acquisition immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. This does not replace current immunity, it builds multiplicatively on top of it.

 
{
    "Boost_Acquire": 0.7
}

Boost_Mortality

float

0

1

0

Specifies the boosting effect on mortality immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. This does not replace current immunity, it builds multiplicatively on top of it.

 
{
    "Boost_Mortality": 1.0
}

Boost_Threshold_Acquire

float

0

1

0

Specifies how much acquisition immunity is required before the vaccine changes from a prime to a boost.

 
{
    "Boost_Threshold_Acquire": 0.0
}

Boost_Threshold_Mortality

float

0

1

0

Specifies how much mortality immunity is required before the vaccine changes from a prime to a boost.

 
{
    "Boost_Threshold_Mortality": 0.0
}

Boost_Threshold_Transmit

float

0

1

0

Specifies how much transmission immunity is required before the vaccine changes from a prime to a boost.

 
{
    "Boost_Threshold_Transmit": 0.0
}

Boost_Transmit

float

0

1

0

Specifies the boosting effect on transmission immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. This does not replace current immunity, it builds multiplicatively on top of it.

 
{
    "Boost_Transmit": 0.5
}

Cost_To_Consumer

float

0

999999

1

The unit cost per vaccine (unamortized).

 
{
    "Cost_To_Consumer": 10.0
}

Prime_Acquire

float

0

1

0

Specifies the priming effect on acquisition immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine.

 
{
    "Prime_Acquire": 0.1
}

Prime_Mortality

float

0

1

0

Specifies the priming effect on mortality immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine.

 
{
    "Prime_Mortality": 0.3
}

Prime_Transmit

float

0

1

0

Specifies the priming effect on transmission immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine.

 
{
    "Prime_Transmit": 0.2
}

{
    "Use_Defaults": 1,
    "Campaign_Name": "Generic Seattle Regression Campaign",
    "Events": [{
        "Start_Day": 10,
        "class": "CampaignEvent",
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Target_Demographic": "Everyone",
            "Demographic_Coverage": 1.0,
            "Intervention_Config": {
                "class": "IndividualImmunityChanger",
                "Cost_To_Consumer": 10.0,
                "Prime_Acquire": 0.1,
                "Prime_Transmit": 0.2,
                "Prime_Mortality": 0.3,
                "Boost_Acquire": 0.7,
                "Boost_Transmit": 0.7,
                "Boost_Mortality": 1.0,
                "Boost_Threshold_Acquire": 0.2,
                "Boost_Threshold_Transmit": 0.1,
                "Boost_Threshold_Mortality": 0.1
            }
        }
    }]
}
IRSHousingModification

The IRSHousingModification intervention class includes Indoor Residual Spraying (IRS) in the simulation. IRS is another key vector control tool in which insecticide is sprayed on the interior walls of a house so that mosquitoes resting on the walls after consuming a blood meal will die. IRS can also have a repellent effect. Because this class is distributed to individuals, it can target subgroups of the population. To target all individuals in a node, use IndoorSpaceSpraying. Do not use IRSHousingModification and IndoorSpaceSpraying together.

At a glance:

  • Distributed to: Individuals

  • Serialized: Yes, if it has been distributed to a person.

  • Uses insecticides: Yes. It can target specific species or other subgroups.

  • Time-based expiration: No

  • Purge existing: Yes. If a new intervention is added to to the individual, the existing intervention of the same name is removed when the new one is added.

  • Vector killing contributes to: Indoor Die After Feeding

  • Vector effects: Repelling and killing

  • Vector sexes affected: Females only

  • Vector life stage affected: Adult

Warning

EMOD simulations models nodes and individuals within nodes; they do not model houses. Therefore, housing modifications are received by individuals, not houses.

Use of this class and other housing modification classes requires caution because they can have unintended effects. For example, individuals in the same household may receive different housing modification interventions. An individual receiving a housing modification intervention who then migrates to another node will continue to receive that intervention. We recommend that you configure your simulation to take these logical inconsistencies into account.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Cost_To_Consumer

float

0

999999

8

The unit cost per housing modification (unamortized).

 
{
    "Cost_To_Consumer": 10.0
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to nodes with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Insecticide_Name

string

NA

NA

UNINITIALIZED STRING

The name of the insecticide defined in <config.Insecticides> for this intervention. If insecticides are being used, this must be defined as one of those values; if they are not being used this can be empty. It cannot have a value if you did not configure <config.Insecticides>.

 
{
    "Insecticide_Name": "carbamate"
}

Intervention_Name

string

NA

NA

IRSHousingModification

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "IRSHousingModification",
        "Intervention_Name": "Indoor DDT spraying"
    }
}

Killing_Config

JSON object

NA

NA

NA

The configuration of killing efficacy and waning for housing modification. Killing is conditional on the vector not being repelled before feeding. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Killing_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0.53429,
        "class": "WaningEffectBox"
    }
}

New_Property_Value

string

NA

NA

NA

An optional IndividualProperty key:value pair that will be assigned when the intervention is first updated. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Repelling_Config

JSON object

NA

NA

NA

Defines a waning effect that determines the probability that a vector is repelled due to the intervention. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Repelling_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0.0,
        "class": "WaningEffectBox"
    }
}

{
    "Events": [
        {
            "class": "CampaignEvent",
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 540,
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Target_Demographic": "Everyone",
                "Demographic_Coverage": 0.8,
                "Intervention_Config": {
                    "class": "IRSHousingModification",
                    "Repelling_Config": {
                        "Box_Duration": 3650,
                        "Initial_Effect": 0,
                        "class": "WaningEffectBox"
                    },
                    "Cost_To_Consumer": 8,
                    "Killing_Config": {
                        "Box_Duration": 3650,
                        "Initial_Effect": 0.5,
                        "class": "WaningEffectBox"
                    }
                }
            }
        }
    ],
    "Use_Defaults": 1
}
IVCalendar

The IVCalendar intervention class contains a list of ages when an individual will receive the actual intervention. In IVCalendar, there is a list of actual interventions where the distribution is dependent on whether the individual’s age matches the next date in the calendar. This implies that at a certain age, the list of actual interventions will be distributed according to a given probability. While a typical use case might involve the distribution of calendars by a BirthTriggeredIV in the context of a routine vaccination schedule, calendars may also be distributed directly to individuals.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Actual_IndividualIntervention_Configs

array of JSON objects

NA

NA

NA

An array of interventions that will be distributed as specified in the calendar. This parameter selects a class for the intervention and configures the parameters specific for that intervention class.

 
{
    "Actual_IndividualIntervention_Config": {
        "class": "IVCalendar",
        "Actual_IndividualIntervention_Configs": [
            {
                "class": "BCGVaccine",
                "Cost_To_Consumer": 10,
                "Vaccine_Take": 1,
                "Vaccine_Take_Age_Decay_Rate": 0,
                "Waning_Config": {
                    "Initial_Effect": 1,
                    "Box_Duration": 3650,
                    "Decay_Rate_Factor": 3650,
                    "class": "WaningEffectBoxExponential"
                }
            }
        ]
    }
}

Calendar

array of JSON objects

NA

NA

[ ]

An array of JSON objects where each object specifies the age and probability of receiving the interventions. The parameters of the Calendar objects are: * Age: The age (in days) that the individual must be in order to receive the list of actual interventions. * Probability: The probability of an individual receiving the list of actual interventions at the corresponding age.

 
{
    "Calendar": [
        {
            "Age": 60,
            "Probability": 1
        },
        {
            "Age": 120,
            "Probability": 1
        },
        {
            "Age": 180,
            "Probability": 1
        },
        {
            "Age": 510,
            "Probability": 0.9
        },
        {
            "Age": 1825,
            "Probability": 0.95
        },
        {
            "Age": 4200,
            "Probability": 0.95
        }
    ]
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Dropout

boolean

NA

NA

0

If set to true (1), when an intervention distribution is missed, all subsequent interventions are also missed. If set to false (0), all calendar dates/doses are applied independently of each other.

 
{
    "Actual_IndividualIntervention_Config": {
        "class": "IVCalendar",
        "Dont_Allow_Duplicates": 0,
        "Dropout": 0,
        "Calendar": [
            {
                "Age": 12045,
                "Probability": 1
            }
        ]
    }
}

Intervention_Name

string

NA

NA

IVCalendar

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "IVCalendar",
        "Intervention_Name": "Routine measles vaccination schedule"
    }
}

New_Property_Value

string

NA

NA

NA

An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

{
    "Use_Defaults": 1,
    "Campaign_Name": "BCG vaccination calendar distributed at birth",
    "Events": [{
        "Event_Name": "BCG vaccinations scheduled at birth",
        "class": "CampaignEvent",
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 1825,
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Target_Demographic": "Everyone",
            "Intervention_Config": {
                "class": "BirthTriggeredIV",
                "Demographic_Coverage": 0.9,
                "Actual_IndividualIntervention_Config": {
                    "class": "IVCalendar",
                    "Calendar": [{
                            "Age": 30,
                            "Probability": 1
                        },
                        {
                            "Age": 3650,
                            "Probability": 1
                        }
                    ],
                    "Dropout": 0,
                    "Actual_IndividualIntervention_Configs": [{
                        "class": "BCGVaccine",
                        "Cost_To_Consumer": 8,
                        "Vaccine_Take": 0.8,
                        "Vaccine_Take_Age_Decay_Rate": 0.2,
                        "Waning_Config": {
                            "Initial_Rate": 0.9,
                            "Decay_Time_Constant": 3650,
                            "class": "WaningEffectExponential"
                        }
                    }]
                }
            }
        }
    }]
}
Ivermectin

The Ivermectin intervention class modifies the feeding outcome probabilities for both indoor- and outdoor-feeding mosquitoes. Ivermectin works by increasing the mortality of mosquitoes after they blood-feed on a human. It is an individually-distributed intervention that configures the waning of the drug’s killing effect on the adult mosquito population. This intervention enables exploration of the impact of giving humans an insecticidal drug, and how the effectiveness and duration of the drug’s killing-effect interacts with other interventions. For example, you can look at the impact of controlling and eliminating malaria transmission using both anti-parasite drugs that clear existing infections and insecticidal drugs.

At a glance:

  • Distributed to: Individuals

  • Serialized: Yes, if it has been distributed to a person.

  • Uses insecticides: Yes. It can target specific species or other subgroups.

  • Time-based expiration: No, but it will expire if the efficacy is below 0.00001.

  • Purge existing: Yes. If a new intervention is added to to the individual, the existing intervention of the same name is removed when the new one is added.

  • Vector killing contributes to: Indoor/Outdoor Die After Feeding

  • Vector effects: Killing

  • Vector sexes affected: Females only

  • Vector life stage affected: Adult

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Cost_To_Consumer

float

0

999999

8

The unit cost per Ivermectin dosing (unamortized).

 
{
    "Cost_To_Consumer": 6.5
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Insecticide_Name

string

NA

NA

UNINITIALIZED STRING

The name of the insecticide defined in the configuration parameter Insecticides for this intervention. If insecticides are being used, this must be defined as one of those values; if they are not being used this can be empty. It cannot have a value if you did not configure the configuration parameter Insecticides.

 
{
    "Insecticide_Name": "carbamate"
}

Intervention_Name

string

NA

NA

Ivermectin

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "Ivermectin",
        "Intervention_Name": "First distribution of ivermectin"
    }
}

Killing_Config

JSON object

NA

NA

NA

The configuration of Ivermectin killing efficacy and waning over time. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Killing_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0.53429,
        "class": "WaningEffectBox"
    }
}

New_Property_Value

string

NA

NA

UNINITIALIZED STRING

An optional IndividualProperty key:value pair that will be assigned when the intervention is first updated. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

{
    "Events": [
        {
            "class": "CampaignEvent",
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 120,
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Number_Repetitions": 5,
                "Target_Demographic": "Everyone",
                "Timesteps_Between_Repetitions": 3,
                "Demographic_Coverage": 0.8,
                "Intervention_Config": {
                    "class": "Ivermectin",
                    "Cost_To_Consumer": 1,
                    "Killing_Config": {
                        "Box_Duration": 3,
                        "Initial_Effect": 1,
                        "class": "WaningEffectBox"
                    }
                }
            }
        }
    ],
    "Use_Defaults": 1
}
MalariaDiagnostic

The MalariaDiagnostic intervention class is similar to SimpleDiagnostic, but distributes a test for malaria. There are several types of configurable diagnostic tests, and the type selected determines the other parameters used.

You should note that the results of MalariaDiagnostic can be different than what you see in the reports. The intervention takes an independent measurement from the reports. It has its own parameters that control the sensitivity and detection threshold.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Cost_To_Consumer

float

0

3.40282E+38

1

The unit cost assigned to the diagnostic. Setting Cost_To_Consumer to zero for all other interventions, and to a non-zero amount for one intervention, provides a convenient way to track the number of times the intervention has been applied in a simulation.

 
{
    "Cost_To_Consumer": 0.333
}

Days_To_Diagnosis

float

0

3.40282E+38

0

The number of days from test until positive diagnosis. A negative test result is given immediately when the intervention is distributed.

 
{
    "Days_To_Diagnosis": 0.0
}

Detection_Threshold

float

0

1.00E+06

100

The diagnostic detection threshold above which a positive diagnosis will be made. The measurement used depends on the value set in Diagnostic_Type.

 
{
    "Diagnostic_Type": "BLOOD_SMEAR_PARASITES",
    "Detection_Threshold": 40
}

Diagnostic_Type

enum

NA

NA

BLOOD_SMEAR_PARASITES

The type of malaria diagnostic test used. Possible values are:

BLOOD_SMEAR_PARASITES

A sample of blood is viewed under a microscope and the number of parasites per microliter of blood is counted. This diagnostic includes some randomness because real-world smear tests may not perfectly measure parasite levels in a person’s system. Detection_Threshold is the number of parasites per microliter of blood. This setting uses the Measurement_Sensitivity parameter and if measurement is larger than Detection_Threshold in the following equation, a positive diagnosis is made:

measurement = ( 1 / Measurement_Sensitivity ) * Poisson (x) where x = Measurement_Sensitivity * true_parasite_density

BLOOD_SMEAR_GAMETOCYTES

A sample of blood is viewed under a microscope and the number of gametocytes per microliter of blood is counted. This diagnostic includes some randomness because real-world smear tests may not perfectly measure gametocyte levels in a person’s system. Detection_Threshold is the number of gametocytes per microliter of blood. This setting uses the Measurement_Sensitivity parameter and if measurement is larger than Detection_Threshold in the following equation, a positive diagnosis is made:

measurement = ( 1 / Measurement_Sensitivity ) * Poisson (x) where x = Measurement_Sensitivity * true_gametocyte_density

FEVER

The fever detection threshold is defined as the number of degrees Celsius above a normal body temperature of 37 degrees. If a person’s body temperature is above this threshold, a positive diagnosis is made. For example, a Detection_Threshold of 2 would result in a positive diagnosis for temperatures above 39 degrees. Fever as a diagnostic uses a model of cytokines based on the paper Innate immunity to malaria.

PCR_PARASITES

The intervention models a quantitative nucleic acid sequence-based amplification (QT-NASBA) type measurement of parasite density and draws a value from a random Gaussian distribution. Detection_Threshold is the number of parasites per microliter of blood. This measurement is based on the paper Improving statistical inference on pathogen densities estimated by quantitative molecular methods: malaria gametocytaemia as a case study.

PCR_GAMETOCYTES

The intervention models a quantitative nucleic acid sequence-based amplification (QT-NASBA) type measurement of gametocyte density and draws a value from a random Gaussian distribution. Detection_Threshold is the number of gametocytes per microliter of blood. This measurement is based on the paper Improving statistical inference on pathogen densities estimated by quantitative molecular methods: malaria gametocytaemia as a case study.

PF_HRP2

Measures the number of picograms of histidine-rick protein 2 (HRP2) in a microliter of blood. Detection_Threshold is in picograms of HRP2 per microliter of blood. This is modeled with boost and decay parameters and is based on the paper Modelling the dynamics of Plasmodium falciparum histidine-rich protein 2 in human malaria to better understand malaria rapid diagnostic test performance.

TRUE_PARASITE_DENSITY

A true measure of the number of parasites per microliter of blood. Detection_Threshold is in parasites per microliter of blood.

 
{
    "Diagnostic_Type": "BLOOD_SMEAR_PARASITES",
    "Detection_Threshold": 40,
    "Measurement_Sensitivity": 5
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Event_Or_Config

enum

NA

NA

Config

Specifies whether the current intervention (or a positive diagnosis, depending on the intervention class) distributes a nested intervention (the Config option) or an event will be broadcast which may trigger other interventions in the campaign file (the Event option). Possible values are:

  • Event

  • Config

 
{
    "Event_Or_Config": "Config"
}

Intervention_Name

string

NA

NA

MalariaDiagnostic

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "MalariaDiagnostic",
        "Intervention_Name": "First round of malaria testing"
    }
}

Measurement_Sensitivity

float

0

1000000.0

0.1

The number of microliters of blood tested to find a single parasite or gametocyte in a traditional smear (corresponds to the inverse of parasites/microliters sensitivity). This is similar to Report_Parasite_Smear_Sensitivity and Report_Gametocyte_Smear_Sensitivity in the configuration file used for reports, but this is for this instance of the diagnostic. This parameter is used when Diagnostic_Type is set to BLOOD_SMEAR_PARASITES or BLOOD_SMEAR_GAMETOCYTES.

 
{
    "Intervention_Config": {
        "class": "MalariaDiagnostic",
        "Diagnostic_Type": "BLOOD_SMEAR_GAMETOCYTES",
        "Detection_Threshold": 10,
        "Measurement_Sensitivity": 0.25
    }
}

Negative_Diagnosis_Config

JSON object

NA

NA

NA

If Event_Or_Config is set to Config, this defines a new intervention to be given to the person upon a negative test result.

 
{
    "Event_Or_Config": "Config",
    "Negative_Diagnosis_Config": {
        "class": "SimpleVaccine",
        "Cost_To_Consumer": 1,
        "Vaccine_Take": 1,
        "Vaccine_Type": "AcquisitionBlocking",
        "Waning_Config": {
            "class": "WaningEffectBox",
            "Box_Duration": 40,
            "Initial_Effect": 0.75
        }
    }
}

Negative_Diagnosis_Event

string

NA

NA

NA

If Event_Or_Config is set to Event, this defines the event that is broadcast upon a negative test result. See Event list for possible values or define custom events by adding to the list for the configuration parameter Custom_Individual_Event.

 
{
    "Event_Or_Config": "Event",
    "Negative_Diagnosis_Event": "TestedNegative_IamSusceptible"
}

New_Property_Value

string

NA

NA

NA

An optional IndividualProperty key:value pair that will be assigned when the intervention is first updated. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Positive_Diagnosis_Config

JSON object

NA

NA

NA

The intervention distributed to individuals if they test positive. Only used when Event_Or_Config is set to Config.

 
{
    "Event_Or_Config": "Config",
    "Positive_Diagnosis_Config": {
        "class": "SimpleVaccine",
        "Cost_To_Consumer": 0.333,
        "Secondary_Decay_Time_Constant": 1,
        "Vaccine_Take": 1,
        "Vaccine_Type": "AcquisitionBlocking",
        "Waning_Config": {
            "class": "WaningEffectBox",
            "Box_Duration": 3650,
            "Initial_Effect": 0.1
        }
    }
}

Positive_Diagnosis_Event

enum

NA

NA

NA

If the test is positive, this specifies an event that can trigger another intervention when the event occurs. Only used if Event_Or_Config is set to Event. See Event list for possible values or define custom events by adding to the list for the configuration parameter Custom_Individual_Event.

 
{
    "Intervention_Config": {
        "class": "SimpleDiagnostic",
        "Base_Sensitivity": 1.0,
        "Base_Specificity": 1.0,
        "Cost_To_Consumer": 0.0,
        "Days_To_Diagnosis": 0.0,
        "Event_Or_Config": "Event",
        "Positive_Diagnosis_Event": "TestedPositive_CureMeNow",
        "Treatment_Fraction": 1.0
    }
}

Treatment_Fraction

float

0

1

1

The fraction of positive diagnoses that are treated or given the Positive_Diagnosis_Config or Positive_Diagnosis_Event when used in campaign classes that trigger separate interventions or events upon a positive diagnosis.

 
{
    "Intervention_Config": {
        "class": "SimpleDiagnostic",
        "Base_Sensitivity": 1.0,
        "Base_Specificity": 1.0,
        "Cost_To_Consumer": 0.0,
        "Days_To_Diagnosis": 0.0,
        "Event_Or_Config": "Event",
        "Positive_Diagnosis_Event": "TestedPositive_CureMeNow",
        "Treatment_Fraction": 1.0
    }
}

{
    "Events": [{
        "class": "CampaignEvent",
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 18050,
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Target_Demographic": "Everyone",
            "Demographic_Coverage": 0.5,
            "Intervention_Config": {
                "class": "NodeLevelHealthTriggeredIV",
                "Trigger_Condition": "TriggerString",
                "Trigger_Condition_String": "Diagnostic_Survey_2",
                "Actual_IndividualIntervention_Config": {
                    "class": "MalariaDiagnostic",
                    "Cost_To_Consumer": 0,
                    "Diagnostic_Type": "BLOOD_SMEAR_PARASITES",
                    "Measurement_Sensitivity": 0.1,
                    "Detection_Threshold": 3,
                    "Event_Or_Config": "Config",
                    "Positive_Diagnosis_Config": {
                        "class": "AntimalarialDrug",
                        "Cost_To_Consumer": 5,
                        "Drug_Type": "Chloroquine"
                    },
                    "Negative_Diagnosis_Config": {
                        "class": "SimpleVaccine",
                        "Cost_To_Consumer": 10,
                        "Vaccine_Take": 1,
                        "Vaccine_Type": "AcquisitionBlocking",
                        "Waning_Config": {
                            "class": "WaningEffectBox",
                            "Box_Duration": 3650,
                            "Initial_Effect": 1
                        }
                    }
                }
            }
        }
    }],
    "Use_Defaults": 1
}
MigrateIndividuals

The MigrateIndividuals intervention class is an individual-level intervention used to force migration and is separate from the normal migration system. However, it does require that human migration is enabled by setting the configuration parameters Migration_Model to FIXED_RATE_MIGRATION and Migration_Pattern to SINGLE_ROUND_TRIP.

As individuals migrate, there are three ways to categorize nodes:

  • Home: the node where the individuals reside; each individual has a single home node.

  • Origin: the “starting point” node for each leg of the migration. The origin updates as individuals move between nodes.

  • Destination: the node the individual is traveling to. The destination updates as individuals move between nodes.

For example, Individual 1 has a home node of Node A. They migrate from Node A to Node B. Node A is both the home node and the origin node, and Node B is the destination node. If Individual 1 migrates from Node B to Node C, Node A remains the home node, but now Node B is the origin node, and Node C is the destination node. If Individual 1 migrates from Node C back to Node A, Node C is the origin node, and Node A becomes the destination node and still remains the home node.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Duration_At_Node_Constant

float

0

3.40282E+38

-1

The duration at the destination node to use for all individuals, in days, when Duration_At_Node_Distribution is set to CONSTANT_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "CONSTANT_DISTRIBUTION",
    "Duration_At_Node_Constant": 8
}

Duration_At_Node_Distribution

enum

NA

NA

NOT_INITIALIZED

The distribution type to use for assigning the duration of time an individual or family spends at a destination node after intervention-based migration. Each assigned value is a random draw from the distribution.

Possible values are:

NOT_INITIALIZED

No distribution set.

CONSTANT_DISTRIBUTION

Use the same value for each individual. Set Duration_At_Node_Constant.

UNIFORM_DISTRIBUTION

Use a uniform distribution with a given minimum and maximum. Set Duration_At_Node_Max and Duration_At_Node_Min.

GAUSSIAN_DISTRIBUTION

The distribution is Gaussian (or normal). Values are resampled to ensure >= 0. Set Duration_At_Node_Gaussian_Mean and Duration_At_Node_Gaussian_Std_Dev.

EXPONENTIAL_DISTRIBUTION

The distribution is exponential with a given mean. Set Duration_At_Node_Exponential.

WEIBULL_DISTRIBUTION

Use a Weibull distribution with a given shape and scale. Set Duration_At_Node_Kappa and Duration_At_Node_Lambda.

LOG_NORMAL_DISTRIBUTION

Use a log-normal distribution with a given mean and standard deviation of the natural log. Set Duration_At_Node_Log_Normal_Mu and Duration_At_Node_Log_Normal_Sigma.

POISSON_DISTRIBUTION

Use a Poisson distribution with a given mean. Set Duration_At_Node_Poisson_Mean.

DUAL_CONSTANT_DISTRIBUTION

Use a distribution where some individuals are set to a value of zero and the rest to a given value. Set Duration_At_Node_Proportion_0 and Peak_2_Value. This distribution does not use the parameters set for CONSTANT_DISTRIBUTION.

DUAL_EXPONENTIAL_DISTRIBUTION

Use two exponential distributions with given means. Set Duration_At_Node_Mean_1, Duration_At_Node_Mean_2, and Duration_At_Node_Proportion_1. This distribution does not use the parameters set for EXPONENTIAL_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "WEIBULL_DISTRIBUTION",
    "Duration_At_Node_Kappa": 0.9,
    "Duration_At_Node_Lambda": 1.5
}

Duration_At_Node_Exponential

float

0

3.40282E+38

-1

The mean of the duration at the destination node after migration, in days, when Duration_At_Node_Distribution is set to EXPONENTIAL_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "EXPONENTIAL_DISTRIBUTION",
    "Duration_At_Node_Exponential": 4.25
}

Duration_At_Node_Gaussian_Mean

float

0

3.40282E+38

-1

The mean of the duration at the destination node after migration, in days, when Duration_At_Node_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Duration_At_Node_Gaussian_Mean": 8,
    "Duration_At_Node_Gaussian_Std_Dev": 1.5
}

Duration_At_Node_Gaussian_Std_Dev

float

1.17549E-38

3.40282E+38

-1

The standard deviation of the duration at the destination node after migration, in days, when Duration_At_Node_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Duration_At_Node_Gaussian_Mean": 8,
    "Duration_At_Node_Gaussian_Std_Dev": 1.5
}

Duration_At_Node_Kappa

float

1.17549E-38

3.40282E+38

-1

The shape value for the duration at the destination node after migration, in days, when Duration_At_Node_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "WEIBULL_DISTRIBUTION",
    "Duration_At_Node_Kappa": 0.9,
    "Duration_At_Node_Lambda": 1.5
}

Duration_At_Node_Lambda

float

1.17549E-38

3.40282E+38

-1

The scale value for the duration at the destination node after migration, in days, when Duration_At_Node_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "WEIBULL_DISTRIBUTION",
    "Duration_At_Node_Kappa": 0.9,
    "Duration_At_Node_Lambda": 1.5
}

Duration_At_Node_Log_Normal_Mu

float

-3.40282e+38

1.70141e+38

3.40282E+38

The mean of the natural log of the duration at the destination node after migration, in days, when Duration_At_Node_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Duration_At_Node_Log_Normal_Mu": 9,
    "Duration_At_Node_Log_Normal_Sigma": 2
}

Duration_At_Node_Log_Normal_Sigma

float

-3.40282e+38

1.70141e+38

3.40282E+38

The standard deviation of the natural log of the duration at the destination node after migration, in days, when Duration_At_Node_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Duration_At_Node_Log_Normal_Mu": 9,
    "Duration_At_Node_Log_Normal_Sigma": 2
}

Duration_At_Node_Max

float

0

3.40282E+38

-1

The maximum duration at the destination node after migration, in days, when Duration_At_Node_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "UNIFORM_DISTRIBUTION",
    "Duration_At_Node_Min": 2,
    "Duration_At_Node_Max": 7
}

Duration_At_Node_Mean_1

float

1.17549E-38

3.40282E+38

-1

The mean of the first exponential distribution, in days, when Duration_At_Node_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Duration_At_Node_Mean_1": 4,
    "Duration_At_Node_Mean_2": 12,
    "Duration_At_Node_Proportion_1": 0.2
}

Duration_At_Node_Mean_2

float

1.17549E-38

3.40282E+38

-1

The mean of the second exponential distribution, in days, when Duration_At_Node_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Duration_At_Node_Mean_1": 4,
    "Duration_At_Node_Mean_2": 12,
    "Duration_At_Node_Proportion_1": 0.2
}

Duration_At_Node_Min

float

0

3.40282E+38

-1

The minimum duration at the destination node after migration, in days, when Duration_At_Node_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "UNIFORM_DISTRIBUTION",
    "Duration_At_Node_Min": 2,
    "Duration_At_Node_Max": 7
}

Duration_At_Node_Peak_2_Value

float

0

3.40282E+38

-1

The duration at the destination node value, in days, to assign to the remaining individuals when Duration_At_Node_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Duration_At_Node_Proportion_0": 0.25,
    "Duration_At_Node_Peak_2_Value": 5
}

Duration_At_Node_Poisson_Mean

float

0

3.40282E+38

-1

The mean of the duration at the destination node after migration, in days, when Duration_At_Node_Distribution is set to POISSON_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "POISSON_DISTRIBUTION",
    "Duration_At_Node_Poisson_Mean": 5
}

Duration_At_Node_Proportion_0

float

0

1

-1

The proportion of individuals to assign a value of zero days at the destination node when Duration_At_Node_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Duration_At_Node_Proportion_0": 0.25,
    "Duration_At_Node_Peak_2_Value": 5
}

Duration_At_Node_Proportion_1

float

0

1

-1

The proportion of individuals in the first exponential distribution when Duration_At_Node_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Duration_At_Node_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Duration_At_Node_Mean_1": 4,
    "Duration_At_Node_Mean_2": 12,
    "Duration_At_Node_Proportion_1": 0.2
}

Duration_Before_Leaving_Constant

float

0

3.40282E+38

-1

The duration before leaving the origin node to use for every individual, in days, when Duration_Before_Leaving_Distribution is set to CONSTANT_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "CONSTANT_DISTRIBUTION",
    "Duration_Before_Leaving_Constant": 8
}

Duration_Before_Leaving_Distribution

enum

NA

NA

NOT_INITIALIZED

The distribution type to use for assigning the duration of time an individual or family waits before migrating to the destination node after intervention-based migration. Each assigned value is a random draw from the distribution.

Possible values are:

NOT_INITIALIZED

No distribution set.

CONSTANT_DISTRIBUTION

Use the same value for each individual. Set Duration_Before_Leaving_Constant.

UNIFORM_DISTRIBUTION

Use a uniform distribution with a given minimum and maximum. Set Duration_Before_Leaving_Max and Duration_Before_Leaving_Min.

GAUSSIAN_DISTRIBUTION

The distribution is Gaussian (or normal). Values are resampled to ensure >= 0. Set Duration_Before_Leaving_Gaussian_Mean and Duration_Before_Leaving_Gaussian_Std_Dev.

EXPONENTIAL_DISTRIBUTION

The distribution is exponential with a given mean. Set Duration_Before_Leaving_Exponential.

WEIBULL_DISTRIBUTION

Use a Weibull distribution with a given shape and scale. Set Duration_Before_Leaving_Kappa and Duration_Before_Leaving_Lambda.

LOG_NORMAL_DISTRIBUTION

Use a log-normal distribution with a given mean and standard deviation of the natural log. Set Duration_Before_Leaving_Log_Normal_Mu and Duration_Before_Leaving_Log_Normal_Sigma.

POISSON_DISTRIBUTION

Use a Poisson distribution with a given mean. Set Duration_Before_Leaving_Poisson_Mean.

DUAL_CONSTANT_DISTRIBUTION

Use a distribution where some individuals are set to a value of zero and the rest to a given value. Set Duration_Before_Leaving_Proportion_0 and Peak_2_Value. This distribution does use the parameters set for CONSTANT_DISTRIBUTION.

