Welcome to EMOD modeling for general disease¶
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 installation and usage instructions for the Generic model, which can be used to simulate a variety of different diseases.
What’s new¶
This topic describes new functionality and breaking changes for recently released versions of Epidemiological MODeling software (EMOD).
Contents
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:
|
{
"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:
|
{
"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:
|
{
"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:
|
{
"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.
|
{
"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.
|
{
"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,
"Event_Or_Config": "Event",
"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,
"Event_Or_Config": "Event",
"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,
"Event_Or_Config": "Event",
"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,
"Event_Or_Config": "Event",
"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"
],
"Event_Or_Config": "Event",
"Negative_Diagnosis_Event": "TestedNegative_IamSusceptible",
"Positive_Diagnosis_Event": "TestedPositive_IamImmune",
"Positive_Threshold_AcquisitionImmunity": 0.99,
"Treatment_Fraction": 1,
"class": "ImmunityBloodTest"
}
}
}
|
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. |
{
"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,
"Event_Or_Config": "Event",
"Negative_Diagnosis_Event": "TestedNegative_IamSusceptible",
"Positive_Diagnosis_Event": "TestedPositive_IamImmune",
"Positive_Threshold_AcquisitionImmunity": 0.99,
"Treatment_Fraction": 1,
"class": "ImmunityBloodTest"
}
}
}
|
Enable_IsSymptomatic |
boolean |
0 |
1 |
0 |
If true (1), requires an infection to be symptomatic to return a positive test. |
{
"Enable_IsSymptomatic": 1,
"Base_Specificity": 0.85,
"Base_Sensitivity": 0.92
}
|
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. |
{
"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": "ImmunityBloodTest"
}
}
}
|
Negative_Diagnosis_Event |
enum |
NA |
NA |
“” |
If an individual tests negative (does not have immunity), then an individual type event is broadcast. This may trigger another intervention when the event occurs. Only used when Event_Or_Config is set to Event. |
{
"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",
"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 distributed. 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_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": {
"Demographic_Coverage": 1,
"Intervention_Config": {
"Base_Sensitivity": 1,
"Base_Specificity": 1,
"Cost_To_Consumer": 0,
"Days_To_Diagnosis": 0,
"Event_Or_Config": "Event",
"Negative_Diagnosis_Event": "TestedNegative_IamSusceptible",
"Positive_Diagnosis_Event": "TestedPositive_IamImmune",
"Positive_Threshold_AcquisitionImmunity": 0.99,
"Treatment_Fraction": 1,
"class": "ImmunityBloodTest"
}
}
}
|
Positive_Diagnosis_Event |
enum |
NA |
NA |
“” |
If the test is positive (has immunity), then an individual type event is broadcast. This may trigger another intervention when the event occurs. Only used if Event_Or_Config is set to Event. |
{
"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",
"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,
"Event_Or_Config": "Event",
"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,
"Event_Or_Config": "Event",
"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_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:
|
{
"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:
|
{
"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_IsSymptomatic |
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_IsSymptomatic": 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_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 |
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:
|
{
"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 |
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:
|
{
"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 |
“” |
The name of the node event to broadcast. This event must be set in the Custom_Node_Events configuration parameter. |
{
"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"
}
}
}
|
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:
|
{
"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:
|
{
"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:
|
{
"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:
|
{
"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:
|
{
"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 |
3.40282e+38 |
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:
|
{
"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:
|
{
"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 |
3.40E+3 |
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:
|
{
"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:
|
{
"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"
}
|
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 |
1000 |
1 |
The number of times an intervention is given, used with Timesteps_Between_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 |
3.40E+38 |
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:
|
{
"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:
|
{
"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"
}
}
|
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 generic simulation type, the following new configuration parameters are available:
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Enable_Maternal_Antibodies_Transmission |
boolean |
0 |
1 |
0 |
Controls whether or not mothers pass antibodies to their infants. |
{
"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"
}
|
Enable_Natural_Mortality |
boolean |
0 |
1 |
0 |
Controls whether or not individuals are removed from the simulation due to natural (non-disease) deaths. |
{
"Enable_Natural_Mortality": 1,
"Enable_Vital_Dynamics": 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 probablistically exposing individuals rather than exposing every single person. Useful in low-prevalence, high-population scenarios. |
{
"Exposure_Skipping": 1
}
|
Enable_Susceptibility_Scaling |
boolean |
0 |
1 |
0 |