DUAL_EXPONENTIAL_DISTRIBUTION

Use two exponential distributions with given means. Set Duration_Before_Leaving_Mean_1, Duration_Before_Leaving_Mean_2, and Duration_Before_Leaving_Proportion_1. This distribution does not use the parameters set for EXPONENTIAL_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "UNIFORM_DISTRIBUTION",
    "Duration_Before_Leaving_Min": 2,
    "Duration_Before_Leaving_Max": 7
}

Duration_Before_Leaving_Exponential

float

0

3.40282E+38

-1

The mean of the duration before leaving the origin node, in days, when Duration_Before_Leaving_Distribution is set to EXPONENTIAL_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "EXPONENTIAL_DISTRIBUTION",
    "Duration_Before_Leaving_Exponential": 4.25
}

Duration_Before_Leaving_Gaussian_Mean

float

0

3.40282E+38

-1

The mean of the duration before leaving the origin node, in days, when Duration_Before_Leaving_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Duration_Before_Leaving_Gaussian_Mean": 8,
    "Duration_Before_Leaving_Gaussian_Std_Dev": 1.5
}

Duration_Before_Leaving_Gaussian_Std_Dev

float

1.17549E-38

3.40282E+38

1

The standard deviation of the duration before leaving the origin node, in days, when Duration_Before_Leaving_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Duration_Before_Leaving_Gaussian_Mean": 8,
    "Duration_Before_Leaving_Gaussian_Std_Dev": 1.5
}

Duration_Before_Leaving_Kappa

float

1.17549E-38

3.40282E+38

-1

The shape value for the duration before leaving the origin node, in days, when Duration_Before_Leaving_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "WEIBULL_DISTRIBUTION",
    "Duration_Before_Leaving_Kappa": 0.9,
    "Duration_Before_Leaving_Lambda": 1.5
}

Duration_Before_Leaving_Lambda

float

1.17549E-38

3.40282E+38

-1

The scale value for the duration before leaving the origin node, in days, when Duration_Before_Leaving_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "WEIBULL_DISTRIBUTION",
    "Duration_Before_Leaving_Kappa": 0.9,
    "Duration_Before_Leaving_Lambda": 1.5
}

Duration_Before_Leaving_Log_Normal_Mu

float

-3.40282e+38

1.70141e+38

3.40282E+38

The mean of the natural log of the duration before leaving the origin node, in days, when Duration_Before_Leaving_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Duration_Before_Leaving_Log_Normal_Mu": 9,
    "Duration_Before_Leaving_Log_Normal_Sigma": 2
}

Duration_Before_Leaving_Log_Normal_Sigma

float

-3.40282e+38

1.70141e+38

3.40282E+38

The standard deviation of the natural log of the duration before leaving the origin node, in days, when Duration_Before_Leaving_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Duration_Before_Leaving_Log_Normal_Mu": 9,
    "Duration_Before_Leaving_Log_Normal_Sigma": 2
}

Duration_Before_Leaving_Max

float

0

3.40282E+38

-1

The maximum duration before leaving the origin node, in days, when Duration_Before_Leaving_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "UNIFORM_DISTRIBUTION",
    "Duration_Before_Leaving_Min": 2,
    "Duration_Before_Leaving_Max": 7
}

Duration_Before_Leaving_Mean_1

float

1.17549E-38

3.40282E+38

-1

The mean of the first exponential distribution, in days, when Duration_Before_Leaving_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Duration_Before_Leaving_Mean_1": 4,
    "Duration_Before_Leaving_Mean_2": 12,
    "Duration_Before_Leaving_Proportion_1": 0.2
}

Duration_Before_Leaving_Mean_2

float

1.17549E-38

3.40282E+38

-1

The mean of the second exponential distribution, in days, when Duration_Before_Leaving_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Duration_Before_Leaving_Mean_1": 4,
    "Duration_Before_Leaving_Mean_2": 12,
    "Duration_Before_Leaving_Proportion_1": 0.2
}

Duration_Before_Leaving_Min

float

0

3.40282E+38

-1

The minimum duration before leaving the origin node, in days, when Duration_Before_Leaving_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "UNIFORM_DISTRIBUTION",
    "Duration_Before_Leaving_Min": 2,
    "Duration_Before_Leaving_Max": 7
}

Duration_Before_Leaving_Peak_2_Value

float

0

3.40282E+38

-1

The duration before leaving the origin node, in days, to assign to the remaining individuals when Duration_Before_Leaving_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Duration_Before_Leaving_Proportion_0": 0.25,
    "Duration_Before_Leaving_Peak_2_Value": 5
}

Duration_Before_Leaving_Poisson_Mean

float

0

3.40282E+38

-1

The mean of the duration before leaving the origin node, in days, when Duration_Before_Leaving is set to POISSON_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "POISSON_DISTRIBUTION",
    "Duration_Before_Leaving_Poisson_Mean": 5
}

Duration_Before_Leaving_Proportion_0

float

0

1

-1

The proportion of individuals to assign a value of zero days before leaving the origin node when Duration_Before_Leaving_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Duration_Before_Leaving_Proportion_0": 0.25,
    "Duration_Before_Leaving_Peak_2_Value": 5
}

Duration_Before_Leaving_Proportion_1

float

0

1

-1

The proportion of individuals in the first exponential distribution when Duration_Before_Leaving_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Duration_Before_Leaving_Mean_1": 4,
    "Duration_Before_Leaving_Mean_2": 12,
    "Duration_Before_Leaving_Proportion_1": 0.2
}

Intervention_Name

string

NA

NA

NA

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "MigrateIndividuals",
        "Intervention_Name": "Seasonal migration for agriculture"
    }
}

Is_Moving

boolean

NA

NA

0

Set to true (1) to indicate the individual is permanently moving to a new home node for intervention-based migration. Once at the new home node, the person will be available for new round trips.

 
{
    "Is_Moving": 1
}

New_Property_Value

string

NA

NA

NA

An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

NodeID_To_Migrate_To

integer

0

4.29E+09

0

The destination node ID for intervention-based migration.

 
{
    "NodeID_To_Migrate_To": 26
}

{
  "Use_Defaults": 1,
  "Events": [
    {
      "class": "CampaignEvent",
      "Start_Day": 5,
      "Nodeset_Config": {
        "class": "NodeSetNodeList",
        "Node_List": [
          1
        ]
      },
      "Event_Coordinator_Config": {
        "class": "StandardInterventionDistributionEventCoordinator",
        "Target_Residents_Only": 1,
        "Target_Demographic": "Everyone",
        "Demographic_Coverage": 1.0,
        "Intervention_Config": {
          "class": "MigrateIndividuals",
          "NodeID_To_Migrate_To": 2,
          "Duration_Before_Leaving_Distribution": "CONSTANT_DISTRIBUTION",
          "Duration_At_Node_Distribution": "CONSTANT_DISTRIBUTION",
          "Is_Moving": 0,
          "Duration_Before_Leaving_Constant": 0,
          "Duration_At_Node_Constant": 999
        }
      }
    }
  ]
}
MultiEffectBoosterVaccine

The MultiEffectBoosterVaccine intervention class is derived from MultiEffectVaccine and preserves many of the same parameters. Upon distribution and successful take, the vaccine’s effect in each immunity compartment (acquisition, transmission, and mortality) is determined by the recipient’s immune state. If the recipient’s immunity modifier in the corresponding compartment is above a user-specified threshold, then the vaccine’s initial effect will be equal to the corresponding priming parameter. If the recipient’s immune modifier is below this threshold, then the vaccine’s initial effect will be equal to the corresponding boost parameter. After distribution, the effect wanes, just like a MultiEffectVaccine. The behavior is intended to mimic biological priming and boosting.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Acquire_Config

JSON object

NA

NA

NA

The configuration for multi-effect vaccine acquisition. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Acquire_Config": {
        "Initial_Effect": 0.9,
        "Decay_Time_Constant": 2525,
        "class": "WaningEffectExponential"
    }
}

Boost_Acquire

float

0

1

0

Specifies the boosting effect on acquisition immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. This does not replace current immunity, it builds multiplicatively on top of it.

 
{
    "Boost_Acquire": 0.7
}

Boost_Mortality

float

0

1

0

Specifies the boosting effect on mortality immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. This does not replace current immunity, it builds multiplicatively on top of it.

 
{
    "Boost_Mortality": 1.0
}

Boost_Threshold_Acquire

float

0

1

0

Specifies how much acquisition immunity is required before the vaccine changes from a prime to a boost.

 
{
    "Boost_Threshold_Acquire": 0.0
}

Boost_Threshold_Mortality

float

0

1

0

Specifies how much mortality immunity is required before the vaccine changes from a prime to a boost.

 
{
    "Boost_Threshold_Mortality": 0.0
}

Boost_Threshold_Transmit

float

0

1

0

Specifies how much transmission immunity is required before the vaccine changes from a prime to a boost.

 
{
    "Boost_Threshold_Transmit": 0.0
}

Boost_Transmit

float

0

1

0

Specifies the boosting effect on transmission immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine. This does not replace current immunity, it builds multiplicatively on top of it.

 
{
    "Boost_Transmit": 0.5
}

Cost_To_Consumer

float

0

999999

10

The unit cost per vaccine (unamortized).

 
{
    "Cost_To_Consumer": 10.0
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Intervention_Name

string

NA

NA

MultiEffectBoosterVaccine

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "MultiEffectBoosterVaccine",
        "Intervention_Name": "Tetanus booster shot"
    }
}

Mortality_Config

JSON object

NA

NA

NA

The configuration for multi-effect vaccine mortality. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Mortality_Config": {
        "Initial_Effect": 1.0,
        "Decay_Time_Constant": 2525,
        "class": "WaningEffectExponential"
    }
}

New_Property_Value

string

NA

NA

NA

An optional IndividualProperty key:value pair that will be assigned when the intervention is first updated. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Prime_Acquire

float

0

1

0

Specifies the priming effect on acquisition immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine.

 
{
    "Prime_Acquire": 0.1
}

Prime_Mortality

float

0

1

0

Specifies the priming effect on mortality immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine.

 
{
    "Prime_Mortality": 0.3
}

Prime_Transmit

float

0

1

0

Specifies the priming effect on transmission immunity for naive individuals (without natural or vaccine-derived immunity) for a multi-effect booster vaccine.

 
{
    "Prime_Transmit": 0.2
}

Transmit_Config

JSON object

NA

NA

NA

The configuration for multi-effect vaccine transmission. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Transmit_Config": {
        "Initial_Effect": 0.6,
        "Decay_Time_Constant": 2525,
        "class": "WaningEffectExponential"
    }
}

Vaccine_Take

float

0

1

1

The rate at which delivered vaccines will successfully stimulate an immune response and achieve the desired efficacy. For example, if it is set to 0.9, there will be a 90 percent chance that the vaccine will start with the specified efficacy, and a 10 percent chance that it will have no efficacy at all.

 
{
    "Intervention_Config": {
        "class": "SimpleVaccine",
        "Cost_To_Consumer": 10,
        "Vaccine_Type": "AcquisitionBlocking",
        "Vaccine_Take": 1,
        "Efficacy_Is_Multiplicative": 0,
        "Waning_Config": {
            "class": "WaningEffectConstant",
            "Initial_Effect": 0.3
        }
    }
}

{
    "Use_Defaults": 1,
    "Campaign_Name": "Generic Seattle Regression Campaign",
    "Events": [{
        "Event_Coordinator_Config": {
            "Demographic_Coverage": 1.0,
            "Intervention_Config": {
                "Cost_To_Consumer": 10.0,
                "Vaccine_Take": 1,
                "Prime_Acquire": 0.1,
                "Prime_Transmit": 0.2,
                "Prime_Mortality": 0.3,
                "Boost_Acquire": 0.7,
                "Boost_Transmit": 0.5,
                "Boost_Mortality": 1.0,
                "Boost_Threshold_Acquire": 0.0,
                "Boost_Threshold_Transmit": 0.0,
                "Boost_Threshold_Mortality": 0.0,
                "Acquire_Config": {
                    "Box_Duration": 100,
                    "Initial_Effect": 0.5,
                    "class": "WaningEffectBox"
                },
                "Transmit_Config": {
                    "Box_Duration": 100,
                    "Initial_Effect": 0.5,
                    "class": "WaningEffectBox"
                },
                "Mortality_Config": {
                    "Box_Duration": 100,
                    "Initial_Effect": 0.5,
                    "class": "WaningEffectBox"
                },
                "class": "MultiEffectBoosterVaccine"
            },
            "Target_Demographic": "Everyone",
            "class": "StandardInterventionDistributionEventCoordinator"
        },
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 1,
        "class": "CampaignEvent"
    }]
}
MultiEffectVaccine

The MultiEffectVaccine intervention class implements vaccine campaigns in the simulation. Vaccines can effect all of the following:

  • Reduce the likelihood of acquiring an infection

  • Reduce the likelihood of transmitting an infection

  • Reduce the likelihood of death

After distribution, the effect wanes over time.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Acquire_Config

JSON object

NA

NA

NA

The configuration for multi-effect vaccine acquisition. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Acquire_Config": {
        "Initial_Effect": 0.9,
        "Decay_Time_Constant": 2525,
        "class": "WaningEffectExponential"
    }
}

Cost_To_Consumer

float

0

999999

10

The unit cost per vaccine (unamortized).

 
{
    "Cost_To_Consumer": 10.0
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Intervention_Name

string

NA

NA

MultiEffectVaccine

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "MultiEffectVaccine",
        "Intervention_Name": "Routine MMR vaccination"
    }
}

Mortality_Config

JSON object

NA

NA

NA

The configuration for multi-effect vaccine mortality. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Mortality_Config": {
        "Initial_Effect": 1.0,
        "Decay_Time_Constant": 2525,
        "class": "WaningEffectExponential"
    }
}

New_Property_Value

string

NA

NA

NA

An optional IndividualProperty key:value pair that will be assigned when the intervention is first updated. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Transmit_Config

JSON object

NA

NA

NA

The configuration for multi-effect vaccine transmission. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Transmit_Config": {
        "Initial_Effect": 0.6,
        "Decay_Time_Constant": 2525,
        "class": "WaningEffectExponential"
    }
}

Vaccine_Take

float

0

1

1

The rate at which delivered vaccines will successfully stimulate an immune response and achieve the desired efficacy. For example, if it is set to 0.9, there will be a 90 percent chance that the vaccine will start with the specified efficacy, and a 10 percent chance that it will have no efficacy at all.

 
{
    "Intervention_Config": {
        "class": "SimpleVaccine",
        "Cost_To_Consumer": 10,
        "Vaccine_Type": "AcquisitionBlocking",
        "Vaccine_Take": 1,
        "Efficacy_Is_Multiplicative": 0,
        "Waning_Config": {
            "class": "WaningEffectConstant",
            "Initial_Effect": 0.3
        }
    }
}

{
    "Events": [{
        "Event_Coordinator_Config": {
            "Demographic_Coverage": 1,
            "Intervention_Config": {
                "Cost_To_Consumer": 20,
                "Vaccine_Take": 1,
                "Vaccine_Type": "Generic",
                "class": "MultiEffectVaccine",
                "Acquire_Config": {
                    "Initial_Effect": 0.9,
                    "Decay_Time_Constant": 7300,
                    "class": "WaningEffectExponential"
                },
                "Transmit_Config": {
                    "Initial_Effect": 0.9,
                    "Decay_Time_Constant": 7300,
                    "class": "WaningEffectExponential"
                },
                "Mortality_Config": {
                    "Initial_Effect": 1.0,
                    "Decay_Time_Constant": 7300,
                    "class": "WaningEffectExponential"
                }
            },
            "Property_Restrictions": [
                "Accessibility:VaccineTake"
            ],
            "Target_Age_Max": 100,
            "Target_Age_Min": 12,
            "Target_Demographic": "ExplicitAgeRanges",
            "class": "StandardInterventionDistributionEventCoordinator"
        },
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 1,
        "class": "CampaignEvent"
    }],
    "Use_Defaults": 1
}
MultiInsecticideIRSHousingModification

The MultiInsecticideIRSHousingModification intervention class is an individual-level intervention that builds on the IRSHousingModification class by enabling the use of multiple insecticides. The killing efficacy of each insecticide can be specified. This class uses Indoor Residual Spraying (IRS), where insecticide is sprayed on the interior walls of houses such that mosquitoes resting on the walls after consuming blood meals will die.

The effectiveness of the intervention is combined using the following equation:

Total efficacy = 1.0 – (1.0 – efficacy_1) * (1.0 – efficacy_2) * … * (1.0 – efficacy_n)

At a glance:

  • Distributed to: Individuals

  • Serialized: Yes, if it has been distributed to a person.

  • Uses insecticides: Yes. It can target specific species or other subgroups.

  • Time-based expiration: No

  • Purge existing: Yes. If a new intervention is added to to the individual, the existing intervention of the same name is removed when the new one is added.

  • Vector killing contributes to: Indoor Die After Feeding

  • Vector effects: Repelling and killing

  • Vector sexes affected: Females only

  • Vector life stage affected: Adult

Warning

EMOD simulations models nodes and individuals within nodes; they do not model houses. Therefore, housing modifications are received by individuals, not houses.

Use of this class and other housing modification classes requires caution because they can have unintended effects. For example, individuals in the same household may receive different housing modification interventions. An individual receiving a housing modification intervention who then migrates to another node will continue to receive that intervention. We recommend that you configure your simulation to take these logical inconsistencies into account.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Cost_To_Consumer

float

0

999999

8

The unit cost per housing modification (unamortized).

 
{
    "Cost_To_Consumer": 8.0
}

Insecticides

array of JSON objects

NA

NA

[]

An array of objects defining the blocking and killing efficacy of each insecticide.

 
{
    "Insecticides" : [
        {
            "Insecticide_Name": "pyrethroid",
            "Repelling_Config": {
                "Box_Duration": 300,
                "Initial_Effect": 0.25,
                "class": "WaningEffectBox"
            },
            "Killing_Config": {
                "Box_Duration": 300,
                "Initial_Effect": 1.0,
                "class": "WaningEffectBox"
            }
        },
        {
            "Insecticide_Name": "carbamate",
            "Repelling_Config": {
                "Box_Duration": 300,
                "Initial_Effect": 0.25,
                "class": "WaningEffectBox"
            },
            "Killing_Config": {
                "Box_Duration": 300,
                "Initial_Effect": 1.0,
                "class": "WaningEffectBox"
             }
        }
    ]
}

Intervention_Name

string

NA

NA

MultiInsecticideIRSHousingModification

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "MultiInsecticideIRSHousingModification",
        "Intervention_Name": "Indoor DDT spraying"
    }
}

Killing_Config

JSON object

NA

NA

NA

The configuration of killing efficacy and waning for housing modification. Killing is conditional on the vector not being repelled before feeding. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Killing_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0.53429,
        "class": "WaningEffectBox"
    }
}

Repelling_Config

JSON object

NA

NA

NA

Defines a waning effect that determines the probability that a vector is repelled due to the intervention. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Repelling_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0.0,
        "class": "WaningEffectBox"
    }
}

{
    "class": "MultiInsecticideIRSHousingModification",
    "Cost_To_Consumer": 1.0,
    "Insecticides": [{
            "Insecticide_Name": "pyrethroid",
            "Repelling_Config": {
                "Box_Duration": 300,
                "Initial_Effect": 0.25,
                "class": "WaningEffectBox"
            },
            "Killing_Config": {
                "Box_Duration": 300,
                "Initial_Effect": 1.0,
                "class": "WaningEffectBox"
            }
        },
        {
            "Insecticide_Name": "carbamate",
            "Repelling_Config": {
                "Box_Duration": 300,
                "Initial_Effect": 0.25,
                "class": "WaningEffectBox"
            },
            "Killing_Config": {
                "Box_Duration": 300,
                "Initial_Effect": 1.0,
                "class": "WaningEffectBox"
            }
        }
    ]
}
MultiInsecticideUsageDependentBednet

The MultiInsecticideUsageDependentBednet intervention class is an individual-level intervention that is similar to the UsageDependentBednet class but allows the addition of multiple insecticides.

The effectiveness of the intervention is combined using the following equation:

Total efficacy = 1.0 – (1.0 – efficacy_1) * (1.0 – efficacy_2) * … * (1.0 – efficacy_n)

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Blocking_Config

JSON object

NA

NA

NA

Object in the Insecticides array, configures the rate of blocking for indoor mosquito feeds on individuals with an ITN. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Blocking_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0,
        "class": "WaningEffectBox"
    }
}

Cost_To_Consumer

float

0

999999

3.75

The unit cost per bednet (unamortized)

 
{
    "Cost_To_Consumer": 4.5
}

Discard_Event

enum

NA

NA

NA

The event that is broadcast when an individual discards their bednet, either by replacing an existing net or due to the expiration timer. See Event list for possible values, or you can use a custom event (defined in the Custom_Individual_Events list in the config file).

 
{
    "Received_Event": "Bednet_Got_New_One",
    "Using_Event": "Bednet_Using",
    "Discard_Event": "Bednet_Discarded",
    "Expiration_Distribution_Type": "FIXED_DURATION",
    "Expiration_Period": 50
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Expiration_Period_Constant

float

0

3.40282E+38

-1

The expiration period to use for all bednets, in days, when Expiration_Period_Distribution is set to CONSTANT_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "CONSTANT_DISTRIBUTION",
    "Expiration_Period_Constant": 8
}

Expiration_Period_Distribution

enum

NA

NA

NOT_INITIALIZED

The distribution type to use for assigning the expiration period to a usage-dependent bednet. Each assigned value is a random draw from the distribution.

Possible values are:

NOT_INITIALIZED

No distribution set.

CONSTANT_DISTRIBUTION

Use the same value for each individual. Set Expiration_Period_Constant.

UNIFORM_DISTRIBUTION

Use a uniform distribution with a given minimum and maximum. Set Expiration_Period_Max and Expiration_Period_Min.

GAUSSIAN_DISTRIBUTION

The distribution is Gaussian (or normal). Values are resampled to ensure >= 0. Set Expiration_Period_Gaussian_Mean and Expiration_Period_Gaussian_Std_Dev.

EXPONENTIAL_DISTRIBUTION

The distribution is exponential with a given mean. Set Expiration_Period_Exponential.

WEIBULL_DISTRIBUTION

Use a Weibull distribution with a given shape and scale. Set Expiration_Period_Kappa and Expiration_Period_Lambda.

LOG_NORMAL_DISTRIBUTION

Use a log-normal distribution with a given mean and width. Set Expiration_Period_Log_Normal_Mu and Expiration_Period_Log_Normal_Sigma.

POISSON_DISTRIBUTION

Use a Poisson distribution with a given mean. Set Expiration_Period_Poisson_Mean.

DUAL_CONSTANT_DISTRIBUTION

Use a distribution where some individuals are set to a value of zero and the rest to a given value. Set Expiration_Period_Proportion_0 and Peak_2_Value. This distribution does not use the parameters set for CONSTANT_DISTRIBUTION.

DUAL_EXPONENTIAL_DISTRIBUTION

Use two exponential distributions with given means. Set Expiration_Period_Mean_1, Expiration_Period_Mean_2, and Expiration_Period_Proportion_1. This distribution does not use the parameters set for EXPONENTIAL_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Duration_Before_Leaving_Log_Normal_Mu": 9,
    "Duration_Before_Leaving_Log_Normal_Sigma": 2
}

Expiration_Period_Exponential

float

0

3.40282E+38

-1

The mean of the expiration period, in days, when Expiration_Period_Distribution is set to EXPONENTIAL_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "EXPONENTIAL_DISTRIBUTION",
    "Expiration_Period_Exponential": 4.25
}

Expiration_Period_Gaussian_Mean

float

0

3.40282E+38

-1

The mean of the expiration period, in days, when Expiration_Period_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Expiration_Period_Gaussian_Mean": 8,
    "Expiration_Period_Gaussian_Std_Dev": 1.5
}

Expiration_Period_Gaussian_Std_Dev

float

1.17549E-38

3.40282E+38

-1

The standard deviation of the expiration period, in days, when Expiration_Period_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Expiration_Period_Gaussian_Mean": 8,
    "Expiration_Period_Gaussian_Std_Dev": 1.5
}

Expiration_Period_Kappa

float

1.17549E-38

3.40282E+38

-1

The shape value for the expiration period, in days, when Expiration_Period_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "WEIBULL_DISTRIBUTION",
    "Expiration_Period_Kappa": 0.9,
    "Expiration_Period_Lambda": 1.5
}

Expiration_Period_Lambda

float

1.17549E-38

3.40282E+38

-1

The scale value for the expiration period, in days, when Expiration_Period_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "WEIBULL_DISTRIBUTION",
    "Expiration_Period_Kappa": 0.9,
    "Expiration_Period_Lambda": 1.5
}

Expiration_Period_Log_Normal_Mu

float

-3.40282e+38

1.70141e+38

3.40282E+38

The mean of the expiration period, in days, when Expiration_Period_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Expiration_Period_Log_Normal_Mu": 9,
    "Expiration_Period_Log_Normal_Sigma": 2
}

Expiration_Period_Log_Normal_Sigma

float

-3.40282e+38

1.70141e+38

3.40282E+38

The width of the expiration period, in days, when Expiration_Period_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Expiration_Period_Log_Normal_Mu": 9,
    "Expiration_Period_Log_Normal_Sigma": 2
}

Expiration_Period_Max

float

0

3.40282E+38

-1

The maximum expiration period, in days, when Expiration_Period_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "UNIFORM_DISTRIBUTION",
    "Expiration_Period_Min": 2,
    "Expiration_Period_Max": 7
}

Expiration_Period_Mean_1

float

1.17549E-38

3.40282E+38

-1

The mean of the first exponential distribution, in days, when Expiration_Period_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Expiration_Period_Mean_1": 4,
    "Expiration_Period_Mean_2": 12,
    "Expiration_Period_Proportion_1": 0.2
}

Expiration_Period_Mean_2

float

1.17549E-38

3.40282E+38

-1

The mean of the second exponential distribution, in days, when Expiration_Period_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Expiration_Period_Mean_1": 4,
    "Expiration_Period_Mean_2": 12,
    "Expiration_Period_Proportion_1": 0.2
}

Expiration_Period_Min

float

0

3.40282E+38

-1

The minimum expiration period, in days, when Expiration_Period_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "UNIFORM_DISTRIBUTION",
    "Expiration_Period_Min": 2,
    "Expiration_Period_Max": 7
}

Expiration_Period_Peak_2_Value

float

0

3.40282E+38

-1

The expiration period value to assign to the remaining bednets, in days, when Expiration_Period_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Expiration_Period_Proportion_0": 0.25,
    "Expiration_Period_Peak_2_Value": 5
}

Expiration_Period_Poisson_Mean

float

0

3.40282E+38

-1

The mean of the expiration period, in days, when Expiration_Period is set to POISSON_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "POISSON_DISTRIBUTION",
    "Expiration_Period_Poisson_Mean": 5
}

Expiration_Period_Proportion_0

float

0

1

-1

The proportion of bednets to assign a value of zero days until expiration when Expiration_Period_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Expiration_Period_Proportion_0": 0.25,
    "Expiration_Period_Peak_2_Value": 5
}

Expiration_Period_Proportion_1

float

0

1

-1

The proportion of bednets in the first exponential distribution.

 
{
    "Expiration_Period_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Expiration_Period_Mean_1": 4,
    "Expiration_Period_Mean_2": 12,
    "Expiration_Period_Proportion_1": 0.2
}

Insecticide_Name

string

NA

NA

UNINITIALIZED STRING

Object in the Insecticides array, the name of the insecticide defined in Insecticides for this intervention. If insecticides are being used, this must be defined as one of those values; if they are not being used this can be empty. It cannot have a value if you did not configure <config.Insecticides>.

 
{
    "Insecticide_Name": "carbamate"
}

Insecticides

array of JSON objects

NA

NA

[]

An array of objects defining the blocking and killing efficacy of each insecticide.

 
{
    "Insecticides" : [
        {
            "Insecticide_Name": "pyrethroid",
            "Repelling_Config": {
                "Box_Duration": 300,
                "Initial_Effect": 0.25,
                "class": "WaningEffectBox"
            },
            "Killing_Config": {
                "Box_Duration": 300,
                "Initial_Effect": 1.0,
                "class": "WaningEffectBox"
            }
        },
        {
            "Insecticide_Name": "carbamate",
            "Repelling_Config": {
                "Box_Duration": 300,
                "Initial_Effect": 0.25,
                "class": "WaningEffectBox"
            },
            "Killing_Config": {
                "Box_Duration": 300,
                "Initial_Effect": 1.0,
                "class": "WaningEffectBox"
             }
        }
    ]
}

Intervention_Name

string

NA

NA

MultiInsecticideUsageDependentBednet

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "MultiInsecticideUsageDependentBednetUsageDependentBednet",
        "Intervention_Name": "IRS bednet that wears over time"
    }
}

Killing_Config

JSON object

NA

NA

NA

Object in the Insecticides array, the configuration of the rate at which mosquitoes die, conditional on a successfully blocked feed. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Killing_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0.53429,
        "class": "WaningEffectBox"
    }
}

Received_Event

enum

NA

NA

“”

This parameter broadcasts when a new net is received, either the first net or a replacement net. See Event list for possible values, or you can use a custom event (defined in the Custom_Individudual_Events list in the config file).

 
{
    "Received_Event": "Bednet_Got_New_One",
    "Using_Event": "Bednet_Using",
    "Discard_Event": "Bednet_Discarded"
}

Repelling_Config

JSON object

NA

NA

NA

Object in the Insecticides array, defines a waning effect that determines the probability that a vector is repelled due to the intervention. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Repelling_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0,
        "class": "WaningEffectBox"
    }
}

Usage_Config_List

array of JSON objects

NA

NA

NA

The list of WaningEffects whose effects are multiplied together to get the usage effect. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Usage_Config_List": [
        {
            "class": "WaningEffectConstant",
            "Initial_Effect": 1.0
        }
    ]
}

Using_Event

enum

NA

NA

NA

This parameter broadcasts each time step in which a bednet is used. See Event list for possible values, or you can use a custom event (defined in the Custom_Individudual_Events list in the config file).

 
{
    "Received_Event": "Bednet_Got_New_One",
    "Using_Event": "Bednet_Using",
    "Discard_Event": "Bednet_Discarded"
}

{
    "Events": [{
            "class": "MultiInsecticideUsageDependentBednet",
            "Cost_To_Consumer": 5,
            "Insecticides": [{
                    "Insecticide_Name": "pyrethroid",
                    "Repelling_Config": {
                        "Box_Duration": 300,
                        "Initial_Effect": 0.25,
                        "class": "WaningEffectBox"
                    },
                    "Killing_Config": {
                        "Box_Duration": 300,
                        "Initial_Effect": 1.0,
                        "class": "WaningEffectBox"
                    }
                },
                {
                    "Insecticide_Name": "carbamate",
                    "Repelling_Config": {
                        "Box_Duration": 300,
                        "Initial_Effect": 0.25,
                        "class": "WaningEffectBox"
                    },
                    "Killing_Config": {
                        "Box_Duration": 300,
                        "Initial_Effect": 1.0,
                        "class": "WaningEffectBox"
                    }
                }
            ],
            "Usage_Config_List": [{
                    "Durability_Map": {
                        "Times": [0.0, 12.99, 13.0, 125.0],
                        "Values": [0.0, 0.00, 1.0, 1.0]
                    },
                    "Initial_Effect": 1.0,
                    "class": "WaningEffectMapLinearAge"
                },
                {
                    "Durability_Map": {
                        "Times": [0.0, 20.0, 21.0, 30.0, 31.0, 365.0],
                        "Values": [1.0, 1.0, 0.0, 0.0, 1.0, 1.0]
                    },
                    "Initial_Effect": 1.0,
                    "class": "WaningEffectMapLinearSeasonal"
                }
            ],
            "Received_Event": "Bednet_Got_New_One",
            "Using_Event": "Bednet_Using",
            "Discard_Event": "Bednet_Discarded",
            "Expiration_Period_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
            "Expiration_Period_Mean_1": 60,
            "Expiration_Period_Mean_2": 50,
            "Expiration_Period_Proportion_1": 1.0
        }

    ],
    "Use_Defaults": 1
}
MultiInterventionDistributor

The MultiInterventionDistributor intervention class allows you to input a list of interventions, rather than just a single intervention, to be distributed simultaneously to the same individuals.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Intervention_List

array of JSON objects

NA

NA

NA

The list of individual interventions that is distributed by MultiInterventionDistributor.

 
{
    "Actual_IndividualIntervention_Config": {
        "Intervention_List": [
            {
                "Cost_To_Consumer": 1,
                "Dosing_Type": "FullTreatmentNewDetectionTech",
                "Drug_Type": "Artemether",
                "class": "AntimalarialDrug"
            },
            {
                "Cost_To_Consumer": 1,
                "Dosing_Type": "FullTreatmentNewDetectionTech",
                "Drug_Type": "Lumefantrine",
                "class": "AntimalarialDrug"
            }
        ],
        "class": "MultiInterventionDistributor"
    }
}

Intervention_Name

string

NA

NA

MultiInterventionDistributor

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "MultiInterventionDistributor",
        "Intervention_Name": "Complete malaria campaign"
    }
}

New_Property_Value

string

NA

NA

NA

An optional IndividualProperty key:value pair that will be assigned when the intervention is first updated. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

{
    "Events": [{
        "class": "SimpleDiagnostic",
        "Treatment_Fraction": 0.7,
        "Base_Sensitivity": 0.8,
        "Base_Specificity": 0.9,
        "Cost_To_Consumer": 0,
        "Days_To_Diagnosis": 0.0,
        "Event_Or_Config": "Config",
        "Positive_Diagnosis_Config": {
            "class": "MultiInterventionDistributor",
            "Intervention_List": [{
                    "class": "SimpleVaccine",
                    "Vaccine_Type": "AcquisitionBlocking",
                    "Vaccine_Take": 1,
                    "Waning_Config": {
                        "class": "WaningEffectBox",
                        "Box_Duration": 3650,
                        "Initial_Effect": 0.1
                    }
                },
                {
                    "class": "SimpleVaccine",
                    "Vaccine_Type": "TransmissionBlocking",
                    "Vaccine_Take": 1,
                    "Waning_Config": {
                        "class": "WaningEffectBox",
                        "Box_Duration": 3650,
                        "Initial_Effect": 0.9
                    }
                },
                {
                    "class": "SimpleVaccine",
                    "Vaccine_Type": "MortalityBlocking",
                    "Vaccine_Take": 1,
                    "Waning_Config": {
                        "class": "WaningEffectBox",
                        "Box_Duration": 3650,
                        "Initial_Effect": 0.5
                    }
                }
            ]
        }
    }]
}
MultiPackComboDrug

The MultiPackComboDrug intervention class is an individual-level intervention that explicitly models which doses of anti-malarial pills are taken when. This intervention is similar to the AdherentDrug class, but allows modeling pill packs that involve multiple drugs with different dosing per drug.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Cost_To_Consumer

float

0

99999

10

The unit cost per drug (unamortized).

 
{
    "Cost_To_Consumer": 10
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to nodes with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Dose_Interval

float

0

1000

1

The number of days to wait between the doses, defined in the Doses parameter.

 
{
    "Dose_Interval": 10
}

Doses

array of strings

NA

NA

NA

This is a two dimensional array of the drug names defined in the config. parameter Malaria_Drug_Params. Each inner array defines the drugs to be taken for a single dose. The number of doses is determined by the number of inner arrays. An empty dose is allowed and counts as if the person did not take any doses that day. If multiple pills of the same drug are taken in the same dose, that drug name can be specified multiple times.

 
{
    "Doses": [
    ["TestDrugA", "TestDrugB"],
    ["TestDrugA"],
    ["TestDrugA", "TestDrugB"],
    ["TestDrugB"]
]
}

Intervention_Name

string

NA

NA

MultiPackDrugCombo

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "MultiPackDrugCombo",
        "Intervention_Name": "Isoniazid with mediocre adherence"
    }
}

New_Property_Value

string

NA

NA

NA

An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

{
    "class": "CampaignEvent",
    "Start_Day": 30,
    "Nodeset_Config": {
        "class": "NodeSetNodeList",
        "Node_List": [7]
    },
    "Event_Coordinator_Config": {
        "class": "StandardInterventionDistributionEventCoordinator",
        "Number_Repetitions": 1,
        "Timesteps_Between_Repetitions": 0,
        "Target_Demographic": "Everyone",
        "Demographic_Coverage": 1,
        "Intervention_Config": {
            "class": "MultiPackComboDrug",
            "Dont_Allow_Duplicates": 0,
            "Cost_To_Consumer": 1,
            "Dose_Interval": 12,
            "Doses": [
                ["TestDrugA", "TestDrugA", "TestDrugB"],
                ["TestDrugBA"],
                ["TestDrugA", "TestDrugB"],
                ["TestDrugBA"],
                ["TestDrugAB"],
                [],
                ["TestDrugAB"]
            ]

        }
    }
}

The Doses array allows the user to specify what drugs are taken in what dose. The Dose_Interval parameter is used to determine the number of days between these doses. A dose can be specified to have zero drugs. For example, if we wanted to model a 7-day pill pack that involved two drugs where the person gets two doses of DrugA on Day 0 and then one every other day for 7-days and DrugB was to be taken every day for 4-days, we would configure it like the following:

{
    "Dose_Interval": 1,
    "Doses": [
        [ "DrugA", "DrugA", "DrugB" ],
        [                   "DrugB" ],
        [ "DrugA",          "DrugB" ],
        [                   "DrugB" ],
        [ "DrugA"                   ],
        [                           ],
        [ "DrugA"                   ]
    ]
}

However, if DrugB was instead to be taken twice–once on Day 0 and once on Day 4, it would be configured like the following:

{
    "Dose_Interval": 2,
    "Doses": [
        [ "DrugA", "DrugA", "DrugB" ],
        [ "DrugA"                   ],
        [ "DrugA",          "DrugB" ],
        [ "DrugA"                   ]
    ]
}
OutbreakIndividual

The OutbreakIndividual intervention class introduces contagious diseases that are compatible with the simulation type to existing individuals using the individual targeted features configured in the appropriate event coordinator. To instead add new infection individuals, use Outbreak.

Note, when using Malaria_Model: MALARIA_MECHANISTIC_MODEL_WITH_PARASITE_GENETICS, do not use this intervention class. Instead, use OutbreakIndividualMalariaGenetics.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Clade

integer

0

10

0

The clade ID of the outbreak infection. Together with the genome ID (Genome) they are a unitary object representing a strain of infection, which allows for differentiation among infections. Values for Clade may range from 0 to Number_of_Clades-1 (set in the configuration file).

 
{
    "Clade": 1
}

Genome

integer

-1

1.67E+07

0

The genome ID of the outbreak infection. Together with the clade ID (Clade) they represent an infection as a unitary object. Values for Genome may range from -1 to 2Log2_Number_of_Genomes_per_Clade-1. Set this to -1 to create a random number generator.

Intervention distribution example:

{
    "Enable_Strain_Tracking": 1,
    "Events": [
        {
            "Event_Coordinator_Config": {
                "Demographic_Coverage": 0.001,
                "Intervention_Config": {
                    "Clade": 1,
                    "Genome": 3,
                    "IgnoreImmunity": 1,
                    "class": "Outbreak"
                },
                "Target_Demographic": "Everyone",
                "class": "StandardInterventionDistributionEventCoordinator"
            },
            "Event_Name": "Outbreak",
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 30,
            "class": "CampaignEvent"
        }
    ]
}

Ignore_Immunity

boolean

NA

NA

1

Individuals will be force-infected (with a specific strain) regardless of actual immunity level when set to true (1).

 
{
    "Intervention_Config": {
        "Clade": 0,
        "Genome": 0,
        "class": "OutbreakIndividual",
        "Ignore_Immunity": 0
    }
}

Incubation_Period_Override

integer

-1

2147480000.0

-1

The incubation period, in days, that infected individuals will go through before becoming infectious. This value overrides the incubation period set in the configuration file. Set to -1 to honor the configuration parameter settings.

 
{
    "Incubation_Period_Override": 0
}

{
  "Events": [
    {
      "Event_Coordinator_Config": {
        "Demographic_Coverage": 0.001,
        "Intervention_Config": {
          "Clade": 1,
          "Genome": 3,
          "IgnoreImmunity": 1,
          "class": "OutbreakIndividual"
        },
        "Target_Demographic": "Everyone",
        "class": "StandardInterventionDistributionEventCoordinator"
      },
      "Nodeset_Config": {
        "class": "NodeSetAll"
      },
      "Start_Day": 30,
      "class": "CampaignEvent"
    }
  ]
}
OutbreakIndividualMalariaGenetics

The OutbreakIndividualMalariaGenetics intervention class is an individual-level intervention that extends the OutbreakIndividual class by adding the ability to specify parasite genetics for the infection. This class is only used when the configuration parameter Malaria_Model is set to MALARIA_MECHANISTIC_MODEL_WITH_PARASITE_GENETICS.

The parameter Create_Nucleotide_Sequence_From (see table below) determines how the parasite genetics are defined.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Barcode_Allele_Frequencies_Per_Genome_Location

array of floats

0

1

1

The fractions of allele occurrences for each location in the barcode. This 2D array should have one array for each location/character in the barcode. For each location, there should be four values between 0 and 1 indicating the probability that specific character appears. The possible letters are: A=0, C=1, G=2, T=3.

 
{
    "Barcode_Allele_Frequencies_Per_Genome_Location" : [
        [ 1.00, 0.00, 0.00, 0.00 ],
        [ 0.00, 1.00, 0.00, 0.00 ],
        [ 0.00, 0.00, 1.00, 0.00 ],
        [ 0.00, 0.00, 0.00, 1.00 ],
        [ 0.50, 0.50, 0.00, 0.00 ],
        [ 0.00, 0.50, 0.50, 0.00 ],
        [ 0.00, 0.00, 0.50, 0.50 ],
        [ 0.25, 0.25, 0.25, 0.25 ],
        [ 0.10, 0.20, 0.30, 0.40 ],
        [ 0.40, 0.30, 0.20, 0.10 ]
    ]
}

Barcode_String

string

NA

NA

UNINITIALIZED STRING

A series of nucleotide base letters (A, C, G, T) that represent the values at locations in the genome. The length of the string depends on the number of locations defined in the Parasite_Genetics configuration parameter Barcode_Genome_Locations. Each character of the string corresponds to one of the locations and locations are assumed to be in ascending order. This also depends on Create_Nucleotide_Sequence_From when it is equal to NUCLEOTIDE_SEQUENCE or BARCODE_STRING.

 
{
    "Barcode_String": "AAAAAAAATTCT"
}

Create_Nucleotide_Sequence_From

enum

NA

NA

BARCODE_STRING

The enum indicates how the genome is created. Possible values are:

BARCODE_STRING

This option allows the user to specify a genome via the barcode. The barcode provided will set the values of the barcode locations and the rest of the locations will depend on the Parasite_Genetics configuration parameter Var_Gene_Randomness_Type setting. If the setting is FIXED_MSP or FIXED_NEIGHBORHOOD, then the barcode will be used to set where in the variant range the MSP and PfEMP1 values will come from. It is assumed that if a person has had an infection with a particular barcode, then it is likely that they have antibodies to fight a new infection with the same barcode. It is necessary to have multiple distributions of this intervention with different barcode strings in order to get infection diversity.

ALLELE_FREQUENCIES

This option allows the user to specify that each new infection from this outbreak will have a its genome created using the frequencies defined in the Barcode_Allele_Frequencies_Per_Genome_Location parameters. This is like selecting a delay time from a distribution and allows random creation of new genomes. However, after the outbreak, new genomes are only created via meiosis and recombination.

NUCLEOTIDE_SEQUENCE

This option allows the user to specify the entire nucleotide sequence including the MSP and major epitope values.

 
{
    "Create_Nucleotide_Sequence_From": "BARCODE_STRING"
}

Drug_Resistant_String

string

NA

NA

UNINITIALIZED STRING

A series of nucleotide base letters (A, C, G, T) that represent the values at locations in the genome. The length of the string depends on the number of locations defined in the Parasite_Genetics configuration parameter Drug_Resistant_Genome_Locations. Each character of the string corresponds to one of the locations, and locations are assumed to be in ascending order. This also depends on Create_Nucleotide_Sequence_From when it is equal to NUCLEOTIDE_SEQUENCE or BARCODE_STRING.

 
{
    "Drug_Resistant_String": "AATTAA"
}

Ignore_Immunity

boolean

0

1

1

Individuals will be force-infected (with a specific strain) regardless of actual immunity level when set to true (1).

 
{
    "Intervention_Config": {
        "Antigen": 0,
        "Genome": 0,
        "class": "OutbreakIndividual",
        "Ignore_Immunity": 0
    }
}

Incubation_Period_Override

integer

-1

2.14E+09

-1

The incubation period, in days, that infected individuals will go through before becoming infectious. This value overrides the incubation period set in the configuration file. Set to -1 to honor the configuration parameter settings.

 
{
    "Incubation_Period_Override": 0
}

MSP_Variant_Value

integer

0

1,000

NA

The Merozoite Surface Protein value used to determine how the antibodies recognize the merozoites. This value must be less than or equal to the value for the configuration parameter Falciparum_MSP_Variants and depends on Create_Nucleotide_Sequence_From when it is equal to NUCLEOTIDE_SEQUENCE.

 
{
    "MSP_Variant_Value": 11
}

PfEMP1_Variants_Values

array of integers

0

10,000

NA

The PfEMP1 Variant values/major epitopes used to define how the antibodies recognize the infected red blood cells. This value must be less than or equal to the value for the configuration parameter Falciparum_PfEMP1_Variants and depends on Create_Nucleotide_Sequence_From when it is equal to NUCLEOTIDE_SEQUENCE.

 
{
    "PfEMP1_Variants_Values": [
        1,  74, 147, 220, 293, 366, 439, 512, 585, 658,
        731, 804, 877, 950,  23,  96, 169, 242, 315, 388,
        461, 534, 607, 680, 753, 826, 899, 972,  45, 118,
        191, 264, 337, 410, 483, 556, 629, 702, 775, 848,
        921, 994,  67, 140, 213, 286, 359, 432, 505, 578
    ]
}

{
    "class": "OutbreakIndividualMalariaGenetics",
    "Create_Nucleotide_Squence_From": "BARCODE_STRING",
    "Barcode_String": "ACGTACGTACGTACGTACGTACGT",
    "Drug_Resistant_String": "AAT"
}

{
    "class": "OutbreakIndividualMalariaGenetics",
    "Create_Nucleotide_Squence_From": "ALLELE_FREQUENCIES",
    "Barcode_Allele_Frequencies_Per_Genome_Location": [
        [1.00, 0.00, 0.00, 0.00],
        [0.00, 1.00, 0.00, 0.00],
        [0.00, 0.00, 1.00, 0.00],
        [0.00, 0.00, 0.00, 1.00],
        [0.50, 0.50, 0.00, 0.00],
        [0.00, 0.50, 0.50, 0.00],
        [0.00, 0.00, 0.50, 0.50],
        [0.25, 0.25, 0.25, 0.25],
        [0.10, 0.20, 0.30, 0.40],
        [0.40, 0.30, 0.20, 0.10]
    ],
    "Drug_Resistant_Allele_Frequencies_Per_Genome_Location": [
        [1.00, 0.00, 0.00, 0.00],
        [0.00, 1.00, 0.00, 0.00],
        [0.00, 0.00, 1.00, 0.00]
    ]
}

{
    "class": "OutbreakIndividualMalariaGenetics",
    "Create_Nucleotide_Squence_From": "NUCLEOTIDE_SEQUENCE",
    "Barcode_String": "ACGTACGTACGTACGTACGTACGT",
    "Drug_Resistant_String": "AAT",
    "MSP_Variant_Value": 11,
    "PfEMP1_Variants_Values": [
        1, 74, 147, 220, 293, 366, 439, 512, 585, 658,
        731, 804, 877, 950, 23, 96, 169, 242, 315, 388,
        461, 534, 607, 680, 753, 826, 899, 972, 45, 118,
        191, 264, 337, 410, 483, 556, 629, 702, 775, 848,
        921, 994, 67, 140, 213, 286, 359, 432, 505, 578
    ]
}
OutbreakIndividualMalariaVarGenes

The OutbreakIndividualMalariaVarGenes intervention class is an individual-level intervention that enables infecting people with an exact set of antigens. This can be helpful when experimenting with the immune model, such as testing how a person’s immune system reacts if they are reinfected by the same parasite. To use this intervention, set the configuration parameters Malaria_Model to MALARIA_MECHINISTIC_MODEL and Malaria_Strain_Model to FALCIPARUM_FIXED_STRAIN.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Ignore_Immunity

boolean

0

1

1

Individuals will be force-infected (with a specific strain) regardless of actual immunity level when set to true (1).

 
{
    "Intervention_Config": {
        "Antigen": 0,
        "Genome": 0,
        "class": "OutbreakIndividual",
        "Ignore_Immunity": 0
    }
}

Incubation_Period_Override

integer

-1

2.14E+09

-1

The incubation period, in days, that infected individuals will go through before becoming infectious. This value overrides the incubation period set in the configuration file. Set to -1 to honor the configuration parameter settings.

 
{
    "Incubation_Period_Override": 0
}

IRBC_Type

array of integers

0

10,000

[]

The array of PfEMP1 Major epitope variant values. There must be exactly 50 values. Min value = 0, MAX value = Falciparum_PfEMP1_Variants.

 
{
    "IRBC_Type": [
        2,75,148,221,294,367,440,513,586,659,732,805,878,951,24,97,170,243,316,389,462,535,608,681,754,827,900,973,46,119,192,265,338,411,484,557,630,703,776,849,922,995,68,141,214,287,360,433,506,579
    ]
}

Minor_Epitope_Type

array of integers

0

10,000

[]

The array of PfEMP1 Minor epitope variant values. There must be exactly 50 values. Min value = 0, MAX value = Falciparum_Nonspecific_Types * MINOR_EPITOPE_VARS_PER_SET(=5).

 
{
    "Minor_Epitope_Type": [
        2,0,3,3,1,2,3,3,0,1,3,2,1,3,0,1,1,2,4,0,1,1,0,4,0,1,1,4,4,0,2,0,4,1,2,1,1,0,1,3,3,1,2,4,2,4,4,3,2,4
    ]
}

MSP_Type

integer

0

1000

0

The Merozoite Surface Protein variant value of this infection. Min value = 0, MAX value = Falciparum_MSP_Variants.

 
{
    "MSP_Type": 2
}

{
    "Event_Coordinator_Config": {
        "class": "StandardInterventionDistributionEventCoordinator",
        "Number_Repetitions": 30,
        "Timesteps_Between_Repetitions": 150,
        "Target_Demographic": "Everyone",
        "Demographic_Coverage": 1.0,
        "Intervention_Config": {
            "class": "OutbreakIndividualMalariaVarGenes",
            "MSP_Type": 2,
            "IRBC_Type": [
                2, 75, 148, 221, 294, 367, 440, 513, 586, 659, 732, 805, 878, 951, 24, 97, 170,
                243, 316, 389, 462, 535, 608, 681, 754, 827, 900, 973, 46, 119, 192, 265, 338,
                411, 484, 557, 630, 703, 776, 849, 922, 995, 68, 141, 214, 287, 360, 433, 506, 579
            ],
            "Minor_Epitope_Type": [
                2, 0, 3, 3, 1, 2, 3, 3, 0, 1, 3, 2, 1, 3, 0, 1, 1, 2, 4, 0,
                1, 1, 0, 4, 0, 1, 1, 4, 4, 0, 2, 0, 4, 1, 2, 1, 1, 0, 1, 3, 3,
                1, 2, 4, 2, 4, 4, 3, 2, 4
            ]
        }
    }
}
PropertyValueChanger

The PropertyValueChanger intervention class assigns new individual property values to individuals. You must update one property value and have the option to update another using New_Property_Value. This parameter is generally used to move patients from one intervention state in the health care cascade (InterventionStatus) to another, though it can be used for any individual property. Individual property values are user-defined in the demographics file (see NodeProperties and IndividualProperties for more information). Note that the HINT feature does not need to be enabled to use this intervention. To instead change node properties, use NodePropertyValueChanger.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Daily_Probability

float

0

1

1

The daily probability that an individual will move to the Target_Property_Value.

 
{
    "Intervention_Config": {
        "class": "PropertyValueChanger",
        "Disqualifying_Properties": [],
        "New_Property_Value": "",
        "Target_Property_Key": "Risk",
        "Target_Property_Value": "LOW",
        "Daily_Probability": 1.0,
        "Maximum_Duration": 0,
        "Revert": 0
    }
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to nodes with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Intervention_Name

string

NA

NA

PropertyValueChanger

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "PropertyValueChanger",
        "Intervention_Name": "Diagnosed HIV+ to ART"
    }
}

Maximum_Duration

float

-1

3.40E+38

3.40E+38

The maximum amount of time individuals have to move to a new group. This timing works in conjunction with Daily_Probability.

 
{
    "Intervention_Config": {
        "class": "PropertyValueChanger",
        "Disqualifying_Properties": [],
        "New_Property_Value": "",
        "Target_Property_Key": "Risk",
        "Target_Property_Value": "LOW",
        "Daily_Probability": 1.0,
        "Maximum_Duration": 0,
        "Revert": 0
    }
}

New_Property_Value

string

NA

NA

NA

An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Revert

float

0

3.40E+38

0

The number of days before an individual moves back to their original group.

 
{
    "Intervention_Config": {
        "class": "PropertyValueChanger",
        "Disqualifying_Properties": [],
        "New_Property_Value": "",
        "Target_Property_Key": "Risk",
        "Target_Property_Value": "LOW",
        "Daily_Probability": 1.0,
        "Maximum_Duration": 0,
        "Revert": 0
    }
}

Target_Property_Key

string

NA

NA

“

The name of the individual property type whose value will be updated by the intervention.

 
{
    "Intervention_Config": {
        "class": "PropertyValueChanger",
        "Disqualifying_Properties": [],
        "New_Property_Value": "",
        "Target_Property_Key": "Risk",
        "Target_Property_Value": "LOW",
        "Daily_Probability": 1.0,
        "Maximum_Duration": 0,
        "Revert": 0
    }
}

Target_Property_Value

string

NA

NA

“

The user-defined value of the individual property that will be assigned to the individual.

 
{
    "Intervention_Config": {
        "class": "PropertyValueChanger",
        "Disqualifying_Properties": [],
        "Target_Property_Key": "Risk",
        "Target_Property_Value": "LOW",
        "New_Property_Value": "",
        "Daily_Probability": 1.0,
        "Maximum_Duration": 0,
        "Revert": 0
    }
}

{
    "Events": [
        {
            "class": "CampaignEvent",
            "Start_Day": 10,
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Target_Demographic": "Everyone",
                "Demographic_Coverage": 1.0,
                "Property_Restrictions": [
                    "Risk:LOW"
                ],
                "Intervention_Config": {
                    "class": "PropertyValueChanger",
                    "Disqualifying_Properties": [ "InterventionStatus:Diagnosed"],
                    "New_Property_Value": "InterventionStatus:Monitor",
                    "Target_Property_Key" : "Risk",
                    "Target_Property_Value" : "HIGH",
                    "Daily_Probability" : 1.0,
                    "Maximum_Duration" : 0,
                    "Revert" : 10
                }
            }
        }
    ],
    "Use_Defaults": 1
}
RTSSVaccine

The RTSSVaccine intervention class protects individuals against infection acquisition by directly boosting the circumsporozoite protein (CSP) antibody concentration. This contrasts with the SimpleVaccine intervention, which is used to modify the probability of acquisition or transmission.

The CSP antibody reduces the probability that sporozoites survive to infect the liver/hepatocytes. A higher Boosted_Antibody_Concentration means the person will be less likely to have sporozoites survive and infect the hepatocytes. Without the vaccine, CSP does not do anything. The following Immunity parameters impact CSP and its sporozoite killing ability:

  • Antibody_CSP_Killing_Threshold

  • Antibody_CSP_Killing_Inverse_Width

  • Antibody_CSP_Decay_Days

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Boosted_Antibody_Concentration

float

0

3.40E+38

1

The boosted antibody concentration, where unity equals maximum from natural exposure.

 
{
    "Intervention_Config": {
        "Antibody_Type": "CSP",
        "Antibody_Variant": 0,
        "Boosted_Antibody_Concentration": 1200,
        "Cost_To_Consumer": 1.0,
        "class": "RTSSVaccine"
    }
}

Cost_To_Consumer

float

0

999999

3.75

The unit cost of RTS,S vaccination (unamortized).

 
{
    "Intervention_Config": {
        "Antibody_Type": "CSP",
        "Antibody_Variant": 0,
        "Boosted_Antibody_Concentration": 1200,
        "Cost_To_Consumer": 1.0,
        "class": "RTSSVaccine"
    }
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Intervention_Name

string

NA

NA

RTSSVaccine

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "RTSSVaccine",
        "Intervention_Name": "CSP boosting vaccine campaign"
    }
}

New_Property_Value

string

NA

NA

NA

An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

{
    "Events": [{
        "class": "CampaignEvent",
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 20,
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Target_Demographic": "Everyone",
            "Demographic_Coverage": 0.8,
            "Intervention_Config": {
                "class": "RTSSVaccine",
                "Boosted_Antibody_Concentration": 1200,
                "Cost_To_Consumer": 1.0
            }
        }
    }],
    "Use_Defaults": 1
}
ScreeningHousingModification

The ScreeningHousingModification intervention class implements housing screens as a vector control effort. Housing screens are used to decrease the number of mosquitoes that can enter indoors and therefore reduce indoor biting.

At a glance:

  • Distributed to: Individuals

  • Serialized: Yes, if it has been distributed to a person.

  • Uses insecticides: Yes. It can target specific species or other subgroups.

  • Time-based expiration: No

  • Purge existing: Yes. If a new intervention is added to to the individual, the existing intervention of the same name is removed when the new one is added.

  • Vector killing contributes to: Indoor After Feeding

  • Vector effects: Repelling and killing

  • Vector sexes affected: Females only

  • Vector life stage affected: Adult

Warning

EMOD simulations models nodes and individuals within nodes; they do not model houses. Therefore, housing modifications are received by individuals, not houses.

Use of this class and other housing modification classes requires caution because they can have unintended effects. For example, individuals in the same household may receive different housing modification interventions. An individual receiving a housing modification intervention who then migrates to another node will continue to receive that intervention. We recommend that you configure your simulation to take these logical inconsistencies into account.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Cost_To_Consumer

float

0

999999

8

The unit cost per housing modification (unamortized).

 
{
    "Cost_To_Consumer": 10.0
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Insecticide_Name

string

NA

NA

UNINITIALIZED STRING

The name of the insecticide defined in the configuration parameter Insecticides for this intervention. If insecticides are being used, this must be defined as one of those values; if they are not being used this can be empty. It cannot have a value if you did not configure the configuration parameter Insecticides.

 
{
    "Insecticide_Name": "carbamate"
}

Intervention_Name

string

NA

NA

ScreeningHousingModification

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "ScreeningHousingModification",
        "Intervention_Name": "Window screen installation"
    }
}

Killing_Config

JSON object

NA

NA

NA

The configuration of killing efficacy and waning for housing modification. Killing is conditional on NOT repelling the mosquito before feeding. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Killing_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0.53429,
        "class": "WaningEffectBox"
    }
}

New_Property_Value

string

NA

NA

NA

An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Repelling_Config

JSON object

NA

NA

NA

Defines a waning effect that determines the probability that a vector is repelled due to the intervention. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Repelling_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0,
        "class": "WaningEffectBox"
    }
}

{
    "Events": [{
        "class": "CampaignEvent",
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 120,
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Target_Demographic": "Everyone",
            "Demographic_Coverage": 0.8,
            "Intervention_Config": {
                "class": "ScreeningHousingModification",
                "Repelling_Config": {
                    "Box_Duration": 100,
                    "Decay_Time_Constant": 150,
                    "Initial_Effect": 0.8,
                    "class": "WaningEffectBoxExponential"
                },
                "Cost_To_Consumer": 1.0,
                "Killing_Config": {
                    "Box_Duration": 100,
                    "Decay_Time_Constant": 150,
                    "Initial_Effect": 0.0,
                    "class": "WaningEffectBoxExponential"
                }
            }
        }
    }],
    "Use_Defaults": 1
}
SimpleBednet

The SimpleBednet intervention class implements insecticide-treated nets (ITN) in the simulation. ITNs are a key component of modern malaria control efforts, and have recently been scaled up towards universal coverage in sub-Saharan Africa. Modern bednets are made of a polyethylene or polyester mesh, which is impregnated with a slowly releasing pyrethroid insecticide. When mosquitoes that are seeking a blood meal encounter a net, the feeding attempt is blocked as long as the net retains its physical integrity and has been correctly installed. Blocked feeding attempts also carry the possibility of killing the mosquito. Net ownership is configured through the demographic coverage, and the blocking and killing rates of mosquitoes are time-dependent.

SimpleBednet can model the bednet usage of net owners by reducing the daily efficacy. To model individuals using nets intermittently, see UsageDependentBednet. To include multiple insecticides, see MultiInsecticideUsageDependentBednet.

At a glance:

  • Distributed to: Individuals

  • Serialized: Yes, if it has been distributed to a person.

  • Uses insecticides: Yes. Insecticides can be used to target specific species or other subgroups.

  • Time-based expiration: No, but it will expire if the WaningEffect expires (WaningEffectRandomBox and WaningEffectMapLinear expire).

  • Purge existing: Yes. If a new intervention is added to to the individual, the existing intervention of the same name is removed when the new one is added. It is possible to have multiple SimpleBednet interventions attached to an individual if they have different Intervention_Name values.

  • Vector killing contributes to: Indoor Before/After Feeding

  • Vector effects: Repelling, blocking, killing

  • Vector sexes affected: Females only

  • Vector life stage affected: Adult

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Blocking_Config

JSON object

NA

NA

NA

Defines a waning effect that determines the probability that a host-seeking vector is blocked from reaching a human host. Blocked vectors are then elligble to be killed according to the Killing_Config parameter. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Blocking_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0,
        "class": "WaningEffectBox"
    }
}

Cost_To_Consumer

float

0

999999

3.75

The unit cost per bednet (unamortized)

 
{
    "Cost_To_Consumer": 4.5
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Insecticide_Name

string

NA

NA

UNINITIALIZED STRING

The name of the insecticide defined in the configuration parameter Insecticides for this intervention. If insecticides are being used, this must be defined as one of those values; if they are not being used this can be empty. It cannot have a value if you did not configure the configuration parameter Insecticides.

 
{
    "Insecticide_Name": "carbamate"
}

Intervention_Name

string

NA

NA

SimpleBednet

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "SimpleBednet",
        "Intervention_Name": "Northern region bednet distribution"
    }
}

Killing_Config

JSON object

NA

NA

NA

Defines a waning effect that determines the probability that a blocked vector is killed due to the intervention. Killing is conditional on being blocked: only blocked vectors are able to be killed. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Killing_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0.53429,
        "class": "WaningEffectBox"
    }
}

New_Property_Value

string

NA

NA

NA

An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Repelling_Config

JSON object

NA

NA

NA

Defines a waning effect that determines the probability that a vector is repelled due to the intervention. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Repelling_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0,
        "class": "WaningEffectBox"
    }
}

Usage_Config

JSON object

NA

NA

NA

The user-defined WaningEffects to determine when and if an individual is using a bed net. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Usage_Config": {
        "class": "WaningEffectConstant",
        "Initial_Effect": 1.0
    }
}

{
    "Events": [{
        "class": "CampaignEvent",
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 1460,
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Target_Demographic": "Everyone",
            "Demographic_Coverage": 0.7,
            "Intervention_Config": {
                "class": "SimpleBednet",
                "Cost_To_Consumer": 3.75,
                "Repelling_Config": {
                    "Initial_Effect": 0.1,
                    "Decay_Time_Constant": 730,
                    "class": "WaningEffectExponential"
                },
                "Killing_Config": {
                    "Initial_Effect": 0.6,
                    "Decay_Time_Constant": 1460,
                    "class": "WaningEffectExponential"
                },
                "Blocking_Config": {
                    "Initial_Effect": 0.9,
                    "Decay_Time_Constant": 730,
                    "class": "WaningEffectExponential"
                },
                "Usage_Config": {
                    "class": "WaningEffectConstant",
                    "Initial_Effect": 1.0
                }
            }
        }
    }],
    "Use_Defaults": 1
}
SimpleBoosterVaccine

The SimpleBoosterVaccine intervention class is derived from SimpleVaccine and preserves many of the same parameters. The behavior is much like SimpleVaccine, except that upon distribution and successful take, the vaccine’s effect is determined by the recipient’s immune state. If the recipient’s immunity modifier in the corresponding channel (acquisition, transmission, or mortality) is above a user-specified threshold, then the vaccine’s initial effect will be equal to the corresponding priming parameter. If the recipient’s immune modifier is below this threshold, then the vaccine’s initial effect will be equal to the corresponding boosting parameter. After distribution, the effect wanes, just like SimpleVaccine. In essence, this intervention provides a SimpleVaccine intervention with one effect to all naive (below- threshold) individuals, and another effect to all primed (above-threshold) individuals; this behavior is intended to mimic biological priming and boosting.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Boost_Effect

float

0

1

1

Specifies the boosting effect on [acquisition/transmission/mortality] immunity for previously exposed individuals (either natural or vaccine-derived). This does not replace current immunity, it builds multiplicatively on top of it.

 
{
    "Intervention_Config": {
        "Cost_To_Consumer": 10.0,
        "Vaccine_Take": 1,
        "Vaccine_Type": "MortalityBlocking",
        "Prime_Effect": 0.25,
        "Boost_Effect": 0.45,
        "Boost_Threshold": 0.0,
        "Waning_Config": {
            "Box_Duration": 10,
            "Initial_Effect": 1,
            "class": "WaningEffectBox"
        },
        "class": "SimpleBoosterVaccine"
    }
}

Boost_Threshold

float

0

1

0

Specifies how much immunity is required before the vaccine changes from a priming effect to a boosting effect.

 
{
    "Intervention_Config": {
        "Cost_To_Consumer": 10.0,
        "Vaccine_Take": 1,
        "Vaccine_Type": "MortalityBlocking",
        "Prime_Effect": 0.25,
        "Boost_Effect": 0.45,
        "Boost_Threshold": 0.0,
        "Waning_Config": {
            "Box_Duration": 10,
            "Initial_Effect": 1,
            "class": "WaningEffectBox"
        },
        "class": "SimpleBoosterVaccine"
    }
}

Cost_To_Consumer

float

0

999999

10

The unit cost per vaccine (unamortized).

 
{
    "Cost_To_Consumer": 10.0
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Efficacy_Is_Multiplicative

boolean

NA

NA

1

The overall vaccine efficacy when individuals receive more than one vaccine. When set to true (1), the vaccine efficacies are multiplied together; when set to false (0), the efficacies are additive.