Controls whether or not susceptibility is scaled by time as defined by Susceptibility_Scaling_Type. |
{
"Enable_Susceptibility_Scaling": 1,
"Susceptibility_Scaling_Type": "LOG_LINEAR_FUNCTION_OF_TIME"
}
|
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": "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": "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": 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": "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": "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": "SIGMOID_BINARY",
"Maternal_Sigmoid_SteepFac": 30,
"Maternal_Sigmoid_HalfMaxAge": 365,
"Maternal_Sigmoid_SusInit": 0.0002
}
|
Node_Contagion_Decay_Rate |
float |
0 |
1 |
1 |
The fraction of contagion not carried over to the next time step. |
{
"Node_Contagion_Decay_Rate": 0.3
}
|
Susceptibility_Scaling_Rate |
float |
0 |
3.40E+38 |
0 |
The scaling rate for the variation in time of the log-linear susceptibility scaling. Susceptibility_Scaling_Type must be set to LOG_LINEAR_FUNCTION_OF_TIME. |
{
"Susceptibility_Scaling_Type": "LOG_LINEAR_FUNCTION_OF_TIME",
"Susceptibility_Scaling_Rate": 1.58
}
|
Susceptibility_Scaling_Type |
enum |
NA |
NA |
CONSTANT_SUSCEPTIBILITY |
The effect of time on susceptibility. |
{
"Susceptibility_Scaling_Type": "CONSTANT_SUSCEPTIBILITY"
}
|
Susceptibility_Type |
enum |
NA |
NA |
FRACTIONAL |
Controls implementation of an individual’s susceptibility. |
{
"Susceptibility_Type": "FRACTIONAL",
"Enable_Maternal_Protection": 1,
"Maternal_Protection_Type": "LINEAR_FRACTIONAL"
}
|
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:
|
{
"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 |
1000 |
1 |
The number of times an intervention is given, used with Timesteps_Between_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:
|
{
"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:
|
{
"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"
]
}
|
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 |
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": "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 |
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 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
}
|
Dosing_Type |
enum |
NA |
NA |
SingleDose |
The type of anti-malarial dosing to distribute in a drug intervention. Possible values are:
|
{
"Intervention_Config": {
"Cost_To_Consumer": 3.75,
"Dosing_Type": "FullTreatmentWhenSymptom",
"Drug_Type": "Chloroquine",
"class": "AntimalarialDrug"
}
}
|
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+03 |
3.40E+03 |
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 |
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 strings |
NA |
NA |
NEXT_UPDATE |
Defines the action the person takes if they do not take a particular dose, are not adherent. |
{
"AD_Non_Adherence_Options": [
"NEXT_DOSAGE_TIME",
"NEXT_UPDATE"
]
}
|
Took_Dose_Event |
string |
NA |
NA |
“” |
The event that triggers the drug intervention campaign. The events contained in the list can be built-in events (see Event list for possible events). |
{
"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"
}
|
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 |
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 |
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 |
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"
}
|
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:
|
{
"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"
}
]
}
|
Genome_Markers |
array of strings |
NA |
NA |
NA |
A list of the names of genome marker(s) that represent the genetic components in a strain of an infection. |
{
"Intervention_Config": {
"class": "OutbreakIndividualMalaria",
"Antigen": 0,
"Genome_Markers": [
"D6",
"W2"
]
}
}
|
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 |
2.15E+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:
|
{
"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 |
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 generic 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"
}
|
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
}
|
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"
}
|
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:
|
{
"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 |
2.15E+0 |
2.15E+09 |
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 |
3.40E+3 |
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 |
3.40E+3 |
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:
|
{
"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 |
2.15E+0 |
2.15E+09 |
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 |
2.15E+0 |
2.15E+09 |
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 |
3.40E+3 |
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:
|
{
"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:
|
{
"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 |
3.40E+3 |
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
}
|
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 |
3.40E+3 |
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 |
2.15E+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 |
2.15E+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 |
2.15E+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 |
10 |
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 |
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 |
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": "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 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_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 a node 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 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 |
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"
}
|
Maximum_Duration |
float |
-1 |
3.40E+38 |
3.40E+38 |
The maximum amount of time 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 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_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 |
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
}
|
Efficacy_Is_Multiplicative |
boolean |
0 |
1 |
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 |
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": "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:
|
{
"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.
Cost_To_Consumer
float
0
999999
3.75
The unit cost per bednet (unamortized)
Discard_Event
enum
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.
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.
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.
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
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.
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_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_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_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_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_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_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_Max
float
0
3.40282E+38
-1
The maximum expiration period, in days, when Expiration_Period_Distribution is set to UNIFORM_DISTRIBUTION.
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_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_Min
float
0
3.40282E+38
-1
The minimum expiration period, in days, when Expiration_Period_Distribution is set to UNIFORM_DISTRIBUTION.
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_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_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_Proportion_1
float
0
1
-1
The proportion of bednets in the first exponential distribution.
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.
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.
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.
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.
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.
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.
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¶
Install the Microsoft HPC Pack 2012 Client Utilities Redistributable Package (64-bit). See http://www.microsoft.com/en-us/download/details.aspx?id=36044 for instructions.
Install the Microsoft MPI v8. See https://www.microsoft.com/en-us/download/details.aspx?id=54607 for instructions, being sure to run the MSMpiSetup.exe file.
Install the Microsoft Visual C++ 2015 Redistributable (64-bit). See https://www.microsoft.com/en-us/download/details.aspx?id=53840 for instructions.
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.
In a web browser, go to https://www.python.org/downloads/release/python-366/ to install Python 3.6 64-bit.
Scroll down and download one of the x86-64 bit installers (you may use the executable installer or the web-based installer.)
Double-click the executable file and, in the installer window, select the Add Python 3.6 to PATH checkbox and click Customize installation.
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.
If you are running EMOD locally, IDM recommends that you select the Advanced Options window to customize the installation directory to “C:\Python36”.
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.
Click Install. When installation is complete, click Close.
To verify installation, open a Command Prompt window and type the following:
python --version
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+.
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
To activate the virtual environment, enter the following:
idm\Scripts\activate
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
To verify you are using Python 3.6 64-bit, enter the following:
python --version
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
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 “cp36-cp36m”, 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.
Go to http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy and select the WHL file for NumPy 1.16.2 (64-bit) that is compatible with Python 3.6 64-bit.
Save the file to your Python installation directory.
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-cp36-cp36m-win_amd64.whl
The Python packages dateutil, six, and pyparsing provide text parsing and datetime functionality.
Note
Be sure NumPy is installed before you install Matplotlib.
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.
Go to https://www.r-project.org/ and install R 3.2.0 (64-bit).
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.
Go to http://www.mathworks.com/products/matlab/ and install MATLAB R2015a.
If desired, go to https://www.mathworks.com/products/statistics.html and install the MATLAB Statistics and Machine Learning Toolbox™ R2015a.
(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.
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.
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
Navigate to the directory where you downloaded the metadata for the input files.
Download the actual data on your local machine:
git lfs fetch
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 1.61.0 and Microsoft MPI v8, 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
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.