 
{
    "Intervention_Config": {
        "class": "SimpleVaccine",
        "Cost_To_Consumer": 10,
        "Vaccine_Type": "AcquisitionBlocking",
        "Vaccine_Take": 1,
        "Efficacy_Is_Multiplicative": 0,
        "Waning_Config": {
            "class": "WaningEffectConstant",
            "Initial_Effect": 0.3
        }
    }
}

Intervention_Name

string

NA

NA

SimpleBoosterVaccine

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "SimpleBoosterVaccine",
        "Intervention_Name": "Tetanus booster shot"
    }
}

New_Property_Value

string

NA

NA

NA

An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Prime_Effect

float

0

1

1

Specifies the priming effect on [acquisition/transmission/mortality] immunity for naive individuals (without natural or vaccine-derived immunity).

 
{
    "Intervention_Config": {
        "Cost_To_Consumer": 10.0,
        "Vaccine_Take": 1,
        "Vaccine_Type": "MortalityBlocking",
        "Prime_Effect": 0.25,
        "Boost_Effect": 0.45,
        "Boost_Threshold": 0.0,
        "Waning_Config": {
            "Box_Duration": 10,
            "Initial_Effect": 1,
            "class": "WaningEffectBox"
        },
        "class": "SimpleBoosterVaccine"
    }
}

Vaccine_Take

float

0

1

1

The rate at which delivered vaccines will successfully stimulate an immune response and achieve the desired efficacy. For example, if it is set to 0.9, there will be a 90 percent chance that the vaccine will start with the specified efficacy, and a 10 percent chance that it will have no efficacy at all.

 
{
    "Intervention_Config": {
        "class": "SimpleVaccine",
        "Cost_To_Consumer": 10,
        "Vaccine_Type": "AcquisitionBlocking",
        "Vaccine_Take": 1,
        "Efficacy_Is_Multiplicative": 0,
        "Waning_Config": {
            "class": "WaningEffectConstant",
            "Initial_Effect": 0.3
        }
    }
}

Vaccine_Type

enum

NA

NA

Generic

The type of vaccine to distribute in a vaccine intervention. Possible values are:

Generic

The vaccine can reduce transmission, acquisition, and mortality.

TransmissionBlocking

The vaccine will reduce pathogen transmission.

AcquisitionBlocking

The vaccine will reduce the acquisition of the pathogen by reducing the force of infection experienced by the vaccinated individual.

MortalityBlocking

The vaccine reduces the disease-mortality rate of a vaccinated individual.

 
{
    "Intervention_Config": {
        "class": "SimpleVaccine",
        "Cost_To_Consumer": 10,
        "Vaccine_Type": "AcquisitionBlocking",
        "Vaccine_Take": 1,
        "Efficacy_Is_Multiplicative": 0,
        "Waning_Config": {
            "class": "WaningEffectConstant",
            "Initial_Effect": 0.3
        }
    }
}

Waning_Config

JSON object

NA

NA

NA

The configuration of how the intervention efficacy wanes over time. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Waning_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 1,
        "class": "WaningEffectBox"
    }
}

{
    "Use_Defaults": 1,
    "Campaign_Name": "Generic Seattle Regression Campaign",
    "Events": [{
        "class": "CampaignEvent",
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 20,
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Target_Demographic": "Everyone",
            "Demographic_Coverage": 1.0,
            "Intervention_Config": {
                "class": "SimpleBoosterVaccine",
                "Cost_To_Consumer": 10.0,
                "Vaccine_Take": 1,
                "Vaccine_Type": "MortalityBlocking",
                "Prime_Effect": 0.25,
                "Boost_Effect": 0.45,
                "Boost_Threshold": 0.0,
                "Waning_Config": {
                    "Box_Duration": 10,
                    "Initial_Effect": 1,
                    "class": "WaningEffectBox"
                }
            }
        }
    }]
}
SimpleDiagnostic

The SimpleDiagnostic intervention class identifies infected individuals, regardless of disease state, based on specified diagnostic sensitivity and specificity. Diagnostics are a key component of modern disease control efforts, whether used to identify high-risk individuals, infected individuals, or drug resistance. This intervention class distributes a specified intervention to a fraction of individuals who test positive.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Base_Sensitivity

float

0

1

1

The sensitivity of the diagnostic. This sets the proportion of the time that individuals with the condition being tested receive a positive diagnostic test. When set to zero, then individuals who have the condition always receive a false-negative diagnostic test.

 
{
    "Base_Sensitivity": 0.8
}

Base_Specificity

float

0

1

1

The specificity of the diagnostic. This sets the proportion of the time that individuals without the condition being tested receive a negative diagnostic test. When set to 1, the diagnostic always accurately reflects the lack of having the condition. When set to zero, then individuals who do not have the condition always receive a false-positive diagnostic test.

 
{
    "Base_Specificity": 0.9
}

Cost_To_Consumer

float

0

3.40E+38

1

The unit ‘cost’ assigned to the diagnostic. Setting Cost_To_Consumer to zero for all other interventions, and to a non-zero amount for one intervention, provides a convenient way to track the number of times the intervention has been applied in a simulation.

 
{
    "Cost_To_Consumer": 0.333
}

Days_To_Diagnosis

float

0

3.40E+38

0

Diagnosis occurs when the intervention is distributed; this parameter governs the time (in days) from positive diagnosis to action on that diagnosis.

 
{
    "Days_To_Diagnosis": 0.0
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Enable_Is_Symptomatic

boolean

NA

NA

0

If true (1), requires an infection to be symptomatic to return a positive test.

 
{
    "Enable_Is_Symptomatic": 1,
    "Base_Specificity": 0.85,
    "Base_Sensitivity": 0.92
}

Event_Or_Config

enum

NA

NA

Config

Specifies whether the current intervention (or a positive diagnosis, depending on the intervention class) distributes a nested intervention (the Config option) or an event will be broadcast which may trigger other interventions in the campaign file (the Event option). Possible values are:

  • Event

  • Config

 
{
    "Event_Or_Config": "Config"
}

Intervention_Name

string

NA

NA

SimpleDiagnostic

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Name": "Diagnostic_Sample"
}

New_Property_Value

string

NA

NA

NA

An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Positive_Diagnosis_Config

JSON object

NA

NA

NA

The intervention distributed to individuals if they test positive. Only used when Event_Or_Config is set to Config.

 
{
    "Positive_Diagnosis_Config": {
        "class": "SimpleVaccine",
        "Cost_To_Consumer": 0.333,
        "Secondary_Decay_Time_Constant": 1,
        "Vaccine_Take": 1,
        "Vaccine_Type": "AcquisitionBlocking",
        "Waning_Config": {
            "Box_Duration": 3650,
            "Initial_Effect": 0.1,
            "class": "WaningEffectBox"
        }
    }
}

Positive_Diagnosis_Event

enum

NA

NA

NA

If the test is positive, this specifies an event that can trigger another intervention when the event occurs. Only used if Event_Or_Config is set to Event. See Event list for possible values.

 
{
    "Intervention_Config": {
        "class": "SimpleDiagnostic",
        "Base_Sensitivity": 1.0,
        "Base_Specificity": 1.0,
        "Cost_To_Consumer": 0.0,
        "Days_To_Diagnosis": 0.0,
        "Event_Or_Config": "Event",
        "Positive_Diagnosis_Event": "TestedPositive_CureMeNow",
        "Treatment_Fraction": 1.0
    }
}

Treatment_Fraction

float

0

1

1

The fraction of positive diagnoses that are treated or given the Positive_Diagnosis_Config or Positive_Diagnosis_Event when used in campaign classes that trigger separate interventions or events upon a positive diagnosis.

 
{
    "Intervention_Config": {
        "class": "SimpleDiagnostic",
        "Base_Sensitivity": 1.0,
        "Base_Specificity": 1.0,
        "Cost_To_Consumer": 0.0,
        "Days_To_Diagnosis": 0.0,
        "Event_Or_Config": "Event",
        "Positive_Diagnosis_Event": "TestedPositive_CureMeNow",
        "Treatment_Fraction": 1.0
    }
}

{
    "Events":[{
            "class":"CampaignEvent",
            "Start_Day":200,
            "Nodeset_Config":{
                "class":"NodeSetAll"
            },
            "Event_Coordinator_Config":{
                "class":"StandardInterventionDistributionEventCoordinator",
                "Target_Demographic":"Everyone",
                "Demographic_Coverage": 1.0,
                "Intervention_Config":{
                    "class":"SimpleDiagnostic",
                    "Base_Sensitivity":1.0,
                    "Base_Specificity":1.0,
                    "Cost_To_Consumer":0,
                    "Days_To_Diagnosis":5.0,
                    "Dont_Allow_Duplicates":0,
                    "Event_Or_Config":"Event",
                    "Positive_Diagnosis_Event":"Acorn",
                    "Intervention_Name":"Diagnostic_Sample",
                    "Treatment_Fraction":1.0
                }
            }
        }],
    "Use_Defaults":1
}
SimpleHealthSeekingBehavior

The SimpleHealthSeekingBehavior intervention class models the time delay that typically occurs between when an individual experiences onset of symptoms and when they seek help from a health care provider. Several factors may contribute to such delays including accessibility, cost, and trust in the health care system. This intervention models this time delay as an exponential process; at every time step, the model draws randomly to determine if the individual will receive the specified intervention. As an example, this intervention can be nested in a NodeLevelHealthTriggeredIV so that when an individual is infected, he or she receives a SimpleHealthSeekingBehavior, representing that the individual will now seek care. The individual subsequently seeks care with an exponentially distributed delay and ultimately receives the specified intervention.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Actual_IndividualIntervention_Config

JSON object

NA

NA

NA

The configuration of an actual intervention sought. Selects a class for the intervention and configures the parameters specific for that intervention class.

 
{
    "Actual_IndividualIntervention_Config": {
        "Cost_To_Consumer": 1,
        "Drug_Type": "FirstLineCombo",
        "Durability_Profile": "FIXED_DURATION_CONSTANT_EFFECT",
        "Primary_Decay_Time_Constant": 180,
        "Remaining_Doses": 1,
        "Secondary_Decay_Time_Constant": 0,
        "TB_Drug_Cure_Rate": 0.2,
        "TB_Drug_Inactivation_Rate": 1e-09,
        "class": "AntiTBDrug"
    }
}

Actual_IndividualIntervention_Event

enum

NA

NA

NA

The event of an actual intervention sought. Selects a class for the intervention and configures the parameters specific for that intervention class. See Event list for possible values.

 
{
    "Actual_IndividualIntervention_Config": {
        "Actual_IndividualIntervention_Event": "ProviderOrdersTBTest",
        "Tendency": 0.005,
        "Event_Or_Config": "Event",
        "class": "SimpleHealthSeekingBehavior"
    }
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Event_Or_Config

enum

NA

NA

Config

Specifies whether the current intervention (or a positive diagnosis, depending on the intervention class) distributes a nested intervention (the Config option) or an event will be broadcast which may trigger other interventions in the campaign file (the Event option). Possible values are:

  • Event

  • Config

 
{
    "Event_Or_Config": "Config"
}

Intervention_Name

string

NA

NA

SimpleHealthSeekingBehavior

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "SimpleHealthSeekingBehavior",
        "Intervention_Name": "Return to clinic after treatment is unsuccessful"
    }
}

New_Property_Value

string

NA

NA

NA

An optional IndividualProperty key:value pair that will be assigned when the intervention is first updated. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Single_Use

boolean

NA

NA

1

If set to true (1), the health-seeking behavior gets used once and discarded. If set to false (0), it remains indefinitely.

 
{
    "Single_Use": 1
}

Tendency

float

0

1

1

The probability of seeking healthcare.

 
{
    "Tendency": 0.01
}

{
     "Use_Defaults": 1,
     "Events": [{
          "class": "CampaignEvent",
          "Event_Name": "Drugs after TB activation",
          "Nodeset_Config": {
               "class": "NodeSetAll"
          },
          "Start_Day": 9125,
          "Event_Coordinator_Config": {
               "class": "StandardInterventionDistributionEventCoordinator",
               "Number_Repetitions": 1,
               "Target_Demographic": "Everyone",
               "Demographic_Coverage": 1,
               "Intervention_Config": {
                    "class": "NodeLevelHealthTriggeredIV",
                    "Trigger_Condition_List": ["NewInfectionEvent"],
                    "Actual_IndividualIntervention_Config": {
                         "class": "SimpleHealthSeekingBehavior",
                         "Event_Or_Config": "Config",
                         "Tendency": 0.0015,
                         "Actual_IndividualIntervention_Config": {
                              "class": "AntiTBDrug",
                              "Cost_To_Consumer": 90,
                              "Drug_Type": "FirstLineCombo",
                              "Durability_Profile": "FIXED_DURATION_CONSTANT_EFFECT",
                              "Primary_Decay_Time_Constant": 180,
                              "Remaining_Doses": 1,
                              "Secondary_Decay_Time_Constant": 0
                         }
                    }
               }
          }
     }]
}
SimpleHousingModification

The SimpleHousingModification intervention class implements a generic housing modification for vector control. It is the base class from which other housing modifications are derived.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Cost_To_Consumer

float

0

999999

8

The unit cost per housing modification (unamortized).

 
{
    "Cost_To_Consumer": 10.0
}

Disqualifying_Properties

array of arrays

NA

NA

[]

A list of NodeProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to nodes with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

0

1

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Insecticide_Name

string

NA

NA

UNINITIALIZED STRING

The name of the insecticide defined in the configuration parameter Insecticides for this intervention. If insecticides are being used, this must be defined as one of those values; if they are not being used this can be empty. It cannot have a value if you did not configure the configuration parameter Insecticides.

 
{
    "Insecticide_Name": "carbamate"
}

Intervention_Name

string

NA

NA

SimpleHousingModification

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "SimpleHousingModification",
        "Intervention_Name": "Closing house eaves"
    }
}

Killing_Config

JSON object

NA

NA

NA

The configuration of killing efficacy and waning for housing modification. Killing is conditional on NOT repelling the mosquito before feeding. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Killing_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0.53429,
        "class": "WaningEffectBox"
    }
}

New_Property_Value

string

NA

NA

UNINITIALIZED STRING

An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Repelling_Config

JSON object

NA

NA

NA

Defines a waning effect that determines the probability that a vector is repelled due to the intervention. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Repelling_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0,
        "class": "WaningEffectBox"
    }
}

{
    "Events": [{
        "class": "CampaignEvent",
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 120,
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Target_Demographic": "Everyone",
            "Demographic_Coverage": 0.8,
            "Intervention_Config": {
                "class": "SimpleHousingModification",
                "Repelling_Config": {
                    "Box_Duration": 100,
                    "Decay_Time_Constant": 150,
                    "Initial_Effect": 0.6,
                    "class": "WaningEffectBoxExponential"
                },
                "Cost_To_Consumer": 1.0,
                "Killing_Config": {
                    "Box_Duration": 100,
                    "Decay_Time_Constant": 150,
                    "Initial_Effect": 0.4,
                    "class": "WaningEffectBoxExponential"
                }
            }
        }
    }],
    "Use_Defaults": 1
}
SimpleIndividualRepellent

The SimpleIndividualRepellent intervention class provides protection to individuals against both indoor-feeding and outdoor-feeding mosquito bites.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Cost_To_Consumer

float

0

999999

8

The unit cost per repellent (unamortized).

 
{
    "Intervention_Config": {
        "Blocking_Config": {
            "Box_Duration": 100,
            "Decay_Time_Constant": 150,
            "Initial_Effect": 0.8,
            "class": "WaningEffectBoxExponential"
        },
        "Cost_To_Consumer": 10.0,
        "class": "SimpleIndividualRepellent"
    }
}

Disqualifying_Properties

string

NA

NA

NA

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

0

1

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Insecticide_Name

string

NA

NA

UNINITIALIZED STRING

The name of the insecticide defined in the configuration parameter Insecticides for this intervention. If insecticides are being used, this must be defined as one of those values; if they are not being used this can be empty. It cannot have a value if you did not configure the configuration parameter Insecticides.

 
{
    "Insecticide_Name": "carbamate"
}

Intervention_Name

string

NA

NA

SimpleIndividualRepellent

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "SimpleIndividualRepellent",
        "Intervention_Name": "Individual DEET usage"
    }
}

New_Property_Value

string

NA

NA

“”

An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Repelling_Config

JSON object

NA

NA

NA

Defines a waning effect that determines the probability that a vector is repelled due to the intervention. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Repelling_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0,
        "class": "WaningEffectBox"
    }
}

{
    "Events": [{
        "class": "CampaignEvent",
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 120,
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Target_Demographic": "Everyone",
            "Demographic_Coverage": 0.5,
            "Intervention_Config": {
                "class": "SimpleIndividualRepellent",
                "Repelling_Config": {
                    "Box_Duration": 100,
                    "Decay_Time_Constant": 150,
                    "Initial_Effect": 0.8,
                    "class": "WaningEffectBoxExponential"
                },
                "Cost_To_Consumer": 10.0
            }
        }
    }],
    "Use_Defaults": 1
}
SimpleVaccine

The SimpleVaccine intervention class implements vaccine campaigns in the simulation. Vaccines can have an effect on one of the following:

  • Reduce the likelihood of acquiring an infection

  • Reduce the likelihood of transmitting an infection

  • Reduce the likelihood of death

To configure vaccines that have an effect on more than one of these, use MultiEffectVaccine instead.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Cost_To_Consumer

float

0

999999

10

The unit cost per vaccine (unamortized).

 
{
    "Cost_To_Consumer": 10.0
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Efficacy_Is_Multiplicative

boolean

NA

NA

1

The overall vaccine efficacy when individuals receive more than one vaccine. When set to true (1), the vaccine efficacies are multiplied together; when set to false (0), the efficacies are additive.

 
{
    "Intervention_Config": {
        "class": "SimpleVaccine",
        "Cost_To_Consumer": 10,
        "Vaccine_Type": "AcquisitionBlocking",
        "Vaccine_Take": 1,
        "Efficacy_Is_Multiplicative": 0,
        "Waning_Config": {
            "class": "WaningEffectConstant",
            "Initial_Effect": 0.3
        }
    }
}

Intervention_Name

string

NA

NA

SimpleVaccine

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "SimpleVaccine",
        "Intervention_Name": "Routine MMR vaccination"
    }
}

New_Property_Value

string

NA

NA

NA

An optional IndividualProperty key:value pair that will be assigned when the intervention is first updated. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Vaccine_Take

float

0

1

1

The rate at which delivered vaccines will successfully stimulate an immune response and achieve the desired efficacy. For example, if it is set to 0.9, there will be a 90 percent chance that the vaccine will start with the specified efficacy, and a 10 percent chance that it will have no efficacy at all.

 
{
    "Intervention_Config": {
        "class": "SimpleVaccine",
        "Cost_To_Consumer": 10,
        "Vaccine_Type": "AcquisitionBlocking",
        "Vaccine_Take": 1,
        "Efficacy_Is_Multiplicative": 0,
        "Waning_Config": {
            "class": "WaningEffectConstant",
            "Initial_Effect": 0.3
        }
    }
}

Vaccine_Type

enum

NA

NA

Generic

The type of vaccine to distribute in a vaccine intervention. Possible values are:

Generic

The vaccine can reduce transmission, acquisition, and mortality.

TransmissionBlocking

The vaccine will reduce pathogen transmission.

AcquisitionBlocking

The vaccine will reduce the acquisition of the pathogen by reducing the force of infection experienced by the vaccinated individual.

MortalityBlocking

The vaccine reduces the disease-mortality rate of a vaccinated individual.

 
{
    "Intervention_Config": {
        "class": "SimpleVaccine",
        "Cost_To_Consumer": 10,
        "Vaccine_Type": "AcquisitionBlocking",
        "Vaccine_Take": 1,
        "Efficacy_Is_Multiplicative": 0,
        "Waning_Config": {
            "class": "WaningEffectConstant",
            "Initial_Effect": 0.3
        }
    }
}

Waning_Config

JSON object

NA

NA

NA

The configuration of how the intervention efficacy wanes over time. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Waning_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 1,
        "class": "WaningEffectBox"
    }
}

{
    "Events": [{
        "class": "CampaignEvent",
        "Nodeset_Config": {
            "class": "NodeSetAll"
        },
        "Start_Day": 60,
        "Event_Coordinator_Config": {
            "class": "StandardInterventionDistributionEventCoordinator",
            "Target_Demographic": "Everyone",
            "Demographic_Coverage": 0.5,
            "Intervention_Config": {
                "class": "SimpleVaccine",
                "Cost_To_Consumer": 10.0,
                "Vaccine_Take": 1,
                "Vaccine_Type": "AcquisitionBlocking",
                "Waning_Config": {
                    "Box_Duration": 3650,
                    "Initial_Effect": 1,
                    "class": "WaningEffectBox"
                }
            }
        }
    }],
    "Use_Defaults": 1
}
SpatialRepellentHousingModification

The SpatialRepellentHousingModification intervention class is a housing modification utilizing spatial repellents. The protection provided by this intervention is exclusively against indoor-biting mosquitoes.

Warning

EMOD simulations models nodes and individuals within nodes; they do not model houses. Therefore, housing modifications are received by individuals, not houses.

Use of this class and other housing modification classes requires caution because they can have unintended effects. For example, individuals in the same household may receive different housing modification interventions. An individual receiving a housing modification intervention who then migrates to another node will continue to receive that intervention. We recommend that you configure your simulation to take these logical inconsistencies into account.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Cost_To_Consumer

float

0

999999

8

The unit cost per housing modification (unamortized).

 
{
    "Cost_To_Consumer": 10.0
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Insecticide_Name

string

NA

NA

UNINITIALIZED STRING

The name of the insecticide defined in <config.Insecticides> for this intervention. If insecticides are being used, this must be defined as one of those values; if they are not being used this can be empty. It cannot have a value if you did not configure <config.Insecticides>.

 
{
    "Insecticide_Name": "carbamate"
}

Intervention_Name

string

NA

NA

SpatialRepellentHousingModification

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "SpatialRepellentHousingModification",
        "Intervention_Name": "Pyrethroid repellent"
    }
}

New_Property_Value

string

NA

NA

“

An optional IndividualProperty key:value pair that will be assigned when the intervention is first updated. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Repelling_Config

JSON object

NA

NA

NA

Defines a waning effect that determines the probability that a vector is repelled due to the intervention. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Repelling_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0,
        "class": "WaningEffectBox"
    }
}

{
    "Events": [
        {
            "class": "CampaignEvent",
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Start_Day": 120,
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Target_Demographic": "Everyone",
                "Demographic_Coverage": 0.8,
                "Intervention_Config": {
                    "class": "SpatialRepellentHousingModification",
                    "Cost_To_Consumer": 1.0,
                    "Repelling_Config": {
                        "Box_Duration": 100,
                        "Decay_Time_Constant": 150,
                        "Initial_Effect": 0.1,
                        "class": "WaningEffectBoxExponential"
                    }
                }
            }
        }
    ],
    "Use_Defaults": 1
}
StandardDiagnostic

The StandardDiagnostic intervention class identifies infected individuals, regardless of disease state, based on specified diagnostic sensitivity and specificity. This the same as SimpleDiagnostic except that it allows you to specify an action upon receiving a negative diagnostic test result. Diagnostics are a key component of modern disease control efforts, whether used to identify high-risk individuals, infected individuals, or drug resistance. The individual receives the diagnostic test immediately when the intervention is distributed, but there may be a delay in receiving a positive result. This intervention class distributes a specified intervention to a fraction of individuals who test positive.

This class is similar to SimpleDiagnostic but allows you to specify an action upon receipt of a negative diagnosis.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Base_Sensitivity

float

0

1

1

The sensitivity of the diagnostic. This sets the proportion of the time that individuals with the condition being tested receive a positive diagnostic test. When set to zero, then individuals who have the condition always receive a false-negative diagnostic test.

 
{
    "Base_Sensitivity": 0.8
}

Base_Specificity

float

0

1

1

The specificity of the diagnostic. This sets the proportion of the time that individuals without the condition being tested receive a negative diagnostic test. When set to 1, the diagnostic always accurately reflects the lack of having the condition. When set to zero, then individuals who do not have the condition always receive a false-positive diagnostic test.

 
{
    "Base_Specificity": 0.9
}

Cost_To_Consumer

float

0

3.40282e+38

1

The unit cost assigned to the diagnostic. Setting Cost_To_Consumer to zero for all other interventions, and to a non-zero amount for one intervention, provides a convenient way to track the number of times the intervention has been applied in a simulation.

 
{
    "Cost_To_Consumer": 0.333
}

Days_To_Diagnosis

float

0

3.40282e+38

0

Diagnosis occurs when the intervention is distributed; this parameter governs the time (in days) from positive diagnosis to action on that diagnosis; action on negative diagnosis occurs immediately.

 
{
    "Days_To_Diagnosis": 0.0
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

0

1

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Enable_Is_Symptomatic

boolean

NA

NA

0

If true (1), requires an infection to be symptomatic to return a positive test.

 
{
    "Enable_Is_Symptomatic": 1,
    "Base_Specificity": 0.85,
    "Base_Sensitivity": 0.92
}

Event_Or_Config

enum

NA

NA

Config

Specifies whether the current intervention (or a positive diagnosis, depending on the intervention class) distributes a nested intervention (the Config option) or an event will be broadcast which may trigger other interventions in the campaign file (the Event option). Possible values are:

  • Event

  • Config

 
{
    "Event_Or_Config": "Config"
}

Intervention_Name

string

NA

NA

NA

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Name": "Diagnostic_Sample"
}

Negative_Diagnosis_Config

JSON object

NA

NA

NA

If Event_Or_Config is set to Config, this is the intervention given out when there is a negative diagnosis.

 
{
    "Event_Or_Config": "Config",
    "Negative_Diagnosis_Config": {
        "class": "DelayedIntervention"
        "Coverage": 1,
        "Delay_Period_Distribution": "FIXED_DURATION",
        "Delay_Period_Fixed": 250,
    }
}

Negative_Diagnosis_Event

string

NA

NA

NA

If Event_Or_Config is set to Event, this defines the event that is broadcast upon a negative test result. See Event list for possible values.

 
{
    "Event_Or_Config": "Event",
    "Negative_Diagnosis_Event": "TestedNegative_IamSusceptible"
}

New_Property_Value

string

NA

NA

NA

An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Positive_Diagnosis_Config

JSON object

NA

NA

NA

The intervention distributed to individuals if they test positive. Only used when Event_Or_Config is set to Config.

 
{
    "Event_Or_Config": "Config",
    "Positive_Diagnosis_Config": {
        "class": "SimpleVaccine",
        "Cost_To_Consumer": 0.333,
        "Secondary_Decay_Time_Constant": 1,
        "Vaccine_Take": 1,
        "Vaccine_Type": "AcquisitionBlocking",
        "Waning_Config": {
            "Box_Duration": 3650,
            "Initial_Effect": 0.1,
            "class": "WaningEffectBox"
        }
    }
}

Positive_Diagnosis_Event

enum

NA

NA

NA

If the test is positive, this specifies an event that can trigger another intervention when the event occurs. Only used if Event_Or_Config is set to Event. See Event list for possible values.

 
{
    "Intervention_Config": {
        "Base_Sensitivity": 1.0,
        "Base_Specificity": 1.0,
        "Cost_To_Consumer": 0.0,
        "Days_To_Diagnosis": 0.0,
        "Event_Or_Config": "Event",
        "Positive_Diagnosis_Event": "TestedPositive_CureMeNow",
        "Treatment_Fraction": 1.0,
        "class": "StandardDiagnostic"
    }
}

Treatment_Fraction

float

0

1

1

The fraction of positive diagnoses that are treated.

 
{
    "Intervention_Config": {
        "Base_Sensitivity": 1.0,
        "Base_Specificity": 1.0,
        "Cost_To_Consumer": 0.0,
        "Days_To_Diagnosis": 0.0,
        "Event_Or_Config": "Event",
        "Positive_Diagnosis_Event": "TestedPositive_CureMeNow",
        "Treatment_Fraction": 1.0,
        "class": "StandardDiagnostic"
    }
}

{
    "Events":[{
            "class":"CampaignEvent",
            "Start_Day":200,
            "Nodeset_Config":{
                "class":"NodeSetAll"
            },
            "Event_Coordinator_Config":{
                "class":"StandardInterventionDistributionEventCoordinator",
                "Target_Demographic":"Everyone",
                "Demographic_Coverage": 1.0,
                "Intervention_Config":{
                    "class":"SimpleDiagnostic",
                    "Base_Sensitivity":1.0,
                    "Base_Specificity":1.0,
                    "Cost_To_Consumer":0,
                    "Days_To_Diagnosis":5.0,
                    "Dont_Allow_Duplicates":0,
                    "Event_Or_Config":"Event",
                    "Positive_Diagnosis_Event":"Acorn",
                    "Intervention_Name":"Diagnostic_Sample",
                    "Treatment_Fraction":1.0
                }
            }
        }],
    "Use_Defaults":1
}
UsageDependentBednet

The UsageDependentBednet intervention class is similar to SimpleBednet, as it distributes insecticide-treated nets to individuals in the simulation. However, bednet ownership and bednet usage are distinct in this intervention. As in SimpleBednet, net ownership is configured through the demographic coverage, and the blocking and killing rates of mosquitoes are time-dependent. Use of bednets is age-dependent and can vary seasonally. Once a net has been distributed to someone, the net usage is determined by the product of the seasonal and age-dependent usage probabilities until the net-retention counter runs out, and the net is discarded.

While SimpleBednet usage is applied as a daily reduction in efficacy, UsageDependentBednet uses the usage efficacy to determine whether or not the person used the net that day. For example, if we look at bednet usage with 0 repelling but 100% blocking and killing effects and 50% usage, the person with the SimpleBednet will have a net that blocks and kills 50% each day and the person with the UsageDependentBednet will have half of their days with a perfectly blocking and killing net and half of their days with no net at all. Note that when a person migrates, they take their net with them.

At a glance:

  • Distributed to: Individuals

  • Serialized: Yes, if this has already been distributed to a person.

  • Uses insecticides: Yes. It can target sub-groups using genomes, especially if you want to target specific species.

  • Time-based expiration: Yes, an expiration timer that is independent from the waning effects can be configured.

  • Purge existing: Yes. If a new intervention is added to to the individual, the existing intervention of the same name is removed when the new one is added. Note that it’s possible to have multiple UsageDependentBednets interventions attached to a person if they have different Intervention_Name values.

  • Vector killing contributes to: Indoor Die Before/After Feeding

  • Vector effects: Repelling, blocking, and killing

  • Vector sexes affected: Females only

  • Vector life stage affected: Adult

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Blocking_Config

JSON object

NA

NA

NA

Configures the rate of blocking for indoor mosquito feeds on individuals with an ITN. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Blocking_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0,
        "class": "WaningEffectBox"
    }
}

Cost_To_Consumer

float

0

999999

3.75

The unit cost per bednet (unamortized)

 
{
    "Cost_To_Consumer": 4.5
}

Discard_Event

enum

NA

NA

NA

The event that is broadcast when an individual discards their bed net, either by replacing an existing net or due to the expiration timer. See Event list for possible values, or you can use a custom event (defined in the Custom_Individudual_Events list in the config file).

 
{
    "Received_Event": "Bednet_Got_New_One",
    "Using_Event": "Bednet_Using",
    "Discard_Event": "Bednet_Discarded",
    "Expiration_Distribution_Type": "FIXED_DURATION",
    "Expiration_Period": 50
}

Disqualifying_Properties

array of strings

NA

NA

[]

A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.

 
{
    "Disqualifying_Properties": [
        "InterventionStatus:LostForever"
    ]
}

Dont_Allow_Duplicates

boolean

NA

NA

0

If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.

 
{
    "Dont_Allow_Duplicates": 0
}

Expiration_Period_Constant

float

0

3.40282E+38

-1

The expiration period to use for all bednets, in days, when Expiration_Period_Distribution is set to CONSTANT_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "CONSTANT_DISTRIBUTION",
    "Expiration_Period_Constant": 8
}

Expiration_Period_Distribution

enum

NA

NA

NOT_INITIALIZED

The distribution type to use for assigning the expiration period to a usage-dependent bednet. Each assigned value is a random draw from the distribution.

Possible values are:

NOT_INITIALIZED

No distribution set.

CONSTANT_DISTRIBUTION

Use the same value for each individual. Set Expiration_Period_Constant.

UNIFORM_DISTRIBUTION

Use a uniform distribution with a given minimum and maximum. Set Expiration_Period_Max and Expiration_Period_Min.

GAUSSIAN_DISTRIBUTION

The distribution is Gaussian (or normal). Values are resampled to ensure >= 0. Set Expiration_Period_Gaussian_Mean and Expiration_Period_Gaussian_Std_Dev.

EXPONENTIAL_DISTRIBUTION

The distribution is exponential with a given mean. Set Expiration_Period_Exponential.

WEIBULL_DISTRIBUTION

Use a Weibull distribution with a given shape and scale. Set Expiration_Period_Kappa and Expiration_Period_Lambda.