Set the EMOD_ROOT environment variable to the path to the EMOD source path:
EMOD_ROOT=~/IDM/EMOD
Include Scripts and . in the path:
export PATH=$PATH:.:$EMOD_ROOT/Scripts
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.
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+.
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
To activate the virtual environment, enter the following:
source idm/bin/activate
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
To verify you are using Python 3.6 64-bit, enter the following:
python --version
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.
Go to https://www.r-project.org/ and install R 3.2.0 (64-bit).
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.
Go to http://www.mathworks.com/products/matlab/ and install MATLAB R2015a.
If desired, go to https://www.mathworks.com/products/statistics.html and install the MATLAB Statistics and Machine Learning Toolbox™ R2015a.
(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.
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.
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
Navigate to the directory where you downloaded the metadata for the input files.
Download the actual data on your local machine:
git lfs fetch
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.
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.
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 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.
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.
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:
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.
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.
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.
Place all demographics files in the directory where the other input files are stored.
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
}
]
}
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.
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.
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
}
}
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
}
}
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:
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 } }
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 } } }
In a Command Prompt window, navigate to the Regression folder.
Run the flatten_config.py script, providing the relative path to the overlay file:
python flatten_config.py experiment/parameter_overrides.json
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 } }
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 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)
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
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.
- event coordinator
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.
- 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 nodes 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.
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:
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" } } } ] }
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" } ] }
In a Command Prompt window, navigate to the Regression folder.
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
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 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.
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.
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:
|
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:
|
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. |
{
"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"
}
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.
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.
To use the same average migration rate for every individual in a node, create the migration files from a CSV input file.
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.
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.
To vary the average migration rate based on age and/or gender, create the migration files from a JSON input file.
Create a JSON file with the structure described in the sections below.
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.
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. |
{
"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]
}]
}]
}
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 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.
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:
|
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"
}
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:
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.
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"
}
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:
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.
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]
]
}
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:
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. 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.
See Simulation setup parameters for more information.
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.
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.
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.
Long form |
Short form |
Description |
|---|---|---|
|
|
Get version information. |
|
Path to write the schema (the complete list of all parameters and associated information). If not specified, the schema will be written to stdout. |
|
|
|
Path to the configuration file. If not specified, EMOD will look for a file named config.json in the current directory. |
|
|
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. |
|
|
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. |
|
|
Path to the EMODule root directory. For more information, see Custom reporters. |
|
|
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.
Long form |
Description |
|---|---|
|
The IP address of the commissioning/monitoring host. Set to “none” for no IP address. |
|
The port of the commissioning/monitoring host. Set to “0” for no port. |
|
The unique ID for this simulation. This ID is needed for self-identification to the UDP host. Set to “none” for no simulation ID. |
|
Send updates on the progress of the simulation to the HPC job scheduler. |
Open a Command Prompt window and navigate to the working directory, which contains the configuration and campaign files.
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
--helpcommand.EMOD will display logging information, including an errors that occur, while running the simulation. See Error and logging files for more information.
Output files will be placed in the directory specified. For more information on how to evaluate and analyze the output, see Output files.
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 2012 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.
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.
Open a Command Prompt window and navigate to the directory that contains the configuration and campaign files for the simulation.
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.
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.
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.
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.
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.
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.
- 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.
- 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.
Output files¶
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. You can enable these reports in the configuration file. 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.
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 files¶
At the end of the simulation, you will notice that a number of files have been written to the output directory. The rest are output reports that contain data from the simulation itself, usually in JSON or CSV format. This topic describes how to parse and use the 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. See Output files for additional information about the reports and how to enable them.
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.
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.
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()
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' );
The inset chart output report is output with every simulation. It is a JSON-formatted file with the channel output results of the simulation, consisting of simulation-wide averages by time step. The channels in an inset chart are fully specified by the simulation type and cannot be altered without making changes to the EMOD source code. The file name is InsetChart.json. You can use the information contained in the file to create a chart, but EMOD does not automatically output a chart.