LOG_NORMAL_DISTRIBUTION

Use a log-normal distribution with a given mean and width. Set Expiration_Period_Log_Normal_Mu and Expiration_Period_Log_Normal_Sigma.

POISSON_DISTRIBUTION

Use a Poisson distribution with a given mean. Set Expiration_Period_Poisson_Mean.

DUAL_CONSTANT_DISTRIBUTION

Use a distribution where some individuals are set to a value of zero and the rest to a given value. Set Expiration_Period_Proportion_0 and Peak_2_Value. This distribution does not use the parameters set for CONSTANT_DISTRIBUTION.

DUAL_EXPONENTIAL_DISTRIBUTION

Use two exponential distributions with given means. Set Expiration_Period_Mean_1, Expiration_Period_Mean_2, and Expiration_Period_Proportion_1. This distribution does not use the parameters set for EXPONENTIAL_DISTRIBUTION.

 
{
    "Duration_Before_Leaving_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Duration_Before_Leaving_Log_Normal_Mu": 9,
    "Duration_Before_Leaving_Log_Normal_Sigma": 2
}

Expiration_Period_Exponential

float

0

3.40282E+38

-1

The mean of the expiration period, in days, when Expiration_Period_Distribution is set to EXPONENTIAL_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "EXPONENTIAL_DISTRIBUTION",
    "Expiration_Period_Exponential": 4.25
}

Expiration_Period_Gaussian_Mean

float

0

3.40282E+38

-1

The mean of the expiration period, in days, when Expiration_Period_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Expiration_Period_Gaussian_Mean": 8,
    "Expiration_Period_Gaussian_Std_Dev": 1.5
}

Expiration_Period_Gaussian_Std_Dev

float

1.17549E-38

3.40282E+38

-1

The standard deviation of the expiration period, in days, when Expiration_Period_Distribution is set to GAUSSIAN_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "GAUSSIAN_DISTRIBUTION",
    "Expiration_Period_Gaussian_Mean": 8,
    "Expiration_Period_Gaussian_Std_Dev": 1.5
}

Expiration_Period_Kappa

float

1.17549E-38

3.40282E+38

-1

The shape value for the expiration period, in days, when Expiration_Period_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "WEIBULL_DISTRIBUTION",
    "Expiration_Period_Kappa": 0.9,
    "Expiration_Period_Lambda": 1.5
}

Expiration_Period_Lambda

float

1.17549E-38

3.40282E+38

-1

The scale value for the expiration period, in days, when Expiration_Period_Distribution is set to WEIBULL_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "WEIBULL_DISTRIBUTION",
    "Expiration_Period_Kappa": 0.9,
    "Expiration_Period_Lambda": 1.5
}

Expiration_Period_Log_Normal_Mu

float

-3.40282e+38

1.70141e+38

3.40282E+38

The mean of the expiration period, in days, when Expiration_Period_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Expiration_Period_Log_Normal_Mu": 9,
    "Expiration_Period_Log_Normal_Sigma": 2
}

Expiration_Period_Log_Normal_Sigma

float

-3.40282e+38

1.70141e+38

3.40282E+38

The width of the expiration period, in days, when Expiration_Period_Distribution is set to LOG_NORMAL_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "LOG_NORMAL_DISTRIBUTION",
    "Expiration_Period_Log_Normal_Mu": 9,
    "Expiration_Period_Log_Normal_Sigma": 2
}

Expiration_Period_Max

float

0

3.40282E+38

-1

The maximum expiration period, in days, when Expiration_Period_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "UNIFORM_DISTRIBUTION",
    "Expiration_Period_Min": 2,
    "Expiration_Period_Max": 7
}

Expiration_Period_Mean_1

float

1.17549E-38

3.40282E+38

-1

The mean of the first exponential distribution, in days, when Expiration_Period_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Expiration_Period_Mean_1": 4,
    "Expiration_Period_Mean_2": 12,
    "Expiration_Period_Proportion_1": 0.2
}

Expiration_Period_Mean_2

float

1.17549E-38

3.40282E+38

-1

The mean of the second exponential distribution, in days, when Expiration_Period_Distribution is set to DUAL_EXPONENTIAL_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Expiration_Period_Mean_1": 4,
    "Expiration_Period_Mean_2": 12,
    "Expiration_Period_Proportion_1": 0.2
}

Expiration_Period_Min

float

0

3.40282E+38

-1

The minimum expiration period, in days, when Expiration_Period_Distribution is set to UNIFORM_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "UNIFORM_DISTRIBUTION",
    "Expiration_Period_Min": 2,
    "Expiration_Period_Max": 7
}

Expiration_Period_Peak_2_Value

float

0

3.40282E+38

-1

The expiration period value to assign to the remaining bednets, in days, when Expiration_Period_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Expiration_Period_Proportion_0": 0.25,
    "Expiration_Period_Peak_2_Value": 5
}

Expiration_Period_Poisson_Mean

float

0

3.40282E+38

-1

The mean of the expiration period, in days, when Expiration_Period is set to POISSON_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "POISSON_DISTRIBUTION",
    "Expiration_Period_Poisson_Mean": 5
}

Expiration_Period_Proportion_0

float

0

1

-1

The proportion of bednets to assign a value of zero days until expiration when Expiration_Period_Distribution is set to DUAL_CONSTANT_DISTRIBUTION.

 
{
    "Expiration_Period_Distribution": "DUAL_CONSTANT_DISTRIBUTION",
    "Expiration_Period_Proportion_0": 0.25,
    "Expiration_Period_Peak_2_Value": 5
}

Expiration_Period_Proportion_1

float

0

1

-1

The proportion of bednets in the first exponential distribution.

 
{
    "Expiration_Period_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
    "Expiration_Period_Mean_1": 4,
    "Expiration_Period_Mean_2": 12,
    "Expiration_Period_Proportion_1": 0.2
}

Insecticide_Name

string

NA

NA

UNINITIALIZED STRING

The name of the insecticide defined in <config.Insecticides> for this intervention. If insecticides are being used, this must be defined as one of those values; if they are not being used this can be empty. It cannot have a value if you did not configure <config.Insecticides>.

 
{
    "Insecticide_Name": "carbamate"
}

Intervention_Name

string

NA

NA

UsageDependentBednet

The optional name used to refer to this intervention as a means to differentiate it from others that use the same class.

 
{
    "Intervention_Config": {
        "class": "UsageDependentBednet",
        "Intervention_Name": "IRS bednet that wears over time"
    }
}

Killing_Config

JSON object

NA

NA

NA

The configuration of the rate at which mosquitoes die, conditional on a successfully blocked feed. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Killing_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0.53429,
        "class": "WaningEffectBox"
    }
}

New_Property_Value

string

NA

NA

NA

An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one.

 
{
    "New_Property_Value": "InterventionStatus:None"
}

Received_Event

enum

NA

NA

NA

This parameter broadcasts when a new net is received, either the first net or a replacement net. See Event list for possible values, or you can use a custom event (defined in the Custom_Individudual_Events list in the config file).

 
{
    "Received_Event": "Bednet_Got_New_One",
    "Using_Event": "Bednet_Using",
    "Discard_Event": "Bednet_Discarded"
}

Repelling_Config

JSON object

NA

NA

NA

Defines a waning effect that determines the probability that a vector is repelled due to the intervention. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Repelling_Config": {
        "Box_Duration": 3650,
        "Initial_Effect": 0,
        "class": "WaningEffectBox"
    }
}

Usage_Config_List

array of JSON objects

NA

NA

NA

The list of WaningEffects whose effects are multiplied together to get the usage effect. Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Usage_Config_List": [
        {
            "class": "WaningEffectConstant",
            "Initial_Effect": 1.0
        }
    ]
}

Using_Event

enum

NA

NA

NA

This parameter broadcasts each time step in which a bed net is used. See Event list for possible values, or you can use a custom event (defined in the Custom_Individudual_Events list in the config file).

 
{
    "Received_Event": "Bednet_Got_New_One",
    "Using_Event": "Bednet_Using",
    "Discard_Event": "Bednet_Discarded"
}

{
    "Events": [{
            "class": "UsageDependentBednet",
            "Cost_To_Consumer": 5,
            "Repelling_Config": {
                "class": "WaningEffectConstant",
                "Initial_Effect": 0.0
            },
            "Blocking_Config": {
                "Initial_Effect": 1.0,
                "class": "WaningEffectConstant"
            },
            "Killing_Config": {
                "Initial_Effect": 0.5,
                "class": "WaningEffectConstant"
            },
            "Usage_Config_List": [{
                    "Durability_Map": {
                        "Times": [0.0, 12.99, 13.0, 125.0],
                        "Values": [0.0, 0.00, 1.0, 1.0]
                    },
                    "Initial_Effect": 1.0,
                    "class": "WaningEffectMapLinearAge"
                },
                {
                    "Durability_Map": {
                        "Times": [0.0, 20.0, 21.0, 30.0, 31.0, 365.0],
                        "Values": [1.0, 1.0, 0.0, 0.0, 1.0, 1.0]
                    },
                    "Initial_Effect": 1.0,
                    "class": "WaningEffectMapLinearSeasonal"
                }
            ],
            "Received_Event": "Bednet_Got_New_One",
            "Using_Event": "Bednet_Using",
            "Discard_Event": "Bednet_Discarded",
            "Expiration_Period_Distribution": "DUAL_EXPONENTIAL_DISTRIBUTION",
            "Expiration_Period_Mean_1": 60,
            "Expiration_Period_Mean_2": 50,
            "Expiration_Period_Proportion_1": 1.0
        }

    ],
    "Use_Defaults": 1
}
Waning effect classes

The following classes are nested within interventions (both individual-level and node-level) to indicate how their efficacy wanes over time. They can be used with several parameters including Blocking_Config, Killing_Config, and Waning_Config. Note that waning effect parameters do not control the overall duration of an intervention and are not assigned probabilistically.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

See the example below that uses a mix of different waning effect classes and the tables below that describe all parameters that can be used with each waning effect class.

{
    "Events": [
        {
            "class": "CampaignEvent",
            "Start_Day": 1,
            "Nodeset_Config": {
                "class": "NodeSetAll"
            },
            "Event_Coordinator_Config": {
                "class": "StandardInterventionDistributionEventCoordinator",
                "Target_Demographic": "Everyone",
                "Demographic_Coverage": 1.0,
                "Number_Repetitions": -1,
                "Timesteps_Between_Repetitions": 60,
                "Intervention_Config": {
                    "class": "SimpleBednet",
                    "Cost_To_Consumer": 5,
                    "Usage_Config": {
                        "class": "WaningEffectRandomBox",
                        "Initial_Effect": 1.0,
                        "Expected_Discard_Time" : 60
                    },
                    "Blocking_Config": {
                        "class": "WaningEffectExponential",                        
                        "Decay_Time_Constant": 150,
                        "Initial_Effect": 0.5
                    },
                    "Killing_Config": {
                        "class": "WaningEffectConstant",
                        "Initial_Effect": 1.0
                    }
                }
            }
        }
    ],
    "Use_Defaults": 1
}
WaningEffectBox

The efficacy is held at a constant rate until it drops to zero after the user-defined duration.

{
    "Intervention_Config": {
        "Blocking_Config": {
            "Box_Duration": 3650,
            "Initial_Effect": 0,
            "class": "WaningEffectBox"
        }
    }
}

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Box_Duration

float

0

100000

100

The box duration of the effect in days.

 
{
    "Box_Duration": 40
}

Initial_Effect

float

0

1

1

Strength of the effect, which remains constant until the duration is complete, as specified with the Box_Duration parameter for the WaningEffectBox class.

 
{
    "Waning_Config": {
        "Box_Duration": 10,
        "Initial_Effect": 0.75,
        "class": "WaningEffectBox"
    }
}
WaningEffectBoxExponential

The initial efficacy is held for a specified duration, then the efficacy decays at an exponential rate where the current effect is equal to Initial_Effect - dt/Decay_Time_Constant.

  {
    "Intervention_Config": {
        "Reduction_Config": {
            "class": "WaningEffectBoxExponential",
            "Box_Duration": 100,
            "Decay_Time_Constant": 150,
            "Initial_Effect": 0.1
        }
    }
  }

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Box_Duration

float

0

100000

100

The box duration of the effect in days.

 
{
    "Box_Duration": 3650,
    "Initial_Effect": 0.97,
    "Decay_Time_Constant": 50
}

Decay_Time_Constant

float

0

100000

100

The exponential decay length, in days, where the current effect is equal to Initial_Effect - dt/Decay_Time_Constant (dt equal to delta time).

 
{
    "Box_Duration": 3650,
    "Initial_Effect": 0.97,
    "Decay_Time_Constant": 50
}

Initial_Effect

float

0

1

1

The initial efficacy, held for a duration as specified with the Box_Duration parameter and then decaying at an exponential length (in days), where the current effect is equal to Initial_Effect - dt/Decay_Time_Constant (dt equal to delta time).

 
{
    "Box_Duration": 3650,
    "Initial_Effect": 0.97,
    "Decay_Time_Constant": 50
}
WaningEffectCombo

The WaningEffectCombo class is used within individual-level interventions and allows for specifiying a list of effects when the intervention only has one WaningEffect defined. These effects can be added or multiplied.

{
    "class" : "WaningEffectCombo",
    "Add_Effects" : 1,
    "Expires_When_All_Expire" :0,
    "Effect_List" : [
        {
            "class": "WaningEffectMapLinear",
            "Initial_Effect" : 1.0,
            "Expire_At_Durability_Map_End" : 1,
            "Durability_Map" :
            {
                "Times"  : [ 0.0, 1.0, 2.0 ],
                "Values" : [ 0.2, 0.4, 0.6 ]
            }
        },
        {
            "class": "WaningEffectBox",
            "Initial_Effect": 0.5,
            "Box_Duration" : 5.0
        }
    ]
}

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Initial_Effect

float

0

1

1

Strength of the effect, which remains constant when using with the WaningEffectConstant class.

 
{
    "Waning_Config": {
        "Initial_Effect": 0.65,
        "class": "WaningEffectConstant"
    }
}
WaningEffectConstant

The efficacy is held at a constant rate.

{
    "Intervention_Config": {
        "class": "SimpleBednet",
        "Killing_Config": {
            "class": "WaningEffectConstant",
            "Initial_Effect": 1.0
        }
    }
}

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Initial_Effect

float

0

1

1

Strength of the effect, which remains constant when using with the WaningEffectConstant class.

 
{
    "Waning_Config": {
        "Initial_Effect": 0.65,
        "class": "WaningEffectConstant"
    }
}
WaningEffectExponential

The efficacy decays at an exponential rate where the current effect is equal to Initial_Effect - dt/Decay_Time_Constant.

{
    "Intervention_Config": {
        "class": "SimpleBednet",
        "Blocking_Config": {
            "class": "WaningEffectExponential",            
            "Decay_Time_Constant": 150,
            "Initial_Effect": 0.5
        }
    }
}

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Decay_Time_Constant

float

0

100000

100

The exponential decay length, in days, where the current effect is equal to 1 - dt/Decay_Time_Constant (dt equal to delta time).

 
{
    "Decay_Time_Constant": 30,
    "Initial_Effect": 0.95
}

Initial_Effect

float

0

1

1

The initial efficacy, held for a specified duration (in days) as specified with the Box_Duration parameter. After this duration the efficacy decays at an exponential decay length (in days), where the current effect is equal to Initial_Effect - dt/Decay_Time_Constant (dt equal to delta time).

 
{
    "Waning_Config": {
        "Decay_Time_Constant": 0.87,
        "Initial_Effect": 0.9,
        "class": "WaningEffectExponential"
    }
}
WaningEffectMapCount

The WaningEffectMapCount class assigns a probability to a particular update of the effect. This typically used with AdherentDrug so one can assign a probability that a person takes a particular dose. The Times value should be integers starting from 1 and increasing.

If the example below were used in AdherentDrug, the probability that the person took the first dose would be 40%, 30% for the second dose, and so on.

{
    "Intervention_Config": {
        "class": "AdherentDrug",
        "Waning_Config": {
            "class": "WaningEffectMapCount",
            "Initial_Effect": 1.0,
            "Durability_Map": {
                "Times": [1.0, 2.0, 3.0, 4.0],
                "Values": [0.4, 0.3, 0.2, 0.1]
            }
        }
    }
}

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Durability_Map

JSON object

NA

NA

NA

The time, in days, since the intervention was distributed and a multiplier for the Initial_Effect.

 
{
    "Durability_Map": {
        "Times": [
            0.0,
            20.0,
            21.0,
            30.0,
            31.0,
            365.0
        ],
        "Values": [
            1.0,
            1.0,
            0.0,
            0.0,
            1.0,
            1.0
        ]
    }
}

Initial_Effect

float

0

1

1

The multiplier used to define the magnitude of the shape of the curve, as specified when using the parameters with the WaningEffectMapLinear class.

 
{
    "Waning_Config": {
        "Durability_Map": {
            "Times": [
                0,
                30,
                60,
                90,
                120
            ],
            "Values": [
                0.9,
                0.3,
                0.9,
                0.6,
                1
            ]
        },
        "Expire_At_Durability_Map_End": 0,
        "Initial_Effect": 1,
        "class": "WaningEffectMapLinear"
    }
}

Times

array of floats

0

999999

NA

An array of days.

 
{
    "Durability_Map": {
        "Times": [
            0,
            385,
            390,
            10000
        ],
        "Values": [
            0,
            0.0,
            0.5,
            0.5
        ]
    }
}

Values

array of floats

0

3.40282e+38

NA

An array of values to match the defined Times.

 
{
    "Times": [
        1998,
        2000,
        2003,
        2006,
        2009
    ],
    "Values": [
        0,
        0.26,
        0.08,
        0.14,
        0.54
    ]
}
WaningEffectMapLinear

The efficacy decays based on the time since the start of the intervention. This change is defined by a map of time to efficacy values in which the time between time/value points is linearly interpolated. When the time since start reaches the end of the times in the map, the last value will be used unless the intervention expires. If the time since start is less than the first value in the map, the efficacy will be zero. This can be used to define the shape of a curve whose magnitude is defined by the Initial_Effect multiplier.

 {
    "Intervention_Config": {
        "class": "ControlledVaccine",
        "Waning_Config": {
            "class": "WaningEffectMapLinear",
            "Reference_Timer": 0,
            "Initial_Effect": 1.0,
            "Expire_At_Durability_Map_End": 1,
            "Durability_Map": {
                "Times": [0, 120, 240, 360],
                "Values": [0.7, 0.8, 1.0, 0.0]
            }
        }
    }
 }

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Durability_Map

JSON object

NA

NA

NA

The time, in days, since the intervention was distributed and a multiplier for the Initial_Effect.

 
{
    "Durability_Map": {
        "Times": [
            0.0,
            20.0,
            21.0,
            30.0,
            31.0,
            365.0
        ],
        "Values": [
            1.0,
            1.0,
            0.0,
            0.0,
            1.0,
            1.0
        ]
    }
}

Expire_At_Durability_Map_End

boolean

0

1

0

Set to 1 to let the intervention expire when the end of the map is reached.

 
{
    "Expire_At_Durability_Map_End": 1
}

Initial_Effect

float

0

1

1

The multiplier used to define the magnitude of the shape of the curve, as specified when using the parameters with the WaningEffectMapLinear class.

 
{
    "Waning_Config": {
        "Durability_Map": {
            "Times": [
                0,
                30,
                60,
                90,
                120
            ],
            "Values": [
                0.9,
                0.3,
                0.9,
                0.6,
                1
            ]
        },
        "Expire_At_Durability_Map_End": 0,
        "Initial_Effect": 1,
        "class": "WaningEffectMapLinear"
    }
}

Reference_Timer

integer

0

2147480000.0

0

The timestamp at which linear-map should be anchored.

 
{
    "Reference_Timer": 1
}

Times

array of floats

0

999999

NA

An array of days.

 
{
    "Durability_Map": {
        "Times": [
            0,
            385,
            390,
            10000
        ],
        "Values": [
            0,
            0.0,
            0.5,
            0.5
        ]
    }
}

Values

array of floats

0

3.40282e+38

NA

An array of values to match the defined Times.

 
{
    "Times": [
        1998,
        2000,
        2003,
        2006,
        2009
    ],
    "Values": [
        0,
        0.26,
        0.08,
        0.14,
        0.54
    ]
}
WaningEffectMapLinearAge

Similar to WaningEffectMapLinear, except that the efficacy decays based on the age of the individual who owns the intervention instead of the time since the start of the intervention.

{
  "class": "WaningEffectMapLinearAge",
  "Initial_Effect": 1,
  "Durability_Map": {
    "Times": [
      1,
      2,
      5,
      7
    ],
    "Values": [
      1,
      0.75,
      0.5,
      0.25
    ]
  }
}

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Durability_Map

JSON object

NA

NA

NA

The time, in days, since the intervention was distributed and a multiplier for the Initial_Effect.

 
{
    "Durability_Map": {
        "Times": [
            1,
            2,
            5,
            7
        ],
        "Values": [
            1,
            0.75,
            0.5,
            0.25
        ]
    }
}

Initial_Effect

float

0

1

1

The multiplier used to define the magnitude of the shape of the curve, as specified when using the parameters with the WaningEffectMapLinearAge class.

 
{
    "class": "WaningEffectMapLinearAge",
    "Initial_Effect": 1,
    "Durability_Map": {
        "Times": [
            1,
            2,
            5,
            7
        ],
        "Values": [
            1,
            0.75,
            0.5,
            0.25
        ]
    }
}

Times

array of floats

0

125

NA

An array of years.

 
{
    "Durability_Map": {
        "Times": [
            1,
            2,
            5,
            7
        ],
        "Values": [
            1,
            0.75,
            0.5,
            0.25
        ]
    }
}

Values

array of floats

0

3.40282e+38

NA

An array of values to match the defined Times.

 
{
    "Durability_Map": {
        "Times": [
            1,
            2,
            5,
            7
        ],
        "Values": [
            1,
            0.75,
            0.5,
            0.25
        ]
    }
}
WaningEffectMapLinearSeasonal

Similar to WaningEffectMapLinear, except that the map will repeat itself every 365 days. That is, the time since start will reset to zero once it reaches 365. This allows you to simulate seasonal effects.

{
    "Intervention_Config": {
        "class": "UsageDependentBednet",
        "Usage_Config_List": [{
            "class": "WaningEffectMapLinearSeasonal",
            "Initial_Effect": 1.0,
            "Durability_Map": {
                "Times": [0.0, 20.0, 21.0, 30.0, 31.0, 365.0],
                "Values": [1.0, 1.0, 0.0, 0.0, 1.0, 1.0]
            }
        }]
    }
}

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Durability_Map

JSON object

NA

NA

NA

The time, in days, since the intervention was distributed and a multiplier for the Initial_Effect.

 
{
    "Durability_Map": {
        "Times": [
            0.0,
            20.0,
            21.0,
            30.0,
            31.0,
            365.0
        ],
        "Values": [
            1.0,
            1.0,
            0.0,
            0.0,
            1.0,
            1.0
        ]
    }
}

Initial_Effect

float

0

1

1

The multiplier used to define the magnitude of the shape of the curve, as specified when using the parameters with the WaningEffectMapLinearSeasonal class.

 
{
    "class": "WaningEffectMapLinearSeasonal",
    "Initial_Effect": 1,
    "Durability_Map": {
        "Times": [
            0,
            20,
            21,
            30,
            31,
            365
        ],
        "Values": [
            1,
            1,
            0,
            0,
            1,
            1
        ]
    }
}

Times

array of floats

0

365

NA

An array of days.

 
{
    "Durability_Map": {
        "Times": [
            0.0,
            20.0,
            21.0,
            30.0,
            31.0,
            365.0
        ],
        "Values": [
            1.0,
            1.0,
            0.0,
            0.0,
            1.0,
            1.0
        ]
    }
}

Values

array of floats

0

3.40282e+38

NA

An array of values to match the defined Times.

 
{
    "Times": [
        1998,
        2000,
        2003,
        2006,
        2009
    ],
    "Values": [
        0,
        0.26,
        0.08,
        0.14,
        0.54
    ]
}
WaningEffectMapPiecewise

Similar to WaningEffectMapLinear, except that the data is assumed to be constant between time/value points (no interpolation). If the time since start falls between two points, the efficacy of the earlier time point is used.

{
    "Intervention_Config": {
        "class": "SimpleVaccine",
        "Waning_Config": {
            "class": "WaningEffectMapPiecewise",
            "Initial_Effect": 1.0,
            "Reference_Timer": 0,
            "Expire_At_Durability_Map_End": 0,
            "Durability_Map": {
                "Times": [10, 30, 50],
                "Values": [0.9, 0.1, 0.6]
            }
        }
    }
}

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Durability_Map

JSON object

NA

NA

NA

The time, in days, since the intervention was distributed and a multiplier for the Initial_Effect.

 
{
    "Durability_Map": {
        "Times": [
            0.0,
            20.0,
            21.0,
            30.0,
            31.0,
            365.0
        ],
        "Values": [
            1.0,
            1.0,
            0.0,
            0.0,
            1.0,
            1.0
        ]
    }
}

Expire_At_Durability_Map_End

boolean

0

1

0

Set to 1 to let the intervention expire when the end of the map is reached.

 
{
    "Expire_At_Durability_Map_End": 1
}

Initial_Effect

float

0

1

1

The multiplier used to define the magnitude of the shape of the curve, as specified when using the parameters with the WaningEffectMapPiecewise class.

 
{
    "Waning_Config": {
        "Durability_Map": {
            "Times": [
                10,
                30,
                50
            ],
            "Values": [
                0.9,
                0.1,
                0.6
            ]
        },
        "Expire_At_Durability_Map_End": 0,
        "Initial_Effect": 1,
        "class": "WaningEffectMapPiecewise"
    }
}

Reference_Timer

integer

0

2147480000.0

0

The timestamp at which linear-map should be anchored.

 
{
    "Reference_Timer": 1
}

Times

array of floats

0

999999

NA

An array of days.

 
{
    "Durability_Map": {
        "Times": [
            0,
            385,
            390,
            10000
        ],
        "Values": [
            0,
            0.0,
            0.5,
            0.5
        ]
    }
}

Values

array of floats

0

3.40282e+38

NA

An array of values to match the defined Times.

 
{
    "Times": [
        1998,
        2000,
        2003,
        2006,
        2009
    ],
    "Values": [
        0,
        0.26,
        0.08,
        0.14,
        0.54
    ]
}
WaningEffectRandomBox

The efficacy is held at a constant rate until it drops to zero after a user-defined duration. This duration is randomly selected from an exponential distribution where Expected_Discard_Time is the mean.

{
    "Intervention_Config": {
        "class": "SimpleBednet",
        "Cost_To_Consumer": 5,
        "Usage_Config": {
            "class": "WaningEffectRandomBox",
            "Initial_Effect": 1.0,
            "Expected_Discard_Time": 60
        }
    }
}

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Expected_Discard_Time

float

0

100000

100

The mean time, in days, of an exponential distribution of the duration of the effect of an intervention (such as a vaccine or bed net). Specify how this effect decays over time using one of the Waning effect classes.

 
{
    "Expected_Discard_Time": 60
}

Initial_Effect

float

0

1

1

Strength of the effect, which remains constant until the duration is complete when using with the WaningEffectRandomBox class.

 
{
    "Waning_Config": {
        "Expected_Discard_Time": 65,
        "Initial_Effect": 0.85,
        "class": "WaningEffectRandomBox"
    }
}
Targeting_Config classes

The following classes can be used to enhance the selection of people when distributing interventions. Most event coordinators and node-level interventions that distribute interventions to people have a parameter called Targeting_Config. This allows you to not only target individuals based on their gender, age, and IndividualProperties (See NodeProperties and IndividualProperties parameters for more information), but also on things such as whether or not they have a particular intervention or are in a relationship.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

Below is a simple example where we want to distribute a vaccine to 20% of the people that do not already have the vaccine on the 100th day of the simulation.

{
    "class": "CampaignEvent",
    "Start_Day": 100,
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Event_Coordinator_Config": {
        "class": "StandardInterventionDistributionEventCoordinator",
        "Target_Demographic": "Everyone",
        "Demographic_Coverage": 0.2,
        "Targeting_Config": {
            "class": "HasIntervention",
            "Is_Equal_To": 0,
            "Intervention_Name": "MyVaccine"
        },
        "Intervention_Config": {
            "class": "SimpleVaccine",
            "Intervention_Name" : "MyVaccine",
            "Cost_To_Consumer": 1,
            "Vaccine_Take": 1,
            "Vaccine_Type": "AcquisitionBlocking",
            "Waning_Config": {
                "class": "WaningEffectConstant",
                "Initial_Effect" : 1.0
            }
        }
    }
}

Below is a slightly more complicated example where we want to distribute a diagnostic to people that are either high risk or have not been vaccinated.

{
    "class": "CampaignEvent",
    "Start_Day": 100,
    "Nodeset_Config": {
        "class": "NodeSetAll"
    },
    "Event_Coordinator_Config": {
        "class": "StandardInterventionDistributionEventCoordinator",
        "Target_Demographic": "Everyone",
        "Demographic_Coverage": 0.2,
        "Targeting_Config": {
            "class" : "TargetingLogic",
            "Logic" : [
                [
                    {
                        "class": "HasIntervention",
                        "Is_Equal_To": 0,
                        "Intervention_Name": "MyVaccine"
                    }
                ],
                [
                    {
                        "class": "HasIP",
                        "Is_Equal_To": 1,
                        "IP_Key_Value": "Risk:HIGH"
                    }
                ]
            ]
        },
        "Intervention_Config": {
            "class": "SimpleDiagnostic",
            "Treatment_Fraction": 1.0,
            "Base_Sensitivity": 1.0,
            "Base_Specificity": 1.0,
            "Event_Or_Config": "Event",
            "Positive_Diagnosis_Event": "TestedPositive"
        }
    }
}
HasIntervention

This determines whether or not the individual has an intervention with the given name. This will only work for interventions that persist like SimpleVaccine and DelayedIntervention. It will not work for interventions like BroadcastEvent since it does not persist.

Configuration

Parameter

Data type

Min

Max

Default

Description

Is_Equal_To

boolean

0

1

1

This is used to determine if the individual is selected based on the result of the value of the question. For example, if the user wants to select individuals that are NOT circumcised, the user will set this parameter to false (0). If the return value of the question ‘is equal to’ the value of this parameter, the individual is selected.

Intervention_Name

string

(no default)

The name of the intervention the person should have. This cannot be an empty string but should be either the name of the class or the name given to the intervention of interest using the parameter Intervention_Name. EMOD does not verify that this name exists.
Example

Select the person if they do NOT have the MyVaccine intervention.

"Targeting_Config": {
    "class": "HasIntervention",
    "Is_Equal_To": 0,
    "Intervention_Name": "MyVaccine"
}
HasIP

This determines if the person has a particular value of a particular IndividualProperties (IP). This is especially needed when determining if a partner has a particular IP (see HasRelationship).

Configuration

Parameter

Data type

Min

Max

Default

Description

Is_Equal_To

boolean

0

1

1

This is used to determine if the individual is selected based on the result of the value of the question. For example, if the user wants to select individuals that are NOT circumcised, the user will set this parameter to false (0). If the return value of the question ‘is equal to’ the value of this parameter, the individual is selected.

IP_Key_Value

string

(no default)

This is the normal IndividualProperties ‘Key:Value’ pair where the key/property name and one of its values is separated by a colon (‘:’).
Example

Select the person if their Risk property is HIGH.

"Targeting_Config": {
    "class": "HasIP",
    "Is_Equal_To": 1,
    "IP_Key_Value": "Risk:HIGH"
}
TargetingLogic

In some cases, the you need to logically combine multiple restrictions. In these situations, you should use the TargetingLogic class where you can “and” and “or” the different questions.

NOTE: Each element is independent and is being asked of the individual in question. For questions that are about a partner of the individual, all of the qualifiers for that partner must be in the element. This will ensure that there is one partner that has all of the qualifications. Otherwise, you could have a situation where one partner satisfies one qualification and another partner satisfies a different one, but no partner has all of the qualifications.

Configuration

Parameter

Data type

Min

Max

Default

Description

Logic

2D array of JSON objects

(no default)

This is a two-dimensional array of JSON objects. The elements of the inner array will be AND’d together while the arrays themselves will be OR’d. This is similar to Property_Restrictions_Within_Node. This array and the inner arrays cannot be empty.
Example

Select the person if they do not have the MyVaccine intervention OR do not have their Risk property set to HIGH. Notice that Logic 2x1 where the first dimention contains two arrays with one JSON object. These two arrays are OR’d together.

"Targeting_Config": {
    "class" : "TargetingLogic",
    "Logic" : [
        [
            {
                "class": "HasIntervention",
                "Is_Equal_To": 0,
                "Intervention_Name": "MyVaccine"
            }
        ],
        [
            {
                "class": "HasIP",
                "Is_Equal_To": 0,
                "IP_Key_Value": "Risk:HIGH"
            }
        ]
    ]
}

Select the person if they do not have the MyVaccine intervention AND do not have their Risk property set to HIGH. Notice that Logic is 1x2 where the first dimension contains a single array with two JSON objects. These two objects are AND’d together.