The file contains a header and a channels section.
The header section contains 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 noted 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 this simulation. |
Channels |
integer |
The number of channels in the simulation. |
The following is a sample of an InsetChart.json file for a simulation with five time steps.
{
"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
]
}
}
}
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. |
When running generic simulations, the following channels are included in the InsetChart.json file.
Channel |
Description |
|---|---|
Births |
The live births cumulative up to that day. |
Campaign Cost |
The cost of campaigns cumulative up to that day (set by the Cost_To_Consumer parameter). |
Daily (Human) Infection Rate |
The per-person daily rate of human infection (Human Infectious Reservoir divided by Statistical Population). |
Disease Deaths |
The number of individuals whose death is attributed to disease, cumulative up to that day. |
Exposed Population |
The fraction of the population that is currently infected but not contagious. |
Human Infectious Reservoir |
The total node infectivity, after modifications by the infectious reservoir, seasonality, climate, etc. (if present). The expected number of infections distributed via the contact route, absent immunity or interventions. |
Infected |
The fraction of the population currently infected. |
Infectious Population |
The fraction of the population that is currently infected and contagious. |
New Infections |
The number of individuals that got infected on that day. |
Newly Symptomatic |
The number individuals that became symptomatic on that day. |
Recovered Population |
The fraction of the population that is currently not infected and has zero susceptibility. |
Statistical Population |
The total number of individuals in the simulation on that day. |
Susceptible Population |
The fraction of the population that is currently not infected and is maximally susceptible. |
Symptomatic Population |
The total number of individuals currently experiencing symptoms. |
Waning Population |
The fraction of the population that is currently not infected, has non-zero susceptibility, but is less than maximally susceptible. |
The binned output report is a JSON-formatted file where the channel data has been sorted into bins. It is very similar to an inset chart. However, with the binned report, channels are broken down into sub-channels (bins) based on various criteria. 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. The file name is BinnedReport.json.
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.
The header section contains 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. |
|||||||||||||||
Timesteps |
integer |
The number of time steps in this simulation. |
|||||||||||||||
Channels |
integer |
The number of channels in the simulation. |
|||||||||||||||
Subchannel_Metadata |
nested JSON object |
Metadata that describes the bins and axis information. The metadata includes the following parameters:
|
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. |
The following is a sample of an 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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]
},
"Infected": {
"Units": "",
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[567, 567, 565, 565, 568, 567, 567, 567, 567, 567, 566, 565, 565, 566, 565, 565, 565, 566, 566, 566, 566, 567, 568, 568, 569, 567, 566, 566, 567, 567, 568, 569, 569, 569, 569, 569, 570, 571, 571, 571, 572, 572, 572, 572, 572, 570, 570, 570, 571, 572, 572, 571, 571, 570, 570, 570, 570, 569, 568, 568, 569, 569, 569, 569, 569, 569, 569, 569, 568, 568, 568, 568, 568, 569, 569, 569, 568, 567, 568, 568, 567, 567, 567, 566, 566, 565, 565, 564, 564, 564, 564, 564, 565, 566, 566, 567, 567, 569, 570, 570, 569, 569, 568, 568, 567, 567, 566, 567, 567, 567, 567, 567, 567, 567, 567, 568, 570, 571, 571, 573, 573, 575, 575, 576, 576, 578, 578, 579, 577, 577, 577, 577, 577, 576, 574, 573, 573, 574, 573, 573, 573, 573, 573, 573, 574, 574, 573, 573, 574, 574],
[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 demographic summary output report 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. The file name is DemographicsSummary.json.
To generate the demographics summary report, set the Enable_Demographics_Reporting configuration parameter to 1. The binned output report will also be generated.
The file contains a header and a channels section.
The header section contains 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 noted 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 this simulation. |
Channels |
integer |
The number of channels in the simulation. |
The channels section contains the following parameters.
Parameter |
Data type |
Description |
|---|---|---|
<Channel_Title> |
string |
The title of the particular demographic channel. |
Units |
string |
The units used for this channel. |
Data |
array |
A list of the channel data at each time step. |
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
]
}
}
}
The node demographics report 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.
The report name is ReportNodeDemographics.csv, and is produced via the libReportNodeDemographics.dll file. See Custom reporters for information on using dynamic link library (DLL) files.
The following parameters need to be configured to generate the report:
Parameter name |
Data type |
Min |
Max |
Default |
Description |
|---|---|---|---|---|---|
IP_Key_To_Collect |
string |
NA |
NA |
(empty string) |
The name of the IndividualProperty key to stratify by; an empty string implies ‘do not stratify by IP.’ |
Age_Bins |
Array of integers |
0 |
MAX_HUMAN_AGE |
[40, 80, 125] |
The Age Bins (in years) to aggregate within and report; an empty array implies ‘do not stratify by age.’ |
Stratify_By_Gender |
boolean |
0 |
1 |
1 |
Set to true (1) to stratify by gender; a value of 0 will not stratify by gender. |
The report will contain the following output data, divided between stratification columns and data columns.
Parameter |
Data type |
Description |
|---|---|---|
Time |
integer |
The day of the simulation that the data was collected. |
NodeID |
string |
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. |
AgeYears |
integer |
The max age in years of the bin for the individuals in the row in the report. |
IndividualProp |
string |
The value of the IP for the individuals in the row in the report. |
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 and are infected. |
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 |
The event counter report 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).
The output file is named ReportEventCounter.json, and is produced via the libreporteventcounter.dll file. See Custom reporters for information on using using dynamic link library (DLL) files.
The following parameters need to be configured to generate the report:
Parameter name |
Data type |
Min |
Max |
Default |
Description |
|---|---|---|---|---|---|
Start_Day |
float |
0 |
3.40E+38 |
0 |
The day to start collecting data for the report. |
Duration_Days |
float |
0 |
3.40E+38 |
3.40E+38 |
The duration of simulation days over which to report events. |
Report_Description |
string |
NA |
NA |
NA |
Name of the report (it augments the filename of the report). If multiple CSV reports are being generated, this allows the user to distinguish one report from another. |
Nodeset_Config |
JSON object |
NA |
NA |
NA |
An object that specifies in which nodes the interventions will be distributed. |
Event_Trigger_List |
list of strings |
NA |
NA |
NA |
The list of event triggers for the events included in the report. |
The header section contains the following parameters:
Parameter |
Data type |
Description |
|---|---|---|
Channels |
integer |
The number of entries in the ‘Channels’ map (e.g. the number of events that the report is counting). |
DTK_Version |
string |
The version of EMOD that was used. |
DateTime |
string |
The date and time the report was created. |
Report_Type |
string |
The type of report created (it should always be InsetChart/Channel Report). |
Report_Version |
string |
The version of the report format. |
Simulation_Timestep |
integer |
The number of days in one time step of the simulation. |
Timesteps |
integer |
The number of time steps recorded in the file, Each channel should have this number of entries. |
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’. |
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": ""
}
}
}
The health events and interventions report is a CSV-formatted output file which provides information on each individual’s events and interventions, such as Birth, Pregnant, DiseaseDeath, HIVSymptomatic, etc. The filename is ReportEventRecorder.csv.
To generate the report, set the configuration parameter Report_Event_Recorder to 1. Note that this file is modifiable; to see the list of parameters that will add new data channels to the report, see Output settings.
With this report, it is possible to customize which events are reported. You may list the specific events to record, or you may record all events EXCEPT those listed. Use the configuration parameter Report_Event_Recorder_Events to list events. If the configuration parameter Report_Event_Recorder_Ignore_Events_In_List is set to 0, then only the events listed in Report_Event_Recorder_Events will be reported. If it is set to 1, then all events EXCEPT those listed will be reported.