"Targeting_Config": {
    "class" : "TargetingLogic",
    "Logic" : [
        [
            {
                "class": "HasIntervention",
                "Is_Equal_To": 0,
                "Intervention_Name": "MyVaccine"
            },
            {
                "class": "HasIP",
                "Is_Equal_To": 0,
                "IP_Key_Value": "Risk:HIGH"
            }
        ]
    ]
}
Event list

The following list provides possible values for built-in events. These events can be broadcast to nodes to trigger interventions. You can also broadcast custom events, but the event names must be listed in the Custom_Individual_Events configuration parameter.

Note

Not all events are appropriate for every simulation type.

  • Births

  • DiseaseDeaths

  • EighteenMonthsOld

  • Emigrating

  • EnteredRelationship

  • EveryTimeStep

  • EveryUpdate

  • ExitedRelationship

  • ExposureComplete

  • FirstCoitalAct

  • FourteenWeeksPregnant

  • GaveBirth

  • HappyBirthday

  • HIVNewlyDiagnosed

  • HIVTestedNegative

  • HIVTestedPositive

  • HumanToVectorTransmission

  • Immigrating

  • InfectionCleared

  • InterventionDisqualified

  • NewClinicalCase

  • NewExternalHIVInfection

  • NewInfectionEvent

  • NewlySymptomatic

  • NewSevereCase

  • NodePropertyChange

  • NonDiseaseDeaths

  • OpportunisticInfectionDeath

  • Pregnant

  • PropertyChange

  • ProviderOrdersTBTest

  • ReceivedInfectiousBites

  • SixWeeksOld

  • StartedART

  • STIDebut

  • STIExposed

  • STINewInfection

  • STIPostImmigrating

  • STIPreEmigrating

  • StoppedART

  • SymptomaticCleared

  • TBActivation

  • TBActivationExtrapulm

  • TBActivationPostRelapse

  • TBActivationPresymptomatic

  • TBActivationSmearNeg

  • TBActivationSmearPos

  • TBFailedDrugRegimen

  • TBMDRTestDefault

  • TBMDRTestNegative

  • TBMDRTestPositive

  • TBPendingRelapse

  • TBRelapseAfterDrugRegimen

  • TBRestartHSB

  • TBStartDrugRegimen

  • TBStopDrugRegimen

  • TBTestDefault

  • TBTestNegative

  • TBTestPositive

  • TestPositiveOnSmear

  • TwelveWeeksPregnant

  • VectorToHumanTransmission

  • WouldHaveDied

  • WouldHaveHadAIDS

  • WouldHaveEnteredLatentStage

Generate a list of all available parameters (a schema)

You can generate a schema from the EMOD executable (Eradication.exe) or Eradication binary for Linux that defines all configuration parameters and campaign parameters available in the version of EMOD that is installed, for all available simulation types. This includes parameter names, data types, defaults, ranges, and short descriptions. The schema does not include demographics parameters.

Logging information is also produced as part of the schema. If you are using EMOD source and have added or modified configuration parameters or intervention code, this logging information can help you troubleshoot any errors that may occur.

Warning

If you modify the source code to add or remove configuration or campaign parameters, you may need to update the code used to produce the schema. You must also verify that your simulations are still scientifically valid.

Command options

The following command-line options are available for providing information about EMOD.

EMOD information commands

Long form

Short form

Description

--help

-help

Shows help options for Eradication.exe.

--version

-v

Shows the version information and supported simulation types. Note capitalization of short alternative.

--get-schema

--get-schema

Outputs the configuration and campaign parameters. Unless --schema-path or a redirect operator is used, the schema will print to the Command Prompt window.

--schema-path

>

When used with --get-schema, if a TXT or JSON file is specified, the schema information will be written to the file instead of the Command Prompt window.
Usage

  1. Open a Command Prompt window and navigate to the directory where Eradication.exe is installed.

  2. To output the schema to the Command Prompt window, enter:

    Eradication.exe --get-schema

  3. To output the schema to a file, do one of the following (replacing <filename> with the desired filename):

    • To output a text file that includes logging information, enter:

      Eradication.exe --get-schema > <filename>.txt

    • To display logging in the Command Prompt window and output a text file that does not include logging information, enter:

      Eradication.exe --get-schema --schema-path <filename>.txt

    • To output the schema to a JSON file that includes logging information, enter:

      Eradication.exe --get-schema > <filename>.json

    • To display logging in the Command Prompt window and output a JSON file that does not include logging information, enter:

      Eradication.exe --get-schema --schema-path <filename>.json

Frequently asked questions

Why doesn’t anything happen when I double-click Eradication.exe?

Unlike many executable files, Eradication.exe does not open a software installer when double-clicked. Instead, you must provide Eradication.exe with various input files used to run a simulation and create outputs. You can run simulations in a number of ways. The simplest way to learn about the model is to run individual simulations at the command line. However, because EMOD is a stochastic model, this is not feasible when you must run many simulations to create useful output for statistical analysis. Once you advance to that stage, you will want to use other software tools to run multiple simulations either locally or, more often, on an HPC cluster. For more information, see EMOD installation and Running a simulation.

Where do I get demographics and climate files?

IDM provides demographics and climate files for many different regions of the world in the EMOD-InputData GitHub repository. See EMOD installation for download instructions. If there are no files available for the region you are interested in, contact support@idmod.org.

How do I plot my output?

EMOD does not produce plots of the data by default. Instead, it produces JSON (JavaScript Object Notation) or CSV files that you can then parse and plot using Python, MATLAB, R, or another programming language. The EMOD scenarios zip file contains input files and plotting scripts for many different disease modeling scenarios intended to teach EMOD. You may want to review these files to see examples of how the output can be plotted.

Does your model include X?

Many aspects of disease dynamics are explicitly modeled in EMOD. Review EMOD parameter reference for an exhaustive list of all parameters that can be used to enable functionality in EMOD. If you want to model something that isn’t listed there, EMOD provides highly flexible functionality with the individual and node property feature. These properties label individuals or regional nodes in a simulation so you can change disease outbreaks, transmission dynamics, behavior, or treatment based on the assigned values. For example, you can add properties to represent vitamin deficiencies, comorbidities, and more. For more information, see Individual and node properties.

If individual and node properties cannot incorporate the functionality you need, EMOD is open-source software that can be extended to meet your needs. For more information, see EMOD source code installation.

Can I model regions, countries, provinces, or cities?

EMOD uses nodes to represent geographic areas. Each node has a population and associated characteristics. Individuals and, if relevant, vectors can migrate between nodes. Nodes can represent areas of any scale you like, from entire countries to individual households.

Do you have a model for disease X?

The software architecture of EMOD has a “generic model” base upon which more specific transmission modes and diseases are built. This allows common functionality to be shared for modeling a variety of diseases. For example, the generic model can be used to model influenza, measles, and more. You can also use one of the transmission mode simulation types (airborne, STI, environmental, vector) to model diseases such as herpes, typhoid, dengue or others that we don’t explicitly support. Often, this can be done without the need to make any source code changes.

Tips and tricks

This is a user-created and curated section designed to provide tips and techniques for utilizing advanced features of the EMOD malaria model.

Heterogeneous biting risk

This page provides an overview of how and when to use the EMOD features for heterogeneous biting risk.

What is heterogeneous biting risk and why would you want to include it?

Many studies have identified substantial variance in the number of bites that individuals within a population experience, which may be attributable to a combination of factors, such as individuals’ age, physiology, behavior, and location (Irvine et al 2018, Cooper et al 2019). Including biting risk heterogeneity in malaria transmission models is important for accurately capturing prevalence, incidence, probability of elimination, and other important simulation outcomes. To capture this heterogeneity in EMOD, we specify both age-specific risks and individual relative risks, which can increase or decrease the number of bites an individual tends to experience relative to the mean risk in the population.

Changing the distribution of relative biting risks does not change the total number of bites given to the population, but it does change how those bites are distributed among individuals. People with higher risk have a higher probability of receiving bites than people with lower risk. Because it is a relative biting rate, the exact values assigned do not matter, only their relation to the sum of all risks in the population. For example, giving everyone a value of 10 is the same as giving everyone a value of 1.

A brief description of the different ways of specifying heterogeneous biting in EMOD is given below, along with links to the main documentation pages for each type.

Age_Dependent_Biting_Risk_Type
Where is it specified?

The relative biting risk among people of different ages is specified in the configuration file with the Age_Dependent_Biting_Risk_Type parameter. There are three options for the type of distribution used (‘OFF’, ‘LINEAR’, or ‘SURFACE_AREA_DEPENDENT’, which are explained in Infectivity and transmission.

Who does it apply to?

If Age_Dependent_Biting_Risk_Type is enabled, the biting risk of everyone in the population is modified according to their age. This includes people who were created at the beginning of the simulation as well as people who are born during the simulation.

How does serialization affect it?

Yes, one can change this parameter when reading from a serialized file. The age dependent part of an individual’s biting risk is calculated each time step.

When should it be used?

If you aim to capture more realistic relative biting risks across ages in your simulation, it is generally a good idea to enable age-dependent biting risk.

Biting risk demographics
Where is it specified?

The distribution of relative biting risks across the entire population is specified in the demographics file using the RiskDistributionFlag (which provides the type of distribution used) along with RiskDistribution1 and RiskDistribution2 parameters (which specify the shape of the distribution). Details on the values for these parameters can be found in Demographics parameters.

Note

It is necessary to also set Enable_Demographics_Risk to 1 in the configuration file, otherwise the demographics risks will be ignored in the simulation. You must also set Enable_Demographics_Birth to 1 if you want newborns to get different relative biting risks.

Who does it apply to?

When the relative biting risk distribution is set in the demographics file, all individuals that are created at initialization and all individuals that are born during the simulation have relative biting risks that are drawn from that distribution.

How does serialization affect it?

An individual that is deserialized from the file will have whatever relative biting risk they had when they were deserialized. However, if Enable_Demographics_Risk and Enable_Demographics_Birth were serialized on, then newborns will get new relative biting risk values based on the RiskDistributionFlag, RiskDistribution1, and RiskDistribution2 parameters in the demographics file.

Individual’s risks do not change at serialization.

When should it be used?

The demographics approach for specifying relative biting risk is well-suited for simulations where you want to include a more realistic distribution of risks across all individuals in the population.

Biting risk intervention
Where is it specified?

The biting risk intervention campaign is a campaign that changes the biting risk of a specified group of people at a particular point in time in the simulation. Details on setting up biting risk intervention campaigns can be found in BitingRisk.

Who does it apply to?

Like other intervention campaigns, individuals with certain individual properties, ages, etc. can be targeted, or it can be distributed to the entire population. Only people who exist in the simulation at the time the campaign occurs are affected. This contrasts with the demographics approach to setting the risk distribution, where all individuals are initialized or born with risks drawn from the distribution.

Will an individual’s current risk be overwritten?

Implementing a biting risk intervention campaign overwrites the existing relative biting risk values of targeted individuals (regardless of these biting risks were set using the demographics file or a previous biting risk intervention). The new biting risk values that are assigned are independent of the biting risk a person had previously.

How does serialization affect it?

Individual’s risks do not change at serialization. However, whenever there is a new BitingRisk intervention, it will overwrite existing relative risk values.

When should it be used?

The intervention version of setting biting risks is best suited for situations where you want a subset of the population to have a different risk than everyone else. For example, if you want a group of people who work in high-risk areas to have substantially higher risk than the general population, you can use IPs and biting risk campaigns to target those individuals.

Common questions
  1. What are reasonable parameters to use for the relative biting risk?

    Check out these blog posts comparing different parameterizations with data, found here, and here.

    Note that you must have permission to this private folder to view the blog posts.

  2. Do heterogeneous biting risks apply when the model is configured to use forced EIR instead of mechanistic mosquito bites?

    No, but there are different parameters that can be used to specify age-based heterogeneity.

Glossary

The Epidemiological MODeling software (EMOD) glossary is divided into the following subsections that define terms related to software usage, general epidemiology, and the particular disease being modeled.

Epidemiology terms

antibodies

See antibody.

antibody

A blood protein produced in response to and counteracting a specific antigen. Antibodies combine chemically with substances that the body recognizes as foreign (eg. bacteria, viruses, or other substances).

antigen

A substance that is capable of inducing a specific immune response and that evokes the production of one or more antibodies.

antigens

See antigen.

Clausius-Clayperon relation

A way of characterizing a transition between two phases of matter; provides a method to find a relationship between temperature and pressure along phase boundaries. Frequently used in meteorology and climatology to describe the behavior of water vapor. See Wikipedia - Clausius-Clayperon relation for more information.

compartmental model

A disease model that divides the population into a number of compartments that represent different disease states, such as susceptible, infected, or recovered. Every person in a compartment is considered identical. Many compartmental models are deterministic, but some are stochastic.

deterministic

Characterized by the output being fully determined by the parameter values and the initial conditions. Given the same inputs, a deterministic model will always produce the same output.

diffusive migration

The diffusion of people in and out of nearby nodes by foot travel.

disability-adjusted life years (DALY)

The number of years of life lost due to premature mortality plus the years lost due to disability while infected. Used to quantify the burden of disease.

epidemic

An outbreak of an infectious disease, such that a greater number of individuals than normal has the disease. Epidemics have very high R0 (Recall R0>1 for a disease to spread) and are often associated with acute, highly transmissible pathogens that can be directly transmitted. Further, pathogens with lower infectious periods create more explosive epidemics. To control epidemics, it is necessary to reduce R0. This can be done by:

  • Reducing transmissibility.

  • Decreasing the number of susceptibles (by vaccination, for example).

  • Decreasing the mean number of contacts or the transmissibility, such as by improving sanitation, or limiting the number of interactions sick people have with healthy people.

  • Reducing the length of the infectious period.

epitope

The portion of an antigen that the immune system recognizes. An epitope is also called an antigenic determinant.

Euler method

Used in mathematics and computational science, this method is a first-order numerical procedure for solving ordinary differential equations with a given initial value.

exp(

The exponential function, \(e^x\), where \(e\) is the number (approximately 2.718281828) such that the function \(e^x\) is its own derivative. The exponential function is used to model a relationship in which a constant change in the independent variable gives the same proportional change (i.e. percentage increase or decrease) in the dependent variable. The function is often written as \(exp(x)\). The graph of \(y = exp(x)\) is upward-sloping and increases faster as \(x\) increases.

exposed

Individual who has been infected with a pathogen, but due to the pathogen’s incubation period, is not yet infectious.

force of infection (FoI)

A measure of the degree to which an infected individual can spread infection; the per-capita rate at which susceptibles contract infection. Typically increases with transmissibility and prevalence of infection.

herd immunity

The resistance to the spread of a contagious disease within a population that results if a sufficiently high proportion of individuals are immune to the disease, especially through vaccination. The portion of the population that needs to be immunized in order to achieve herd immunity is P > 1 – (1/ R0), where P = proportion vaccinated * vaccine efficacy.

immune

Unable to become infected/infectious, whether through vaccination or having the disease in the past.

incidence

The rate of new cases of a disease during a specified time period. This is a measure of the risk of contracting a disease.

infectious

Individual who is infected with a pathogen and is capable of transmitting the pathogen to others.

Koppen-Geiger Climate Classification System

A system based on the concept that native vegetation is a good expression of climate. Thus, climate zone boundaries have been selected with vegetation distribution in mind. It combines average annual and monthly temperatures and precipitation, and the seasonality of precipitation. EMOD has several options for configuring the climate, namely air temperature, rainfall, and humidity.

One option utilizes input files that associate geographic nodes with Koppen climate indices. The modified Koppen classification uses three letters to divide the world into five major climate regions (A, B, C, D, and E) based on average annual precipitation, average monthly precipitation, and average monthly temperature. Each category is further divided into sub-categories based on temperature and precipitation. While the Koppen system does not take such things as temperature extremes, average cloud cover, number of days with sunshine, or wind into account, it is a good representation of our earth’s climate.

loss to follow-up (LTFU)

Patients who at one point were actively participating in disease treatment or clinical research, but have become lost either by error or by becoming unreachable at the point of follow-up.

LTFU

See loss to follow-up.

ordinary differential equation (ODE)

A differential equation containing one or more functions of one independent variable and its derivatives.

prevalence

The rate of all cases of a disease during a specified time period. This is a measure of how widespread a disease is.

recovered

Individual who is either no longer infectious, or “removed” from the population.

reproductive number

In a fully susceptible population, the basic reproductive number R0 is the number of secondary infections generated by the first infectious individual over the course of the infectious period. R0=S*L* \(\beta\) (where S = the number of susceptible hosts, L = length of infection, and \(\beta\) = transmissibility). When R0> 1, disease will spread. It is essentially a measure of the expected or average outcome of transmission. The effective reproductive number takes into account non-susceptible individuals. This is the threshold parameter used to determine whether or not an epidemic will occur, and determines:

  • The initial rate of increase of an epidemic (the exponential growth phase).

  • The final size of an epidemic (what fraction of susceptibles will be infected).

  • The endemic equilibrium fraction of susceptibles in a population (1/ R0).

  • The critical vaccination threshold, which is equal to 1-(1/ R0), and determines the number of people that must be vaccinated to prevent the spread of a pathogen.

routine immunization (RI)

The standard practice of vaccinating the majority of susceptible people in a population against vaccine-preventable diseases.

SEIR model

A generic epidemiological model that provides a simplified means of describing the transmission of an infectious disease through individuals where those individuals can pass through the following five states: susceptible, exposed, infectious, and recovered.

SEIRS model

A generic epidemiological model that provides a simplified means of describing the transmission of an infectious disease through individuals where those individuals can pass through the following five states: susceptible, exposed, infectious, recovered, and susceptible.

SI model

A generic epidemiological model that provides a simplified means of describing the transmission of an infectious disease through individuals where those individuals can pass through the following five states: susceptible and infectious.

simulation burn-in

A modeling concept in which a simulation runs for a period of time before reaching a steady state and the output during that period is not used for predictions. This concept is borrowed from the electronics industry where the first items produced by a manufacturing process are discarded.

SIR model

A generic epidemiological model that provides a simplified means of describing the transmission of an infectious disease through individuals where those individuals can pass through the following five states: susceptible, infectious, and recovered.

SIRS model

A generic epidemiological model that provides a simplified means of describing the transmission of an infectious disease through individuals where those individuals can pass through the following five states: susceptible, infectious, recovered, and susceptible.

SIS model

A generic epidemiological model that provides a simplified means of describing the transmission of an infectious disease through individuals where those individuals can pass through the following five states: susceptible, infectious, and susceptible.

stochastic

Characterized by having a random probability distribution that may be analyzed statistically but not predicted precisely.

stochastic die-out

When an disease outbreak ends, despite having an effective R0 above 1, due to randomness. A deterministic model cannot estimate the probability of stochastic die- out, but a stochastic model can.

subpatent

When an individual is infected but asymptomatic, so the infection is not readily detectable.

superinfection

The simultaneous infection with multiple strains of the same pathogen.

supplemental immunization activity (SIA)

In contrast to routine immunization (RI), SIAs are large-scale operations with a goal of delivering vaccines to every household.

susceptible

Individual who is able to become infected.

transmissibility (\(\beta\))

Also known as the effective contact rate, is the product of the contact rate and the probability of transmission per contact.

virulence

The capacity of a pathogen to produce disease. It is proportional to parasitemia, or the number of circulating copies of the pathogen in the host. The higher the virulence (given contact between S and I individuals), the more likely transmission is to occur. However, higher virulence means contact may be less likely as infected hosts show more symptoms of the disease. There is a trade-off that occurs between high transmissibility and disease- induced mortality.

WAIFW matrix

A matrix of values that describes the rate of transmission between different population groups. WAIFW is an abbreviation for Who Acquires Infection From Whom.

Weibull distribution

A probability distribution often used in EMOD and that requires both a shape parameter and a scale parameter. The shape parameter governs the shape of the density function. When the shape parameter is equal to 1, it is an exponential distribution. For shape parameters above 1, it forms a unimodal (hump-shaped) density function. As the shape parameter becomes large, the function forms a sharp peak. The inverse of the shape parameter is sometimes referred to here as the “heterogeneity” of the distribution (heterogeneity = 1/shape), because it can be helpful to think about the degree of heterogeneity of draws from the distribution, especially for hump-shaped functions with heterogeneity values between 0 and 1 (i.e., shape parameters greater than 1). The scale parameter shifts the distribution from left to right. When heterogeneity is small (i.e., the shape parameter is large), the scale parameter sets the location of the sharp peak.

EMOD software terms

agent-based model

A type of simulation that models the actions and interactions of autonomous agents (both individual and collective entities such as organizations or groups).

Boost

Free, peer-reviewed, portable C++ source libraries aimed at a wide range of uses including parallel processing applications (Boost.MPI). For more information, please see the Boost website, http://www.boost.org.

boxcar function

A mathematical function that is equal to zero over the entire real line except for a single interval where it is equal to a constant.

campaign

A collection of events that use interventions to modify a simulation.

campaign event

A JSON object that determines when and where an intervention is distributed during a campaign.

campaign file

A JSON (JavaScript Object Notation) formatted file that contains the parameters that specify the distribution instructions for all interventions used in a campaign, such as diagnostic tests, the target demographic, and the timing and cost of interventions. The location of this file is specified in the configuration file with the Campaign_Filename parameter. Typically, the file name is campaign.json.

channel

A property of the simulation (for example, “Parasite Prevalence”) that is accumulated once per simulated time step and written to file, typically as an array of the accumulated values.

class factory

A function that instantiate objects at run-time and use information from JSON-based configuration information in the creation of these objects.

configuration file

A JSON (JavaScript Object Notation) formatted file that contains the parameters sufficient for initiating a simulation. It controls many different aspects of the simulation, such as population size, disease dynamics, and length of the simulation. Typically, the file name is config.json.

core

In computing, a core refers to an independent central processing unit (CPU) in the computer. Multi-core computers have more than one CPU. However, through technologies such as Hyper- Threading Technology (HTT or HT), a single physical core can actually act like two virtual or logical cores, and appear to the operating system as two processors.

demographics file

A JSON (JavaScript Object Notation) formatted file that contains the parameters that specify the demographics of a population, such as age distribution, risk, birthrate, and more. IDM provides demographics files for many geographic regions. This file is typically named <region>_demographics.json.

destination node

The node an individual or group is traveling to for each leg of the migration. The destination updates as individuals move between nodes.

disease-specific build

A build of the EMOD executable (Eradication.exe) built using SCons without any dynamic link libraries (DLLs).

Microsoft’s implementation of a shared library, separate from the EMOD executable (Eradication.exe), that can be dynamically loaded (and unloaded when unneeded) at runtime. This loading can be explicit or implicit.

EMODule

A modular component of EMOD that are consumed and used by the EMOD executable (Eradication.exe). Under Windows, a EMODule is implemented as a dynamic link library (DLL) and, under CentOS, EMODules are currently not supported. EMODules are primarily custom reporters.

Epidemiological MODeling software (EMOD)

The modeling software from the Institute for Disease Modeling (IDM) for disease researchers and developers to investigate disease dynamics, and to assist in combating a host of infectious diseases. You may see this referred to as Disease Transmission Kernel (DTK) in the source code.

Eradication.exe

Typical (default) name for the EMOD executable (Eradication.exe), whether built using monolithic build or modular (EMODule-enabled) build.

event coordinator

A JSON object that determines who will receive a particular intervention during a campaign.

flattened file

A single campaign or configuration file created by combining a default file with one or more overlay files. Multiple files must be flattened prior to running a simulation. Configuration files are flattened to a single-depth JSON file without nesting, the format required for consumption by the EMOD executable (Eradication.exe). Separating the parameters into multiple files is primarily used for testing and experimentation.

Heterogeneous Intra-Node Transmission (HINT)

A feature for modeling person-to-person transmission of diseases in heterogeneous population segments within a node for generic simulations.

high-performance computing (HPC)

The use of parallel processing for running advanced applications efficiently, reliably, and quickly.

home node

The node where the individuals reside; each individual has a single home node.

individual properties

Labels that can be applied to individuals within a simulation and used to configure heterogeneous transmission, target interventions, and move individuals through a health care cascade.

input files

The JSON and binary files used as inputs to an EMOD simulation. The primary input files are the JSON-formatted configuration, demographics, and campaign files. They may also include the binary files for migration, climate, population serialization, or load- balancing.

inset chart

The default JSON output report for EMOD that includes multiple channels that contain data at each time step of the simulation. These channels include number of new infections, prevalence, number of recovered, and more.

intervention

An object aimed at reducing the spread of a disease that is distributed either to an individual; such as a vaccine, drug, or bednet; or to a node; such as a larvicide. Additionally, initial disease outbreaks and intermediate interventions that schedule another intervention are implemented as interventions in the campaign file.

JSON

See JavaSCript Object Notation.

JSON (JavaScript Object Notation)

A human-readable, open standard, text-based file format for data interchange. It is typically used to represent simple data structures and associative arrays, and is language-independent. For more information, see https://www.json.org.

Keyhole Markup Language (KML)

A file format used to display geographic data in an Earth browser, for example, Google Maps. The format uses an XML-based structure (tag-based structure with nested elements and attributes). Tags are case-sensitive.

A method for the linker to optimize code (for size and/or speed) after compilation has occurred. The compiled code is turned not into actual code, but instead into an intermediate language form (IL, but not to be confused with .NET IL which has a different purpose). The LTCG then, unlike the compiler, can see the whole body of code in all object files and be able to optimize the result more effectively.

Message Passing Interface (MPI)

An interface used to pass information between computing cores in parallel simulations. One example is the migration of individuals from one geographic location to another within EMOD simulations.

microsolver

A type of “miniature model” that operates within the framework of EMOD to compute a particular set of parameters. Each microsolver, in effect, is creating a microsimulation in order to accurately capture the dynamics of that particular aspect of the model.

monolithic build

A single EMOD executable (Eradication.exe) with no DLLs that includes all components as part of Eradication.exe itself. You can still use EMODules with the monolithic build; for example, a custom reporter is a common type of EMODule. View the documentation on EMODules and emodules_map.json for more information about creation and use of EMODules.

Monte Carlo method

A class of algorithms using repeated random sampling to obtain numerical results. Monte Carlo simulations create probability distributions for possible outcomes, which provides a more realistic way of describing uncertainty.

node

A grid size that is used for modeling geographies. Within EMOD, a node is a geographic region containing simulated individuals. Individuals migrate between nodes either temporarily or permanently using mobility patterns driven by local, regional, and long- distance transportation.

node properties

Labels that can be applied to nodes within a simulation and used to target interventions based on geography.

node-targeted intervention

An intervention that is distributed to a geographical node rather than to a single individual. One example is larvicides, which affect all mosquitoes living and feeding within a given node.

nodes

See node.

origin node

The “starting point” node fo reach leg of a migration trip. The origin updates as individuals move between nodes.

output report

A file that is the output from an EMOD simulation. Output reports are in JSON, CSV, or binary file format. You must pass the data from an output report to graphing software if you want to visualize the output of a simulation.

overlay file

An additional configuration, campaign, or demographic file that overrides the default parameter values in the primary file. Separating the parameters into multiple files is primarily used for testing a nd experimentation. In the case of configuration and campaign files, the files can use an arbitrary hierarchical structure to organize parameters into logical groups. Configuration and campaign files must be flattened into a single file before running a simulation.

preview

Software that undergoes a shorter testing cycle in order to make it available more quickly. Previews may contain software defects that could result in unexpected behavior. Use EMOD previews at your own discretion.

regression test

A test to verify that existing EMOD functionality works with new updates, located in the Regression subdirectory of the EMOD source code repository. Directory names of each subdirectory in Regression describe the main regression attributes, for example, “1_Generic_Seattle_MultiNode”. Also can refer to the process of regression testing of software.

release

Software that includes new functionality, scientific tutorials leveraging new or existing functionality, and/or bug fixes that have been thoroughly tested so that any defects have been fixed before release. EMOD releases undergo full regression testing.

reporter

Functionality that extracts simulation data, aggregates it, and saves it as an output report. EMOD provides several built-in reporters for outputting data from simulations and you also have the ability to create a custom reporter.

scenario

A collection of input files that describes a real-world example of a disease outbreak and interventions. Many scenarios are included with EMOD source installations or are available to download at EMOD scenarios to learn more about epidemiology and disease modeling.

schema

A text or JSON file that can be generated from the EMOD executable (Eradication.exe) that defines all configuration and campaign parameters.

simulation

An execution of the EMOD software using an associated set of input files.

simulation type

The disease or disease class to model.

EMOD supports the following simulation types for modeling a variety of diseases:

  • Generic disease (GENERIC_SIM), which can be used for modeling a variety of diseases such as influenza or measles

  • Vector-borne diseases (VECTOR_SIM), which can be used for modeling vector-borne diseases such as dengue

  • Malaria (MALARIA_SIM), which adds features specific to malaria biology and treatment

  • Tuberculosis with HIV coinfection (TBHIV_SIM), which can be used for modeling TB transmission, with the option to add HIV coinfection as a contributing factor

  • Sexually transmitted infections (STI_SIM), which adds features for sexual relationship networks

  • HIV (HIV_SIM), which adds features specific to HIV biology and treatment

  • Environmental transmission (ENVIRONMENTAL_SIM), which adds features for diseases transmitted through contaminated food or water

  • Typhoid (TYPHOID_SIM), which adds features specific to typhoid biology and treatment

solvers

Solvers are used to find computational solutions to problems. In simulations, they can be used, for example, to determine the time of the next simulation step, or to compute the states of a model at particular time steps.

Standard Template Library (STL)

A library that contains a set of common C++ classes (including generic algorithms and data structures) that are independent of container and implemented as templates, which enables compile-time polymorphism (often more efficient than run-time polymorphism). For more information and discussion of STL, see Wikipedia - Standard Template Library for more information.

state transition event

A change in state (e.g. healthy to infected, undiagnosed to positive diagnosis, or birth) that may trigger a subsequent action, often an intervention. “Campaign events” should not be confused with state transition events.

time step

A discrete number of hours or days in which the “simulation states” of all “simulation objects” (interventions, infections, immune systems, or individuals) are updated in a simulation. Each time step will complete processing before launching the next one. For example, a time step would process the migration data for populations moving between nodes via rail, airline, and road. The migration of individuals between nodes is the last step of the time step after updating states.

tutorial

A set of instructions in the documentation to learn more about epidemiology and disease modeling. Tutorials are based on real-world scenarios and demonstrate the mechanics of the the model. Each tutorial consists of one or more scenarios.

working directory

The directory that contains the configuration and campaign files for a simulation. You must be in this directory when you invoke Eradication.exe at the command line to run a simulation.

Vector terms

cohort model

A vector model that tracks properties of mosquito “cohorts”, for example, population, age, gender, fertility, etc.

entomological inoculation rate (EIR)

The commonly-used measure of the intensity of vector-borne disease transmission. EIR is equal to the number of mosquito bites per night times the proportion of those bites that test positive for infectious agents, such as sporozoites in malaria.

gene drive mosquito

Gene drive is a genetic technique that promotes the inheritance of particular genes or specific genetic elements. In mosquitoes, approaches are being tested that work to suppress the spread of malaria by creating mosquito lines through fertility suppression, driving-Y chromosomes, or express genes that limit malaria transmission.

gonotrophic

A female mosquito’s feeding and egg-laying cycle, where the duration is defined as the average number of days that gravid mosquitoes take to oviposit after taking a blood meal.

homing endonuclease gene (HEG)

A “selfish” genetic element that can spread through the mosquito population even at a cost to the host. The HEG allele status is a tracked property of mosquitoes. Large numbers of HEG- positive mosquitoes may be introduced into the simulation by a MosquitoRelease intervention.

individual mosquito model

A vector model simulation where every individual mosquito in the population is modeled, as well as a simulation of a sampled subset of mosquitoes to represent the population as the whole.

indoor residual spraying (IRS)

The process of spraying insecticide on the interior surfaces of dwellings in order to repel or kill mosquitoes.

insecticide-treated nets (ITN)

Bednets hung over sleeping areas that are treated with insecticide to repel mosquitoes and kill those that land on them.

larval habitat

Mosquito larval ecology includes several habitat types including brackish swamp, constant, human population, temporary rainfall, and semi-permanent water vegetation.

larvicide

A node-targeted intervention that may be configured to have a direct killing effect on the larval population and a temporary reduction on available larval habitat.

oviposition

To deposit or lay eggs.

spatial repellent

An intervention that may be individually-distributed (protective indoors and outdoors), or node-distributed at the community-level, that repels mosquitoes from biting.

sugar-baited trap

Bait stations, often placed inside houses, that have an attractive and toxic sugar used to attract and kill sugar-feeding male and female mosquitoes.

vector

Insect or other living carrier that transmits an infectious agent. The vector life cycle and feeding cycle in the model are described in the article Eckhoff, Malaria Journal 2011, 10:303.

vector model

The vector model includes both a cohort model and an individual mosquito model. The model is a closed-loop feeding cycle where successful animal feeds (both indoors and outdoors) produce eggs, larva, vector mating, and then, adult mosquitoes. The vector model inherits the same human- infection model structure from the generic simulation type: uninfected, latent incubation, infectious, multiple immune variables, and superinfection. However, the transmission of infections is not between individual humans, but rather via the human-to-vector and vector- to-human pathways.

vectorial capacity

The daily rate of all mosquitoes that would be infected after biting a single infectious host. This measure can be used to describe the transmission intensity of malaria, and serves as a rate at which future infections arise from a currently infected host.