The file contains the following data channels:
Data channel |
Data type |
Description |
|---|---|---|
Year |
float |
The time of the event in units of calendar year, including fractions of years up to two decimal places. |
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 |
boolean |
Identifies the individual’s gender; 0 is assigned to males, 1 is assigned to females. |
Infected |
boolean |
Describes whether the individual is infected or not; 0 when not infected, 1 for infected. |
Infectiousness |
float |
The per-act probability of transmission, including intrahost factors like disease stage and ART, but excluding condoms. |
<custom channels> |
string |
If any IndividualProperties are listed under the config.json array Report_Event_Recorder_Individual_Properties, they will appear as additional columns between the Infectiousness and HasHIV columns of the CSV file. |
HasHIV |
string |
This column is included for HIV simulations only. N if the individual is not infected, Y if the individual is infected with HIV. |
OnART |
string |
This column is included for HIV simulations only. N if the individual is not on ART, Y if the individual is on ART. |
CD4 |
float |
The current CD4 count, regardless of when CD4 testing was performed. |
WHO_Stage |
float |
The individual’s WHO stage, linearly interpolated between integer values. Round down to obtain the integer value for the WHO clinical stage. Uninfected individuals will be assigned a value of -1. |
Intervention_Status |
string |
The individual’s intervention status. |
In addition to the default channels listed above, it is possible to list whether an individual has been assigned IndividualProperties. To do so, ad the name of the property to the array for the configuration parameter Report_Event_Recorder_Individual_Properties.
The following is an example of a ReportEventRecorder.csv report:
Year |
Node_ID |
Event_Name |
Individual_ID |
Age |
Gender |
Infected |
Infectiousness |
HasHIV |
OnART |
CD4 |
WHO_Stage |
InterventionStatus |
1960 |
1 |
Births |
8699 |
0 |
F |
0 |
0 |
N |
N |
1.00E+06 |
-1 |
None |
1960 |
1 |
Births |
8700 |
0 |
M |
0 |
0 |
N |
N |
1.00E+06 |
-1 |
None |
1960 |
1 |
Births |
8701 |
0 |
F |
0 |
0 |
N |
N |
1.00E+06 |
-1 |
None |
1960 |
1 |
Births |
8702 |
0 |
F |
0 |
0 |
N |
N |
1.00E+06 |
-1 |
None |
1960 |
1 |
Births |
8703 |
0 |
M |
0 |
0 |
N |
N |
1.00E+06 |
-1 |
None |
1960 |
1 |
Births |
8704 |
0 |
F |
0 |
0 |
N |
N |
1.00E+06 |
-1 |
None |
1960 |
1 |
Births |
8705 |
0 |
M |
0 |
0 |
N |
N |
1.00E+06 |
-1 |
None |
1960 |
1 |
Births |
8706 |
0 |
M |
0 |
0 |
N |
N |
1.00E+06 |
-1 |
None |
1960 |
1 |
Births |
8707 |
0 |
F |
0 |
0 |
N |
N |
1.00E+06 |
-1 |
None |
1960 |
1 |
Births |
8708 |
0 |
M |
0 |
0 |
N |
N |
1.00E+06 |
-1 |
None |
1960 |
1 |
Births |
8709 |
0 |
M |
0 |
0 |
N |
N |
1.00E+06 |
-1 |
None |
1960 |
1 |
Births |
8710 |
0 |
M |
0 |
0 |
N |
N |
1.00E+06 |
-1 |
None |
1960 |
1 |
Births |
8712 |
0 |
F |
0 |
0 |
N |
N |
1.00E+06 |
-1 |
None |
1960 |
1 |
Births |
8732 |
0 |
M |
0 |
0 |
N |
N |
1.00E+06 |
-1 |
None |
1960 |
1 |
Births |
8733 |
0 |
M |
0 |
0 |
N |
N |
1.00E+06 |
-1 |
None |
1960 |
1 |
Births |
8734 |
0 |
M |
0 |
0 |
N |
N |
1.00E+06 |
-1 |
None |
1960 |
1 |
Births |
8735 |
0 |
F |
0 |
0 |
N |
N |
1.00E+06 |
-1 |
None |
1960 |
1 |
Births |
8736 |