Wolbachia

This is a genus of bacteria that infects many arthopod species (especially insects). It has been discovered that insects infected with Wolbachia have increased resistance to viral or other parasitic infections. Work is underway to determine if strains of modified Wolbachia can be used to control malaria, by effectively preventing mosquitoes from becoming infected with Plasmodium parasites.

Malaria terms

circumsporozoite protein (CSP)

One of two proteins (the other being thrombospondin-related adhesive protein) involved in sporozoite recognition of host cells in malaria.

CRISPR

A genome-editing technique that utilizes the CRISPR/Cas9 system (a prokaryotic immune system) that can be used to splice DNA sequences in very specific target locations, enabling the precise insertion or removal of genes of interest.

cytokine

Cytokines are small secreted proteins that are released by cells, and have specific effects on interactions and communications between cells. They are especially important in the immune system, and can function in inflammatory (and anti-inflammatory) response.

gametocyte

The male and female sexual forms of the blood-stage malaria parasite. During blood-feeding, gametocytes are taken up into the mosquito mid-gut where they commence the mosquito-phase of the parasite life cycle.

gametocytes

The plural of gametocyte.

hepatocyte

The cell type that comprises liver tissue.

mass drug administration (MDA)

The treatment of an entire population in a geographic area with a curative dose of an antimalarial drug without first testing for infection, and regardless of the presence of symptoms.

merozoite

The malaria parasite stage that invades red blood cells. After each cycle of asexual reproduction within the infected red blood cell, merozoites are released into the bloodstream to invade new red blood cells.

merozoite surface protein (MSP)

A type of protein integral to red blood cell invasion by malaria parasites that is an important target of the human immune response.

merozoites

See merozoite.

Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1)

A protein encoded by the malaria parasite and expressed on the surface of infected red blood cells. PfEMP1 adheres to various human receptors, allowing sequestration of parasitized red blood cells and avoidance of clearance in the spleen. PfEMP1 is an important antigenic target of the human adapted immune response. It exhibits a high degree of antigenic variation via a genetic switching mechanism evolved to escape immune pressure.

pre-erythrocytic

Stages of a malaria parasite that occur before the red blood cells are invaded. This includes the period starting from inoculation with sporozoites, through the liver-stage multiplication, up to infected hepatocyte rupture, and the first invasion of erythrocytes by merozoites.

pre-erythrocytic vaccine (PEV)

A vaccine targeted at the earliest stage of malaria parasite development, namely before the blood-stage infection. Other vaccines target the asexual and sexual stages of the parasite.

schizont

Cells formed during the asexual stage of the life cycle of sporozoan protozoans, such as the malaria parasite.

sporogony

The asexual process of spore formation in parasitic sporozoans.

sporozoite

a motile, spore-like stage of the malaria parasite that is the infectious agent introduced into the host.

transmission-blocking vaccine (TBV)

A vaccine that blocks gametocytes from infected humans by producing viable sporozoites in mosquitoes. Also referred to as a “sexual-stage” vaccine, since vaccines in this class block gametocytes from infected humans by producing viable sporozoites in mosquitoes.

vaccine intervention types

In EMOD, vaccine intervention types are either “AcquisitionBlocking”, “TransmissionBlocking” or “MortalityBlocking”. For example, “mode-of-action”, “targeted parasite stages”, (such as pre-erythrocytic, asexual, or sexual), etc.

EMOD source code installation

This section describes how to install the software needed to build the EMOD executable (Eradication.exe) or Eradication binary for Linux from the EMOD source code. This is necessary if you want to modify the source code to extend the capabilities of the model beyond what’s available with the latest EMOD release. For example, you may want to model a disease that isn’t currently supported in EMOD. You can build Eradication.exe from source code on computers running Windows 10, Windows Server 12, and Windows HPC Server 12 (64-bit) or build the Eradication binary for Linux on computers running CentOS 7.1.

After building, you should run a single simulation to verify basic functionality. We recommend the 27_Vector_Sandbox scenario in the Regression directory, which is a simple five-year vector simulation with an indoor residual spraying (IRS) campaign in the third year, executed on a single-node geography that is based on Namawala, Tanzania. This simulation generally takes a few minutes to execute.

However, you can run a simulation using any of the subdirectories under Regression, which when used with the demographics and climate files provided by IDM, contain complete sets of files for EMOD simulations.

After that, we recommend running full regression tests to further verify that EMOD is behaving as expected and that none of the source code changes inadvertently changed the EMOD functionality. See Regression testing for more information.

The EMOD executable (Eradication.exe) is tested using Windows 10, Windows Server 12, and Windows HPC Server 12 (64-bit). Windows HPC Server is used for testing remote simulations on a high-performance computing (HPC) cluster and the other Windows operating systems are used to test local simulations.

The Eradication binary for Linux is tested and supported on a CentOS 7.1 virtual machine on Azure. It has also been successfully built and run on Ubuntu, SUSE, and Arch, but has not been tested and is not supported on those Linux distributions.

Install Windows prerequisites for EMOD source code

This section describes the software packages or utilities must be installed on computers running Windows 10, Windows Server 12, and Windows HPC Server 12 (64-bit) to build the EMOD executable (Eradication.exe) from source code and run regression tests.

If additional software is needed for the prerequisite software due to your specific environment, the installer for the prerequisite software should provide instructions. For example, if Microsoft MPI v10 requires additional Visual C++ redistributable packages, the installer will display that information.

Note

IDM does not provide support or guarantees for any third-party software, even software that we recommend you install. Send feedback if you encounter any issues, but any support must come from the makers of those software packages and their user communities.

Install prerequisites for running simulations

The following software packages are required to run simulations using Eradication.exe. If you already installed the pre-built Eradication.exe using the instructions in EMOD installation, you can skip this section.

  1. Install the Microsoft HPC Pack 2019 Client Utilities Redistributable Package (64-bit). See https://www.microsoft.com/en-us/download/details.aspx?id=101361 for instructions.

  2. Install the Microsoft MPI v10. See https://www.microsoft.com/en-us/download/details.aspx?id=100593 for instructions, being sure to run the MSMpiSetup.exe file.

  3. Install the Microsoft Visual C++ 2022 Redistributable (64-bit). See https://docs.microsoft.com/en-us/cpp/windows/latest-supported-vc-redist for instructions.

Install prerequisites for compiling from source code

The following software packages are required to build Eradication.exe from EMOD source code on Windows 10, Windows Server 12, and Windows HPC Server 12 (64-bit).

Visual Studio

  1. Purchase a license from Microsoft or use an MSDN subscription to install Visual Studio 2022 (Professional, Premium, or Ultimate). Other versions of Visual Studio are not supported.

    While you can use a free copy of Visual Studio Community, IDM does not test on or support this version.

  2. Select Desktop development with C++ during installation.

Python

Python is required for building the disease-specific Eradication.exe and running Python scripts.

  1. In a web browser, go to https://www.python.org/downloads/release/python-399/ to install Python 3.9 64-bit.

  2. Scroll down and download one of the x86-64 bit installers (you may use the executable installer or the web-based installer.)

  3. Double-click the executable file and, in the installer window, select the Add Python 3.9 to PATH checkbox and click Customize installation.

  4. On the Optional Features window that opens, leave all default values selected and click Next. The Python package manager, pip, is used to install other Python packages.

  5. If you are running EMOD locally, IDM recommends that you select the Advanced Options window to customize the installation directory to “C:\Python39”.

    You may install Python in another location, but the Python plotting scripts included in the EMOD scenarios zip file assume that Python is installed directly under the C: drive. If you install it elsewhere, you may need to edit those scripts when using the scenarios to learn about EMOD functionality.

  6. Click Install. When installation is complete, click Close.

  7. To verify installation, open a Command Prompt window and type the following:

    python --version

  8. From Control Panel, select Advanced system settings, and then click Environment Variables.

    • To build the source code using Python 3.9 64-bit, add a new variable called IDM_PYTHON3X_PATH and set it to the directory where you installed Python 3.9 64-bit, and then click OK.

  9. Restart Visual Studio if it was open when you set the environment variables.

HPC SDK
Boost

  1. Go to https://sourceforge.net/projects/boost/files/boost/1.77.0/ and select one of the compressed files.

  2. Unpack the libraries to the location of your choice. If unpacking the files results duplicate folders with an extra level of nesting (for example, C://boost_1_77_0//boost_1_77_0), delete the extra folder.

  3. From Control Panel, select Advanced system settings, and then click Environment Variables.

  4. Add a new variable called IDM_BOOST_PATH and set it to the directory where you installed Boost, and then click OK.

  5. Restart Visual Studio if it was open when you set the environment variable.

(Optional) SCons

SCons is required for the building disease-specific Eradication.exe and is optional for the monolithic Eradication.exe that includes all simulation types.

  1. Open a Command Prompt window and enter the following:

    pip install wheel
pip install scons
Install prerequisites for running regression tests

The following plotting software is required for running regression tests, where graphs of model output are created both before and after source code changes are made to see if those changes created a discrepancy in the regression test output. For more information, see Regression testing.

NumPy

We recommended that you download some of the NumPy Python package from http://www.lfd.uci.edu/~gohlke/pythonlibs, a page compiled by Christoph Gohlke, University of California, Irvine. The libraries there include linear algebra packages that are not included by default with the standard Windows packages. They are compiled Windows binaries, including the 64-bit versions required by EMOD. The naming convention used lists the Python version after “cp”, for example “cp39-cp39m”, and the Windows bit version after “win”, for example “win_amd64”.

The NumPy package adds support for large, multi-dimensional arrays and matrices to Python.

  1. Go to http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy and select the WHL file for NumPy 1.22.1 (64-bit) that is compatible with Python 3.9 64-bit.

  2. Save the file to your Python installation directory.

  3. Open a Command Prompt window and navigate to the Python installation directory, then enter the following, substituting the name of the specific NumPy WHL file you downloaded:

    pip install numpy-1.x.x+mkl-cp39-cp39m-win_amd64.whl
Python packages

The Python packages dateutil, six, and pyparsing provide text parsing and datetime functionality.

Note

Be sure NumPy is installed before you install Matplotlib.

  1. Open a Command Prompt window and enter the following:

    pip install python-dateutil
pip install pyparsing
pip install matplotlib
pip install future
(Optional) R

The IDM test team uses R 4.1.2 (64-bit) for regression testing, but it is considered optional.

R is a free software environment for statistical computing and graphics.

(Optional) MATLAB

The IDM test team uses MATLAB R2021b and the MATLAB Statistics and Machine Learning Toolbox™ R2021b for regression testing, but they are both considered optional.

MATLAB is a high-level language and interactive environment for numerical computation, visualization, and programming. The MATLAB Statistics and Machine Learning Toolbox™ provides functions and applications to describe, analyze and model data using statistics and machine learning algorithms.

  1. Go to http://www.mathworks.com/products/matlab/ and install MATLAB R2021b.

  2. If desired, go to https://www.mathworks.com/products/statistics.html and install the MATLAB Statistics and Machine Learning Toolbox™ R2021b.

Install CentOS prerequisites for EMOD source code

This section describes the software packages or utilities must be installed on computers running CentOS 7.1 to build the Eradication binary for Linux from source code and run regression tests.

If additional software is needed for the prerequisite software due to your specific environment, the installer for the prerequisite software should provide instructions. For example, if Microsoft MPI v10 requires additional Visual C++ redistributable packages, the installer will display that information.

Note

IDM does not provide support or guarantees for any third-party software, even software that we recommend you install. Send feedback if you encounter any issues, but any support must come from the makers of those software packages and their user communities.

Install prerequisites for running simulations

The following software packages are required to run simulations using the Eradication binary for Linux. If you already installed the pre-built Eradication.exe using the instructions in EMOD installation, you can skip this section.

Before you begin, you must have the following:

  • sudo privileges to install packages

  • 15 GB free in your home directory (if you install the EMOD source code and input files)

  • An Internet connection

  1. On GitHub on the EMOD releases page, download and run the PrepareLinuxEnvironment.sh script.

    Respond to the prompts for information while the script is running.

    1. Set the EMOD_ROOT environment variable to the path to the EMOD source path:

      EMOD_ROOT=~/IDM/EMOD

    2. Include Scripts and . in the path:

      export PATH=$PATH:.:$EMOD_ROOT/Scripts

    3. If you want to run simulations in the same session that you updated EMOD_ROOT and the Scripts path, reload the .bashrc file using source .bashrc.

Install prerequisites for compiling from source code

For CentOS 7.1, all prerequisites for building the Eradication binary for Linux are installed by the setup script described above. However, if you originally installed EMOD without including the source code and input files that are optional for running simulations using a pre-built Eradication binary for Linux, rerun the script and install those.

Install prerequisites for running regression tests

The setup script includes most plotting software required for running regression tests, where graphs of model output are created both before and after source code changes are made to see if those changes created a discrepancy in the regression test output. For more information, see Regression testing. You may want to install R or MATLAB, but both are optional.

We recommended that you download some of the NumPy Python package from http://www.lfd.uci.edu/~gohlke/pythonlibs, a page compiled by Christoph Gohlke, University of California, Irvine. The libraries there include linear algebra packages that are not included by default with the standard Windows packages. They are compiled Windows binaries, including the 64-bit versions required by EMOD. The naming convention used lists the Python version after “cp”, for example “cp39-cp39m”, and the Windows bit version after “win”, for example “win_amd64”.

(Optional) R

The IDM test team uses R 4.1.2 (64-bit) for regression testing, but it is considered optional.

R is a free software environment for statistical computing and graphics.

(Optional) MATLAB

The IDM test team uses MATLAB R2021b and the MATLAB Statistics and Machine Learning Toolbox™ R2021b for regression testing, but they are both considered optional.

MATLAB is a high-level language and interactive environment for numerical computation, visualization, and programming. The MATLAB Statistics and Machine Learning Toolbox™ provides functions and applications to describe, analyze and model data using statistics and machine learning algorithms.

  1. Go to http://www.mathworks.com/products/matlab/ and install MATLAB R2021b.

  2. If desired, go to https://www.mathworks.com/products/statistics.html and install the MATLAB Statistics and Machine Learning Toolbox™ R2021b.

Download the EMOD source code

The EMOD source code is available on GitHub. The EMOD source includes the source code, Visual Studio solution, sample configuration files, as well as regression test and other files needed to fully build and test the EMOD executable (Eradication.exe).

You can have multiple versions of the EMOD source code in separate directories on your local computer. For example, you might want to download a new release of EMOD but also keep a previous release of the source. In the following examples, the source code is downloaded to the directory EMOD at C:/IDM, but you can save to any location you want.

You can use any Git client of your choice to clone the EMOD repository from from GitHub. These instructions walk through the process using Git GUI or Git Bash if you are new to using Git.

Install Git GUI and Git Bash

To install Git GUI and Git Bash, download a 64-bit version of Git from https://git-scm.com/download. On the Select Components installer window, you can select one or both of Git GUI Here for a GUI or Git Bash Here for a command window.

Use Git GUI to download the EMOD source

  1. Launch the Git GUI application and click Clone Existing Repository.

  2. From the Clone Existing Repository window:

    1. In Source Location, enter https://github.com/InstituteforDiseaseModeling/EMOD

    2. In Target Directory, enter the location and target directory name: C:/IDM/EMOD

    3. Click Clone. Git GUI will create the directory and download the source code.

  3. Close the Git GUI window when the download completes.

Use Git Bash to download the EMOD source

Note

For a list of the Git Bash commands, type git help git or type git help <command> for information about a specific command.

  1. Launch the Git Bash application. From the command line:

    1. Navigate to the location where you want to save your copy of the EMOD source code:

      cd C:/IDM

    2. Clone the repository from GitHub:

      git clone https://github.com/InstituteforDiseaseModeling/EMOD

Git Bash will copy the EMOD source to a directory named EMOD by default.

Verify that all directories on https://github.com/InstituteforDiseaseModeling/EMOD are now reflected on your local clone of the repository.

(Optional) Download input files

IDM provides input files that describe the demographics, migration patterns, and climate of many different locations across the world. You can download these files from the EMOD-InputData repository, which uses large file storage (LFS) to manage the binaries and large JSON (JavaScript Object Notation) files. A standard Git clone of the repository will only retrieve the metadata for these files managed with LFS. To retrieve the actual data, follow the steps below.

  1. Install the Git LFS plugin, if it is not already installed.

    • For Windows users, download the plugin from https://git-lfs.github.com.

    • For CentOS users, the plugin is included with the PrepareLinuxEnvironment.sh script.

  2. Using a Git client or Command Prompt window, clone the input data repository to retrieve the metadata:

    git clone https://github.com/InstituteforDiseaseModeling/EMOD-InputData.git

  3. Navigate to the directory where you downloaded the metadata for the input files.

  4. Download the actual data on your local machine:

    git lfs fetch

  5. Replace the metadata in the files with the actual data:

    git lfs checkout

Build EMOD from source code

You can build the Eradication.exe for Windows 10, Windows Server 12, and Windows HPC Server 12 (64-bit) using Microsoft Visual Studio or SCons. You can build the Eradication binary for Linux for CentOS 7.1 using SCons.

If you want full debugging support, you must build using Visual Studio. However, Visual Studio is only capable of a monolithic build that includes all supported simulation types.

EMOD supports the following simulation types for modeling a variety of diseases:

  • Generic disease (GENERIC_SIM), which can be used for modeling a variety of diseases such as influenza or measles

  • Vector-borne diseases (VECTOR_SIM), which can be used for modeling vector-borne diseases such as dengue

  • Malaria (MALARIA_SIM), which adds features specific to malaria biology and treatment

  • Tuberculosis with HIV coinfection (TBHIV_SIM), which can be used for modeling TB transmission, with the option to add HIV coinfection as a contributing factor

  • Sexually transmitted infections (STI_SIM), which adds features for sexual relationship networks

  • HIV (HIV_SIM), which adds features specific to HIV biology and treatment

  • Environmental transmission (ENVIRONMENTAL_SIM), which adds features for diseases transmitted through contaminated food or water

  • Typhoid (TYPHOID_SIM), which adds features specific to typhoid biology and treatment

If you want to create a disease-specific build, you must build using SCons. However, SCons builds build only the release version without extensive debugging information.

Build a monolithic Eradication.exe with Visual Studio

You can use the Microsoft Visual Studio solution file in the EMOD source code repository to build the monolithic version of the EMOD executable (Eradication.exe), which can be either a release or debug build. Visual Studio 2022 (Professional, Premium, or Ultimate) is the currently supported version.

Warning

Visual Studio creates a debug build by default. However, you must use a release build to commission simulations to COmputational Modeling Platform Service (COMPS); attempting to use a debug build will result in an error.

  1. In Visual Studio, navigate to the directory where the EMOD repository is cloned and open the EradicationKernel solution.

  2. If prompted to upgrade the C++ toolset used, do so.

  3. From the Solution Configurations ribbon, select Debug or Release.

  4. On the Build menu, select Build Solution.

Eradication.exe will be in a subdirectory of the Eradication directory.

Build Eradication.exe or Eradication binary for Linux with SCons

SCons is a software construction tool that is an alternative to the well-known “Make” build tool. It is implemented in Python and the SCons configuration files, SConstruct and SConscript, are executed as Python scripts. This allows for greater flexibility and extensibility, such as the creation of custom SCons abilities just for EMOD. For more information on Scons, see www.scons.org. SCons 3.0.1 is the currently supported version.

Warning

EMOD will not build if you use the --Debug flag. To build a debug version, you must use Visual Studio.

  1. Open a Command Prompt window.

  2. Go to the directory where EMOD is installed:

    cd C:\IDM\EMOD

  3. Run the following command to build Eradication.exe:

    • For a monolithic build:

      scons --Release

    • For a disease-specific build, specify one of the supported disease types using the --Disease flag:

      scons --Release --Disease=Vector

  4. The executable will be placed, by default, in the subdirectory build\x64\Release\Eradication\ of your local EMOD source.

Additionally, you can parallelize the build process with the --jobs flag. This flag indicates the number of jobs that can be run at the same time (a single core can only run one job at a time). For example:

scons --Release --Disease=Vector --jobs=4

Getting started with building EMOD source code from CentOS Docker images

The EMOD source code can be built from the following CentOS Docker images available on JFrog Artifactory:

Image name

Repository path

Description

dtk-build

docker-production.packages.idmod.org/idm/centos:dtk-build

EMOD source code to build and run the EMOD executable (Eradication.exe).

dtk-runtime

docker-production.packages.idmod.org/idm/centos:dtk-runtime

EMOD files needed for running a pre-built CentOS EMOD executable (Eradication.exe).

dtk-sfts

docker-production.packages.idmod.org/idm/centos:dtk-sfts

EMOD source code to build test and run the EMOD executable (Eradication.exe).

You can build these images on Windows and Linux machines. The following steps were tested and documented from machines running Linux for CentOS 7.7 and Windows 10.

Prerequisites

  • privileges to install packages (sudo on Linux, admin on Windows)

  • An Internet connection

  • Git client

  • Docker client

To verify whether you have Git and Docker clients installed you can type the following at a command line prompt:

git --version
docker --version
Download and install prerequisites

Git version 1.8.3.1 and Docker CE version 19.03.3 were used for creating this documentation.

To download and install a Git client, see https://git-scm.com/download.

To download and install a Docker client for CentOS, see https://docs.docker.com/install/linux/docker-ce/centos/ To download and install a Docker client for Windows, see https://docs.docker.com/docker-for-windows/install/

Clone EMOD source code

To clone EMOD source code, type the following at a command line prompt:

git clone https://github.com/InstituteForDiseaseModeling/EMOD.git

The next steps are to run the Docker container and build the EMOD source code from the CentOS Docker images. If your host machine is running Linux, see Building EMOD source code from CentOS Docker images on Linux host machine. If your host machine is running Windows, see Building EMOD source code from CentOS Docker images on Windows host machine.

Building EMOD source code from CentOS Docker images on Linux host machine

These steps walk you through building the EMOD source code from CentOS Docker images on a Linux host machine running Linux for CentOS 7.7. Before following these steps you must meet the prerequisites in Getting started with building EMOD source code from CentOS Docker images. Although these steps are specific to the dtk-build image, you can use them for the other images by replacing “dtk-build” with the desired image name, such as “dtk-runtime” or “dtk-sfts”.

Download Docker image to Linux host machine

To download Docker image to your Linux host machine type the following at command line prompt:

docker pull docker-production.packages.idmod.org/idm/centos:dtk-build
Run Docker container from Linux host machine

To run Docker image from your Linux host machine type the following at command line prompt:

docker run -it --user `id -u $USER`:`id -g $USER` -v ~/EMOD:/EMOD docker-production.packages.idmod.org/idm/centos:dtk-build

To see additional information about the options used with the “docker run” command, type “docker run –help” at a command line prompt.

Upon completion you will then see a new command prompt for the Docker image, which in this example is as follows:

bash-4.2$

To then build the EMOD executable, Eradication.exe, move to the /EMOD directory:

cd /EMOD

This directories contains the necessary build script and files.

Build binary executable from Docker image running Linux CentOS 7.7 within Linux host machine

To build a binary executable you run the “scons” script. For more information about the build script options, type “scons –help” from within the /EMOD directory. In this example the Generic disease model is built. Type the following at command line prompt:

scons --Disease=Generic

Upon completion you will see the following line at the end of the build process:

scons: done building targets.

For more information, see Build EMOD from source code.

Verify EMOD binary executable

You can verify the successful build of the EMOD binary executable by typing the following at command line prompt:

/EMOD/build/x64/Release/Eradication/Eradication --version

You should see a response similar to the following:

Intellectual Ventures(R)/EMOD Disease Transmission Kernel 2.20.17.0
Built on Oct 25 2019 by luser from master(2d8a9f2) checked in on 2019-04-05 15:49:43 -0700
Supports sim_types: GENERIC.

You can then use this executable for running simulations. For more information, see Run a simulation using the command line.

Building EMOD source code from CentOS Docker images on Windows host machine

These steps walk you through building the EMOD source code from CentOS Docker images on a Windows host. Before following these steps you must meet the prerequisites in Getting started with building EMOD source code from CentOS Docker images. Although these steps are specific to the “dtk-build” image, you can use them for the other images by replacing “dtk-build” with the desired image name, such as “dtk-runtime” or “dtk-sfts”.

Download Docker image to Windows host machine

To download Docker image to your Windows host machine type the following at command line prompt:

docker pull docker-production.packages.idmod.org/idm/centos:dtk-build
Run Docker container from Windows host machine

To run Docker image from your Windows host machine type the following at command line prompt:

docker run -it -v C:\EMOD:/EMOD docker-production.packages.idmod.org/idm/centos:dtk-build

To see additional information about the options used with the “docker run” command, type “docker run –help” at a command line prompt.

Upon completion you will then see a new command prompt for the Docker image, which in this example is as follows:

[root@c23ffe9cb1fb /]#

To then build the EMOD executable, Eradication.exe, move to the /EMOD directory:

cd EMOD

This directories contains the necessary build script and files.

Build binary executable from Docker image running Linux CentOS 7.7 within Windows host machine

To build a binary executable you run the “scons” script. For more information about the build script options, type “scons –help” from within the EMOD directory. To specify a specific user name associated with the built binary executable, you can enter a specific user name by setting the “USER” environment variable that the build script uses. For example:

USER=johndoe

In this example the Generic disease model is built. Type the following at command line prompt:

scons --Disease=Generic

Upon completion you will see the following line at the end of the build process:

scons: done building targets.

For more information, see Build EMOD from source code.

Verify EMOD binary executable

You can verify the successful build of the EMOD binary executable by typing the following at command line prompt:

/EMOD/build/x64/Release/Eradication/Eradication --version

You should see a response similar to the following:

Intellectual Ventures(R)/EMOD Disease Transmission Kernel 2.20.17.0
Built on Oct 25 2019 by johndoe from master(2d8a9f2) checked in on 2019-04-05 15:49:43 -0700
Supports sim_types: GENERIC.

You can then use this executable for running simulations. For more information, see Run a simulation using the command line.

EMOD architecture

Epidemiological MODeling software (EMOD) is implemented in C++ and has two subsystems: the environment and the simulation controller. The environment contains the interfaces to the simulation controller subsystem and the external inputs and outputs. The simulation controller contains the epidemiological model (simulation and campaign management), and reporters that capture output data and create reports from a simulation. The following figure shows a high-level view of the system components of EMOD and how they are related to each other.

_images/ArchSystemComponents.png

EMOD system components

Warning

If you modify the source code to add or remove configuration or campaign parameters, you may need to update the code used to produce the schema. You must also verify that your simulations are still scientifically valid.

Environment subsystem

The environment subsystem provides access to global resources and most of the external input and output interfaces. Output reports are the only output not handled through the environment subsystem. It consists of a a singleton utility class, Environment. The environment subsystem consists of five logical components.

The following figure shows a high-level view of the system components of EMOD and how they are related to each other.

_images/ArchSystemComponents.png

EMOD system components

Input file readers

Provide small utility functions for reading in the input data, configuration, and campaign files. The actual parsing of the configuration and campaign files is done by the configuration manager component. The parsing of the input files is done by the model classes that consume that data, which are part of the simulation controller subsystem.

Configuration manager

Parses the configuration files, stores the parsed values for system-global configuration values, and provides a central container resource for the configuration as JSON to those classes that need to parse out the remaining configuration values locally for themselves. It retains the contents of the configuration file and the directories provided on the command line.

Error handler

Provides an application-level exception library and a centralized mechanism for handling all errors that occur during program execution. These errors are ultimately reported through the logging component.

Logger

Writes the output logs and errors. Output reports, such as time-series channel data reports, spatial channel data reports, progress and status summary, and custom reports, are handled by the simulation controller. For information and setting the logging level, see Set log levels.

Message Passing Interface (MPI)

EMOD is designed to support large-scale multi-node simulations by running multiple instances of EMOD in parallel across multiple compute nodes on a cluster. During initialization, geographical nodes are assigned to compute nodes. Individuals who migrate between geographical nodes that are not on the same compute node are migrated via a process of serialization, network I/O, and deserialization. The network communication is handled through a mixture of direct calls to the MPI library and use of Boost’s MPI facilities. This component provides the system-wide single interface to MPI, caching the number of tasks and rank of current process from the MPI environment.

Simulation controller subsystem

The simulation controller is the top-level structure for the epidemiological model. The controller’s capabilities are simple, running a single simulation in a single time direction at a constant rate. It exists to support future capabilities, such as running multiple simulations, pausing a simulation, or bootstrapping a simulation from an archived simulation. It contains two components: simulation and reporters.

The following figure shows a high-level view of the system components of EMOD and how they are related to each other.

_images/ArchSystemComponents.png

EMOD system components

Simulation

The simulation component contains core functionality that models the behavior of a disease without any interventions and extended functionality to include migration, climate, or other input data to create a more realistic simulation. Disease transmission may be more or less complex depending on the disease being modeled.

Campaign management

The simulation component also includes a campaign manager sub-component for including disease interventions in a simulation.

Reporter

The reporter component creates output reports for both simulation-wide aggregate reporting and spatial reporting.

Simulation core components

The simulation component contains core functionality that models the behavior of a disease without any interventions and extended functionality to include migration, climate, or other input data to create a more realistic simulation. Disease transmission may be more or less complex depending on the disease being modeled.

Warning

If you modify the source code to add or remove configuration or campaign parameters, you may need to update the code used to produce the schema. You must also verify that your simulations are still scientifically valid.

Content

Each generic EMOD simulation contains the following core base classes:

Simulation

Created by the simulation controller via a SimulationFactory with each run of EMOD.

Node

Corresponds to a geographic area. Each simulation maintains a collection of one or more nodes.

IndividualHuman

Represents a human being. Creates Susceptibility and Infection objects for the collection of individuals it maintains. The file name that defines this class is “Individual” and you may see it likewise shortened in diagrams.

Susceptibility

Manages an individual’s immunity.

Infection

Represents an individual’s infection with a disease.

For vector simulations, the following corresponding classes are derived from generic classes:

SimulationVector

Creates NodeVector objects instead of generic Node objects.

NodeVector

Creates IndividualHumanVector objects instead of generic IndividualHuman objects and creates and manages VectorPopulation objects to model the mosquito vectors.

IndividualHumanVector

Represents a human being and provides the additional layer of functionality for how vectors interact with individual humans.

VectorPopulation

The mosquito species at each node, which can be represented by a collection of cohorts that counts the population of a specific state of mosquitoes (VectorCohort) or by a collection of individual agent mosquitoes (VectorIndividual).

SusceptibilityVector

Represents a human being’s susceptibility to vector-borne disease.

InfectionVector

Represents a human being’s infection with a vector-borne disease.

Substitute these classes wherever you see the generic base classes in the architecture documentation.

For malaria simulations, the following corresponding classes are derived from vector classes:

SimulationMalaria

Creates NodeMalaria objects instead of generic Node objects.

NodeMalaria

Creates IndividualHumanMalaria objects instead of generic IndividualHuman objects and provides various malaria-specific counters for the purposes of reporting.

IndividualHumanMalaria

Represents a human being and provides the additional layer of functionality for how malaria vectors interact with individual humans.

SusceptibilityMalaria

Represents a human being’s susceptibility to malaria. It contains much of the intra-host model, by modeling the specific details of an individual’s immune system in the context of the malaria infection life cycle. It is highly configurable, and interacts closely with InfectionMalaria objects and the IndividualHumanMalaria object, its parent. From a software point of view, SusceptibilityMalaria derives from SusceptibilityVector, but in reality SusceptibilityVector provides minimal epidemiological functionality.

InfectionMalaria

Represents a human being’s infection with malaria. It is the other part of the detailed intra-host malaria model. It models the progression of the malaria parasite through sporozoite, schizont, hepatocyte, merozoite, and gametocyte stages. InfectionMalaria objects are contained with IndividualMalaria objects. There can be multiple such objects. They all interact closely with the SusceptibilityMalaria object. From a software point of view, InfectionMalaria derives from InfectionVector, but in reality InfectionVector provides minimal epidemiological functionality.

For generic simulations, human-to-human transmission uses a homogeneous contagion pool for each node. Every individual in the node sheds disease into the pool and acquires disease from the pool. For vector-borne diseases, disease transmission is more complex as it must take into account the vector life cycle. See Disease transmission.

The relationship between these classes is captured in the following figure.

_images/ArchSimulation.png

Simulation components

After the simulation is initialized, all objects in the simulation are updated at each time step, typically a single day. Each object implements a method Update that advances the state of the objects it contains, as follows:

  • Controller updates Simulation

  • Simulation updates Nodes

  • Node updates IndividualHuman

  • IndividualHuman updates Susceptibility, Infections, and InterventionsContainer

  • InterventionsContainer updates Interventions

Simulation

As a stochastic model, EMOD uses a random number seed for all simulations. The Simulation object has a data member (RNG) that is an object maintaining the state of the random number generator for the parent Simulation object. The only generator currently supported is pseudo-DES. The random seed is initialized from the configuration parameter Run_Number and from the process MPI rank. All child objects needing access to the RNG must be provided an appropriate (context) pointer by their owners.

The Simulation class contains the following methods:

Method

Description

Populate()

Initializes the simulation. The Populate method initializes the simulation using both the configuration file and the demographic files. Populate calls through to populateFromDemographics to enable the Simulation object to create one or many Node objects populated with IndividualHumans as dictated by the demographics file, in conjunction with the sampling mode and value dictated by the configuration file. If the configuration file indicates that a migration and a climate model are to be used, those input file are also read. Populate also initializes all Reporters.

Update()

Advances the state of nodes.
_images/ArchSimulationTree.png

Simulation object hierarchy

For multi-core parallelization, the demographics file is read in order on each process and identity of each node and is compared with a policy assigning nodes to processes embodied in objects implementing InitialLoadBalancingScheme. If the initial load balancing scheme allows a node for the current rank, the node is created via addNewNodeFromDemographics. After all nodes have been created and propagated, the NodeRankMaps are merged across all processes. See Load-balancing files for more information.

Node

Nodes are model abstractions that represent a population of individuals that interact in a way that does not depend on their geographic location. However, they represent a geographic location with latitude and longitude coordinates, climate information, migration links to other nodes, and miscellaneous demographic information. The Node is always the container for IndividualHumans and the contagion pool. The Node provides important capabilities for how IndividualHumans are created and managed. It can also contain a Climate object and Migration links if those features are enabled. The climate and migration settings are initialized based on the information in the input data files.

The Node class contains the following methods:

Method

Description

PopulateFromDemographics()

The entry point that invokes populateNewIndividualsFromDemographics(InitPop), which adds individuals to a simulation and initializes them. PopulateNewIndividualsFromBirth() operates similarly, but can use different distributions for demographics and initial infections.

Update()

Advances the state of individuals.

updateInfectivity()

The workhorse of the simulation, it processes the list of all individuals attached to the Node object and updates the force of infection data members in the contagion pool object. It calls a base class function updatePopulationStatistics, which processes all individuals, sets the counters for prevalence reporting, and calls IndividualHuman::GetInfectiousness for all IndividualHuman objects.

The code in GetInfectiousness governs the interaction of the IndividualHuman with the contagion pool object. The rest of the code in updateInfectivity processes the contagion beyond individual contributions. This can include decay of persisting contagion, vector population dynamics, seasonality, etc. This is also where the population-summed infectivity must be scaled by the population in the case of density-independent transmission.

updateVitalDynamics()

Manages community level vital dynamics, primarily births, since deaths occur at the individual level.

By default, an IndividualHuman object is created, tracked, and updated for every person within a node. To reduce memory usage and processing time, you may want to sample such that each IndividualHuman object represents multiple people. There are several different sampling strategies implemented, with different strategies better suited for different simulations. See Sampling for more information.

If migration is enabled, at the end of the Node update, the Node moves all migrating individuals to a separate migration queue for processing. Once the full simulation time step is completed, all migrating individuals are moved from the migration queue and added to their destination nodes.

IndividualHuman

The IndividualHuman class corresponds to human beings within the simulation. Individuals are always contained by a Node object. Each IndividualHuman object may represent one or more human beings, depending on the sampling strategy and value chosen.

The IndividualHuman class contains components for susceptibility, infection, and interventions. Infection and Susceptibility cooperate to represent the detailed dynamics of infection and immune mechanisms. Every IndividualHuman contains a Susceptibility object that represents the state of the immune system over time. Only an infected IndividualHuman contains an Infection object, and may contain multiple Infection objects.. Susceptibility is passed to initialize the infection immunology in Infection::InitInfectionImmunology(). The state of an individual’s susceptibility and infection are updated with Update() methods. Disease-specific models have additional derived classes with properties and methods to represent specifics of the disease biology.

The InterventionsContainer is the mediating structure for how interventions interrupt disease transmission or progression. Campaign distribution results in an Intervention object being added to an individual’s InterventionsContainer, where it remains unless and until it is removed. When an IndividualHuman calls Update(), the InterventionsContainer is updated and its effects are applied to the IndividualHuman. These effects are used in the individual, infection, and susceptibility update operations. If migration is enabled, at the end of each individual’s update step EMOD checks if the individual is scheduled for migration (IndividualHuman::CheckForMigration()), setting a flag accordingly.

The IndividualHuman class contains the following methods:

Method

Description

Update()

Advances the state of both the infection and the immune system and then registers any necessary changes in an individual’s state resulting from those dynamics (that is, death, paralysis, or clearance). It also updates intrinsic vital dynamics, intervention effects, migration, and exposure to infectivity of the appropriate social network.

ExposeToInfectivity()

Passes the IndividualHuman to the ExposeIndividual() function if it is exposed to infectivity at a time step.

UpdateInfectiousness()

Advances the quantity of contagion deposited to the contagion pool by an IndividualHuman at each time step of their infectious period. This is explained in more detail below.
Disease transmission

Transmission of disease is mediated through a pool mechanism which tracks abstract quantities of contagion. The pool mediates individual acquisition and transmission of infections as well as external processes that modify the infectivity dynamics external to individuals. The pool provides basic mechanisms for depositing, decaying, and querying quantities of contagion which are associated with a specific StrainIdentity. The pool internally manages a separate ContagionPopulation for each possible antigen identity. ContagionPopulations have further structure and manage an array of contagion quantities for each substrain identity.

Each IndividualHuman has a sampling weight \(W_i\) and a total infectiousness \(X_i\), the rate at which contacts with the infectious individual become infected. This rate can be modified by transmission-reducing immunity or heterogeneous contact rates, which are gathered in \(Y_{i,transmit}\), and the transmission-reducing effects of interventions, such as transmission- blocking vaccines in the factor \(Z_{i,transmit}\). The sampling weight \(W_i\) is not included in the probability of acquiring a new infection. Sample particles are simulated as single individuals, their weighting \(W_i\) is used to determine their effects upon the rest of the simulation.The total infectiousness \(T\) of the local human population is then calculated as:

\[T = \sum_iW_iX_iY_{i,transmit}Z_{i,transmit}\]

For simulation of population density-independent transmission, individual infectiousness \(X_i\) includes the average contact rate of the infectious individual, so this total infectiousness is divided by the population \(P\) to get the force of infection \(FI=\frac{T}{P}\) for each individual in the population. The base rate of acquisition of new infections per person is then \(FI\), which can be modified for each individual \(I\) by their characteristics \(Y_{i,acquire}\) and interventions \(Z_{i,acquire}\). Over a time step \(\Delta t\), the probability of an individual acquiring a new infection is then:

\[P_{I,i} = 1- \text{exp}(-F_IY_{i,acquire}Z_{i,acquire}\Delta t)\]

A new infection receives an incubation period and infectious period from the input configuration (a constant at present, but possibly from specified distributions in the future) and a counter tracks the full latency, which is possible when simulating individual particles. After the incubation period, the counter for the infectious period begins, during which time the infection contributes to the individual’s infectiousness \(X_i\).

For vector-borne diseases, during an Update() for a node, the infectiousness of vectors is calculated on the local human population regarding the vector life cycle, along with the effects their interventions, such as bednets. Weather data from the Climate object is then used to update the available larval habitat for each local vector species. Multiple local vector species are supported, and after the weather updates, each vector species is advanced through a time step with the VectorPopulation::TimeStep() method. This calculates the life cycle and vector infection dynamics, along with the feeding cycle. It updates the indoor and outdoor bites, and indoor and outdoor infectious bites on the local human population. Each Individual in the local human population is then advanced in an Update(), propagating its infections forward in time, updating its interventions and infectivity, and acquiring any new infections.

NodeVector::updateInfectivity() calls the VectorPopulation::TimeStep() method for mortality adjustments due to weather and the human population and, when completed, processes each list. The effects of the human population are accounted for in VectorPopulation::Update_Host_Effects(), which determines the outcomes for indoor and outdoor attempted feeds on each individual human, and then on the human population as a whole, weighting by any heterogeneous biting. The processing order is:

  1. All infectious female mosquitoes

  2. All infected female mosquitoes

  3. All adult uninfected female mosquitoes

  4. All adult male mosquitoes

  5. Immature mosquitoes

  6. All mosquito larvae

  7. All mosquito eggs

After each list is updated, all indoor and outdoor bites, both infectious and non-infectious, are tallied and updated within the Node infectivity objects.

Within a list update, each cohort of identical-state mosquitoes or each individual-agent mosquito experiences risk of mortality, may progress through any stage of development, such as sporogony (for infected) or larval maturation (for larval), and may attempt a blood feed (for adult females). For mosquitoes attempting a blood feed, outcomes are governed by the calculated host effects, with sequential conditional draws for choice of host type, choice of indoor or outdoor feeding location, and feed outcome. The potential feed outcomes are die before feeding, no host found, die during feeding, die post-feeding, and successful feed. Successful feeds result in ovipositions, and eggs laid start the next generation of mosquitoes.

Campaign management

Campaigns are structured into campaign events that determine when, where, and to whom the intervention will be distributed and interventions that determine what is distributed, for example vaccines or other treatments. This section describes the software architecture used for managing campaigns.

Campaigns can be very simple, such as a single intervention with fixed parameters at a point in time, or a complex, adaptive, repetitive, and responsive combination of interventions. A Campaign is a collection of events that modify a simulation. An Event is a distribution within a campaign, and an Intervention is an item that is given out, such as a vaccine, a drug or a bednet.

_images/ArchCampaignManagement.png

Warning

If you modify the source code to add or remove configuration or campaign parameters, you may need to update the code used to produce the schema. You must also verify that your simulations are still scientifically valid.

Campaign events

A campaign event triggers the campaign management system to evaluate when, where, and to whom the intervention will be distributed. This section describes the SimulationEventContext, CampaignEvent, NodeSet, and EventCoordinator classes used to manage this process.

For more information on setting up campaign files, see Creating campaigns.

SimulationEventContext

The Simulation class has a nested helper class called SimulationEventContextHost, referred to as SimulationEventContext. It serves as the interface to, or manager of, CampaignEvents for the Simulation class.

Incoming: The Simulation to SimulationEventContext interface contains the following methods:

Method

Description

LoadCampaignFromFile()

Loads the campaign file during simulation initialization (t=0).

RegisterEventCoordinator()

Registers new event coordinators. Also used by CampaignEvent during activation to notify the SimulationEventContext of a new active EventCoordinator.

Update()

Advances the state at each time step.

VisitNodes()

Called from CampaignEvent during its activation. CampaignEvent is not a Node container, so it needs to communicate with the Simulation class to do anything with nodes.

Outgoing: The SimulationEventContext communicates with CampaignEvents and EventCoordinators, which are described below.

CampaignEvent

The CampaignEvent class exists primarily as an initialization-time, minimal intelligence container with a 1-to-1 mapping for CampaignEvents found in the JSON campaign file. They do not do much in the way of run-time campaign distribution or control.

Incoming: The public interface for CampaignEvent contains the following methods:

Method

Description

CreateInstance()

For each campaign event that the SimulationEventContext finds in the campaign file, it creates a corresponding CampaignEvent instance via CampaignEventFactory. The nested JSON elements in a campaign event are stored as an opaque (that is, unparsed) JSON object known as Event_Coordinator_Config. Immediate parameters like Start_Day are parsed out of the JSON and stored in explicit variables in the CampaignEvent class.

Dispatch()

In its steady-state, the SimulationEventContext checks the start day for all existing CampaignEvents and invokes this method to when the time step matches the start day.

Outgoing: The CampaignEvent communicates with two other classes: NodeSet and EventCoordinator.

NodeSet

The NodeSet class parses the Nodeset_Config and helps the EventCoordinator determine if a given node is included in a particular intervention distribution. It can be as simple as NodeSetAll, which includes all nodes, or something much more involved. Usual cases include NodeSetPolygon and NodeSetCommunityList.

Incoming: The public interface for NodeSet contains the following methods:

Method

Description

Contains()

The caller passes a Node via the INodeEventContext interface and gets back a Boolean. The NodeSet class completely encapsulates whatever logic is used to evaluate node membership.
EventCoordinator

EventCoordinator is a base type that contains most of the functionality in the event distribution mechanism. There are two actual EventCoordinator classes implemented, but you can new implementations easily by creating a new EventCoordinator class.

Incoming: There are four classes or class groups that make calls into EventCoordinator:

  • CampaignEvents, which create and activate them.

  • The SimulationEventContext that invokes their steady-state methods.

  • Nodes (via the NodeEventContext helper class)

  • Interventions, which get EventCoordinator-level configuration data needed for intervention execution.

The public interface for EventCoordinator contains the following methods:

Method

Description

AddNode()

Invoked by CampaignEvent::Dispatch() to allow the EventCoordinator to act as a node container and have access to all its constituent nodes via the INodeEventContext interface.

Note

The AddNode calls actually come via a delegate function that is in the EventCoordinator, but is invoked from the VisitNodes method up in the SimulationEventContext. The CampaignEvent passes the delegate function it wants used when it calls SimulationEventContext::VisitNodes() . This circuitous journey is needed because EMOD operates on a node-by-node basis and the SimulationEventContext, not the CampaignEvent, is the primary node container.

CreateInstance()

During their instantiation, CampaignEvents create EventCoordinators as the CampaignEvents parse the Event_Coordinator_Config, which specifies the class name and is passed by SimulationEventContext. Nothing then happens until CampaignEvent::Dispatch() is called.

IsFinished()

When an EventCoordinator determines that it is finished distributing its interventions, it sets an internal flag. The SimulationEventContext queries the IsFinished() function on each registered EventCoordinator and disposes of it if true. An EventCoordinator can activate and complete in a single time step, or it could last the entire length of the simulation.

Update()

The SimulationEventContext calls this on all registered EventCoordinators to advance their state at each time step. This method exists for global EventCoordinator communication and logic.

UpdateNodes()

Distributes the actual intervention. For the simplest possible use case, an EventCoordinator will distribute an intervention on its start day and then finish in that same time step. However, an EventCoordinator may implement repeating interventions, phased distributions, or sustained distributions that are contingent on node or individual attributes.

Outgoing: The outgoing function calls consist of the distribution of the intervention either to individuals or nodes. For the interface between the EventCoordinator and the actual Intervention, the EventCoordinator calls InterventionFactory and passes it the remaining unparsed JSON from the campaign file. Namely, this is the Intervention_Config, the actual intervention-level campaign parameters including the intervention class. The InterventionFactory returns the newly created intervention instance for each individual via a one of two types of IDistributableIntervention interfaces.

Interventions

Interventions are the actual objects aimed at reducing the spread of disease, such as a vaccine or drug. THey can be either distributed to individuals or to nodes. For example, a vaccine is an individual-distributed intervention and a larvicide is a node-distributed intervention. Intervention is an abstract type class and specific interventions are classes that implement either IDistributableIntervention, for individual-distributed interventions, or INodeDistributableIntervention, for node-distributed interventions.

The determination of whether an intervention is individual-distributed or node-distributed is contained by logic in InterventionFactory. At the beginning of the EventCoordinator::UpdateNodes()**method, the **InterventionFactory is queried with the unparsed JSON campaign file to see if it returns an INodeDistributableInterface pointer. If it does, the intervention is executed as a node-distributed intervention; if it does not, the individual- distributed intervention execution is used.

Incoming: The interface to the abstract type Intervention class is the Distribute() method. It functions slightly differently for each intervention type.

Outgoing: Intervention is an abstract type that interacts with Nodes or IndividualHumans with an interface specific to that intervention type. The intervention calls QueryInterface on the container to ask for a consumer interface, for example IBednetConsumer. If supported, a Give method specific to that intervention will be called, for example, GiveBednet(this) to give itself to this individual. It is then up to the internal IndividualHuman or Node logic to decide what to do with this intervention.

Individual-distributed interventions

An individual-distributed intervention implements IDistributableIntervention, a container for the actual parameter name-value pairs from the campaign file, that is contained by an IndividualContainer, an IndividualHuman helper class. The EventCoordinator calls IDistributableIntervention::Distribute(), passing the pointer IIndividualHumanDistributionContext, which provides an ISupports interface to the individual by which the intervention can request a consumer interface. This interface can then be used with Give().

In the EventCoordinator::UpdateNodes() method, the EventCoordinator organizes the distribution of Interventions to IndividualHumans mediated by Nodes, which contain the IndividualHuman objects. The EventCoordinator also applies campaign parameters at the event coordinator level (for example, Target_Demographic, Demographic_Coverage, Num_Repetitions, and Days_Between_Reps) which mainly consists of filtering the distribution to individuals based on individual attributes.

Node-distributed interventions

A node-distributed intervention implements INodeDistributableIntervention, a container for the actual parameter name-value pairs from the campaign file, that is contained by a Node. The EventCoordinator calls INodeDistributableIntervention::Distribute(), passing the pointer INodeEventContext, which provides an ISupports interface to the individual by which the intervention can request a consumer interface. This interface can then be used with Give(). Similar to individual-distributed interventions, the EventCoordinator also applies campaign parameters for filtering the distribution.

The architectural diagram below illustrates how campaign file settings are processed by an EMOD simulation.

_images/ArchCampaignFlow.png

EMOD campaign architecture

Reporter component

After running simulations, EMOD creates output reports that contain the model results. Two methods of coordinated reporting are implemented: simulation-wide aggregated reporting and spatial reporting. See Output files (reports) for more information about the different output reports available.

Simulation-wide aggregate

Simulation-wide aggregate reporting is the most commonly used. This reporting method generates the output written to InsetChart.json. Conceptually, for each time step, the value of a named channel is derived from values provided for that channel by each node. The values are accumulated (summed) over all nodes and then transformed (often normalized by an internal parameter or another channel) just prior to writing. This is implemented through the simulation’s insetChartDataAccumulator.

Basic usage of the NodeTimestepDataAccumulator is explained in Report.h. The simulation is responsible for calling Begin/EndTimestep() and collecting and writing out the data at the end of the simulation. The accumulation calls occur in Node::accumulateInsetChartData().

Spatial

The second method is spatial reporting which is facilitated by SpatialReporter. In spatial reporting, each node again produces values for named channels, but no simulation-wide accumulation takes place. Instead, the values of each channel for each node are written to a binary table (ReportNNNN.dat) where NNNN is the time step. The format of this file is simple and can be summarized as the maximally packed layout of this structure:

struct ReportDataFormat
{
    int32_t num_nodes;
    int32_t num_channels;
    float data[num_nodes*num_channels];
}

Num_Channels is the number of user channels implicitly generated by the number of different channel names requested in calls to Node::reportSpatialDataReportChannels() plus two additional channels for longitude and latitude. JSON metadata for the ReportNNNN.dat files is written to Report.header.dat after the first time step. It does not include entries for latitude and longitude.

In general, the value of channel \(c\) for node \(n\), starting at zero, is found at \(data[n\times num\_channels+c]\). The latitude is \(data[n\times num\_channels+0]\) and the longitude is \(data[n\times num\_channels+1]\).

The report data files are written after every time step at the request of the Controller by calling WriteTimestep(). Under MPI, the default implementation reduces all the data to rank 0 and writes the combined data out to file on rank 0.

See Custom reporters for information about how to use a custom reporter.

Debugging and testing

When you build Eradication.exe or Eradication binary for Linux from the EMOD source code, it’s important to debug the code and run regression tests to be sure your changes didn’t inadvertently change EMOD functionality. You can run simulations directly from Visual Studio to step through the code, troubleshoot any build errors you encounter, and run the standard EMOD regression tests or create a new regression test.

Warning

If you modify the source code to add or remove configuration or campaign parameters, you may need to update the code used to produce the schema. You must also verify that your simulations are still scientifically valid.

Set log levels

EMOD simulations output several files, including log files. See Error and logging files for more information on the logging files that are created by a simulation. By default, logging is at the “INFO” level, but you can set the level at which you want logging information generated. If you see redundant log messages, you can also throttle logging to eliminate them.

There are five log levels. The level chosen will log messages for that level and all higher levels (levels of smaller numeric value). You can set default log levels across the entire Eradication.exe or for individual files in the Eradication.exe; individual file settings will override the default. For example, if you want to increase logging for a particular file you are debugging without increasing logging across the entire Eradication.exe, you can set the default log level to “WARNING” and set the log level for the file to “DEBUG”. The standard output will reflect the default log level for all settings except “ERROR” or “WARNING”. For example:

Log-levels:
        Default -> INFO
        Eradication -> INFO

The following log levels are available:

ERROR (1)

This is the highest level. Only errors, including critical errors, are logged to standard output.

WARNING (2)

Warnings and errors are logged to standard output.

INFO (3)

This is the default logging level for the executable and is set implicitly. Informational messages, warnings, and errors are logged to standard output.

DEBUG (4)

Debug information, informational messages, warnings, and errors are logged to standard output. Debug level logging messages do not require a debug build of Eradication.exe. This setting will generate a large number of messages and may impact performance. If you need the debugging messages, we recommend that you limit the number of files with this setting and not set it as the default across the entire Eradication.exe.

VALID (5)

This is the lowest level. Validation information, debug information, informational messages, warnings, and errors are logged to standard output. This log level is for very low-level debugging of specific values, including values in tight loops. Because it could severely impact performance, this level will only output to standard output during a debug build of Eradication.exe.

Set default Eradication.exe level

To set the default log level across the entire Eradication.exe, add the parameter logLevel_default to your configuration file and set it to the log level desired. For example:

{
    "logLevel_default": "WARNING"
}
Set level for individual files

You can set the log level for an individual file, if it supports the ability. This is typically only used for debugging after making source code changes. This setting will override the default set using logLevel_default, whether set to a higher or lower level.

  1. In the C++ files that make up the EMOD source code, open the file for which you want to set the log level.

  2. Search for static const char * _module.

    “Module” refers to the C++ file and is not the same as an EMODule. If this string isn’t present, you cannot set a log level for the file.

  3. Look for logging macros in the file, such as LOG_WARN(x).

    If macros are present, this indicates that the file supports that log level setting. The file may not support all log levels. Logging macros can be found in utils/Log.h. You can also add your own logging messages to the file using the logging macros.

  4. In the configuration file, add the logLevel_<FileName> parameter with the log level desired. For example:

    {
    "logLevel_SimulationVectorExport":"DEBUG"
}
Throttle redundant logging

It’s possible that multiple identical logging messages are generated by a file of Eradication.exe. This is normal behavior, but the verbosity can be unwanted or unnecessary. You can eliminate duplicate messages by turning on “throttling”, which retains only one logging message instead of a series of duplicate messages from each file.

Only one copy of the message will be generated when it is one of a series of duplicates from the same file. The identical messages need not be output one right after one another to be throttled. If an identical message is output from a different file, it will still be generated. Throttling can only be enabled across the entire Eradication.exe, not per file. It is off by default.

To enable throttling, add the following parameter and value to your configuration file:

{
    "Enable_Log_Throttling": 1
}

For example, the following log messages are seen with throttling turned off:

00:00:00 [0] [D] [FileA] identical message: I'm in FileA
00:00:00 [0] [D] [FileB] another message from B
00:00:00 [0] [D] [FileA] identical message: I'm in FileA
00:00:00 [0] [D] [FileB] different message from B
00:00:00 [0] [D] [FileA] identical message: I'm in FileA
00:00:00 [0] [D] [FileB] yet another message from B.
00:00:00 [0] [D] [FileA] identical message: I'm in FileA

With throttling on, the repeated messages from file A are removed, even though they are intermixed with other log messages from file B:

00:00:00 [0] [D] [FileA] identical message: I'm in FileA
00:00:00 [0] [D] [FileB] another message from B
00:00:00 [0] [D] [FileB] different message from B
00:00:00 [0] [D] [FileB] yet another message from B.

Run debug simulations in Visual Studio

If you have modified the EMOD source code, you can run simulations directly from Visual Studio as part of debugging your changes by using the built-in debugger. For example, you may want to run one or more of the simulations in the Regression directory to verify that the results match those obtained prior to your changes.

  1. Open the EradicationKernel solution in Visual Studio.

  2. On the Solution Explorer pane, right-click the Eradication project and select Properties.

  3. On the Property Pages window, in the Configuration Properties pane, click Debugging.

  4. Set the Command Arguments and Working Directory to the appropriate values and click OK. See Run a simulation using the command line for more information.

  5. On the Debug menu, do one of the following:

    • Click Start Without Debugging to run the simulation in Release mode, where the simulation runs with essentially the same behavior and performance as running it at the command line.

    • Click Start Debugging to run the simulation in Debug mode, where you have the ability to add break points and step through the code, inspecting the values of different variables throughout the simulation.

Troubleshooting EMOD builds

If you encounter any of the following warnings or errors when attempting to build the EMOD executable (Eradication.exe) or Eradication binary for Linux, see the information below to resolve the issue.

If you need assistance, you can contact support for help with solving issues. You can contact Institute for Disease Modeling (IDM) support at support@idmod.org. When submitting the issue, please include any error information.

Unknown compiler version

If you encounter the warning “Unknown compiler version - please run the configure tests and report the results” when attempting to build the Eradication.exe or Eradication binary for Linux, this indicates you are using a version of Boost that is no longer supported. Install Boost 1.77.0.

Inconsistent DLL linkage

If you see the following warning on some files, “c:python27includepymath.h(22): warning C4273: ‘round’: inconsistent dll linkage”, this indicates that you are using a version of Python that is no longer supported. Install Python 3.9 64-bit.

Error 255

Check to see if you have any white spaces in the path to your local EMOD source code. If you do, remove the white spaces in all of the directory names in the path.

Error LNK4272

If you see the error “LNK4272 library machine type ‘X86’ conflicts with target machine type ‘x64’”, this indicates that you need to uninstall 32-bit Python and reinstall 64-bit Python. Install Python 3.9 64-bit.

Regression testing

After building the EMOD executable (Eradication.exe), it’s important to verify that Eradication.exe is performing properly. Regression testing is a method by which the built code is tested to see if it has “regressed” or moved backwards in any way, such as previously reported (and fixed) issues reappearing.

Within the EMOD Regression directory there are many subdirectories that correspond to different disease scenarios in a variety of locations. Each of these contains the configuration and campaign files needed to run the simulation and the reference output, which represents the expected results. These reference outputs have been calculated by the scientific researchers modeling each scenario. Configurations and campaigns that use base and overlay files will be flattened as part of the regression tests.

EMOD regression testing consists of running one or more of these simulations and comparing the output to the reference output. If the two outputs are comparable, the test passes; if they differ, the test fails. Because EMOD is stochastic, a passing test will fall within some acceptable range of the reference output, rather than be an identical match. If a regression test fails, EMOD will produce a matplotlib chart of the first 16 channels in the InsetChart.json output report. You can then review the charts to identify the problem. Base and overlay configuration files will be flattened

If you want to quickly compare a simulation output to the reference output, you can also run any of the regression scenarios as a typical simulation, as described in Running a simulation. However, this will not include the comparison and pass/fail evaluation that regression_test.py conducts. In addition, if you choose to do this, be sure to specify a different output directory, such as “testing”, so as not to overwrite the reference output.

Run regression tests

The Python script regression_test.py runs a suite of regression simulations and compares the output to the reference output for each simulation. It is set up to run the simulations on an HPC cluster; however, you can run modify the script to run tests locally. However, the script was written with remote execution in mind and running it locally can be time-consuming. Running the entire regression suite locally will take several hours on average.

The regression scenarios, script, configuration file, and other relevant files are all in the Regression directory. Be aware that many of these tests, due to abnormally high or low values, will produce output that should not be considered scientifically accurate.

  1. Modify the configuration file, regression_test.cfg, for your environment, setting the values for the location of the working directory, input files, binary file, and cluster settings.

    • For local Windows simulations, set the values under [WINDOWS].

    • For local CentOS simulations, set the values under [POSIX]. Note that CentOS simulations are run locally by default and cannot be commissioned to an HPC cluster.

    • For simulations on IDM HPC clusters, no changes are necessary if your username and password are cached locally.

    • For simulations on your own HPC cluster, create [HPC-<cluster>] and [ENVIRONMENT-<cluster>] sections for your cluster that contain the same variables as shown for IDM HPC clusters.

  2. Select the suite of regression tests you want to run. This is indicated by a JSON file in the following format:

    {
    "tests": [{
        "path": "Relative path to test directory."
    }, {
        "path": "Relative path to test directory."
    }]
}

    You can use one of the JSON files in the Regression directory or create your own. The sanity.json file is recommended for quickly testing a wide range of EMOD functionality.

  3. From the Regression directory, open a Command Prompt window and run the regression test script, regression_test.py. It requires the name of the regression suite (without the .json extension) and the relative path to Eradication.exe. For example:

    regression_test.py sanity ..\Eradication\x64\Release\Eradication.exe

    In addition, you may need to include the following optional arguments depending on your testing environment or how Eradication.exe was built.

    Argument

    Default

    Description

    --perf

    False

    Measure Eradication.exe performance.

    --hidegraphs

    False

    Suppress pop-up graphs in case of validation failures.

    --debug

    False

    Use the debug path for EMODules.

    --label

    Add a custom suffix for HPC job name.

    --config

    regression_test.cfg

    The regression test configuration file.

    --disable-schema-test

    True

    Include to suppress schema testing, which is on by default.

    --use-dlls

    False

    Use EMODules when running tests.

    --all-outputs

    False

    Use all output JSON files for validation, not just InsetChart.json.

    --dll-path

    The path to the root directory of the EMODules to use.

    --skip-emodule-check

    False

    Skip checking if EMODules on the cluster are up-to-date, which can be slow.

    --scons

    False

    Indicate that this is a SCons build so custom DLLs are found in the build/64/Release directory.

    --local

    False

    Run all simulations locally.

  4. Review the output and examine any failures.

    EMOD will output the standard error and logging files, StdErr.txt and StdOut.txt, produced from any simulation (see Error and logging files). In addition, regression_test.py will output time.txt under the regression test working directory and report_xxxx.xml under Regression/reports. The time report contains the EMOD version and total run time of the simulation. The regression report is in JUnit standard form and captures run information, including pass/fail/complete and time to complete.

    If a simulation completes saying the run passed but the channel order was different than the reference output, this is considered a pass. However, if any output completes but does not match the reference output, this is considered a failure and a matplotlib chart of the output will appear in a pop-up window. The chart will appear immediately after the simulation, before the entire suite of regression tests completes. You can manipulate the output of the charts, such as adjusting the scale of the plots, zooming or panning, and so forth, through the icons at the bottom of the chart window.

    _images/matplotlibPopupChart.png

If any of the regression tests fail and you have not made any changes to the EMOD source code, email support@idmod.org.

Create a new regression test

You can create a new regression based off one of the ones included with the EMOD source code using the steps below.

  1. Under the Regression directory, create a new subdirectory.

  2. Copy the contents of the regression test that you want to base the new test on into the subdirectory.

  3. Modify the configuration, campaign, and demographic files as desired.

  4. Create the reference output by doing one of the following:

    • Modify the InsetChart.json file to match the output you expect.

    • Run simulations until you have an acceptable InsetChart.json that you wish to use as the reference.

Scientific feature testing

Scientific feature testing (SFT) verifies that EMOD features are functioning as expected. That is, they are requirements-based tests of model features that are quantitatively verified. They are implemented as a Python post-processing script named dtk_post_process.py.

All of the files can be found in the Regression directory of the EMOD GitHub repository.

Run locally

If you are running locally without access to an HPC, do one the following depending on your operating system:

Run on Windows using regression_test.py

  1. In the Regression directory, edit the regression_test.cfg file to list the directories where you want the simulations to be run and output saved.

  2. Make a file listing all of the SFTs you want to run, using on of the files in Regression<sim>_science.json as an example.

  3. From the Regression directory in a command prompt window, run the following, adding --scons if you built the Eradication.exe using SCONS:

    python regression_test.py my_sfts.json --local

    Note

    Enter python regression_test.py --help for a list of all arguments you can use with the testing script.

Run on Windows using run_test.cmd

  1. Generate a single config.json file from a base file and the parameter_overrides.json that contains (see Configuration overlay files for instructions).

  2. Navigate to the directory that contains your config.json file and other test files.

    Note

    Verify in the config.json file that the paths to the demographics and other additional input files are correct.

  3. In a command prompt window, enter the following, substituting your path to the testing script as necessary:

    ../../run_test.cmd
Run on CentOS using run_test.sh

  1. Generate a single config.json file from a base file and the parameter_overrides.json that contains (see Configuration overlay files for instructions).

  2. Navigate to the directory that contains your config.json file and other test files.

    Note

    Verify in the config.json file that the paths to the demographics and other additional input files are correct.

  3. In a command prompt window, enter the following, substituting your path to the testing script as necessary:

    ../../run_test.sh