0 |
F |
0 |
0 |
N |
N |
1.00E+06 |
-1 |
None |
1960.08 |
1 |
Loop_Entry_InitialPopulation |
1 |
17179.7 |
F |
0 |
0 |
N |
N |
1.00E+06 |
-1 |
None |
1960.08 |
1 |
Loop_Entry_InitialPopulation |
2 |
1509.3 |
F |
0 |
0 |
N |
N |
1.00E+06 |
-1 |
None |
1960.08 |
1 |
Loop_Entry_InitialPopulation |
3 |
550.404 |
M |
0 |
0 |
N |
N |
1.00E+06 |
-1 |
None |
1960.08 |
1 |
Loop_Entry_InitialPopulation |
4 |
13136.9 |
F |
0 |
0 |
N |
N |
1.00E+06 |
-1 |
None |
1960.08 |
1 |
Loop_Entry_InitialPopulation |
5 |
2716.6 |
F |
0 |
0 |
N |
N |
1.00E+06 |
-1 |
None |
1960.08 |
1 |
Loop_Entry_InitialPopulation |
6 |
14578.1 |
M |
0 |
0 |
N |
N |
1.00E+06 |
-1 |
None |
1960.08 |
1 |
Loop_Entry_InitialPopulation |
7 |
24638.7 |
M |
0 |
0 |
N |
N |
1.00E+06 |
-1 |
None |
1960.08 |
1 |
Loop_Entry_InitialPopulation |
8 |
4697.56 |
M |
0 |
0 |
N |
N |
1.00E+06 |
-1 |
None |
1960.08 |
1 |
Loop_Entry_InitialPopulation |
9 |
2899.98 |
M |
0 |
0 |
N |
N |
1.00E+06 |
-1 |
None |
1960.08 |
1 |
Loop_Entry_InitialPopulation |
10 |
17541.3 |
F |
0 |
0 |
N |
N |
1.00E+06 |
-1 |
None |
1960.08 |
1 |
Loop_Entry_InitialPopulation |
11 |
3843.27 |
F |
0 |
0 |
N |
N |
1.00E+06 |
-1 |
None |
1960.08 |
1 |
Loop_Entry_InitialPopulation |
12 |
29140.1 |
F |
0 |
0 |
N |
N |
1.00E+06 |
-1 |
None |
The human migration tracking report 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.
The report name is ReportHumanMigrationTracking.csv, and is produced via the libhumanmigrationtracking.dll file. See Custom reporters for information on using dynamic link library (DLL) files.
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).
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 migrating individual (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 |
Possible values are air, local, sea, regional, local, family, intervention; the type of migration that is occurring. |
Event |
enum |
Possible values are Emigrating or SimulationEnd. Emigrating means the individual is moving, SimulationEnd means the simulation has finished. |
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 |
The property output report 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. For example, it allows you to compare disease deaths for people in the high risk group versus the low risk group. If you want to aggregate the data, you must create a script for aggregating information. The file name is PropertyReport.json.
To generate the property report, set the Enable_Property_Output configuration parameter to 1.
The file contains a header and a channels section.
The header section contains 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 noted 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 this simulation. |
Channels |
integer |
The number of channels in the simulation. |
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. |
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
]
}
}
}
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:
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 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.
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:
Define the location of the dynamic link library (DLL) in a JSON-formatted file (emodules_map.json).
Point to the location using the
--dll-pathcommand-line option when invoking Eradication.exe.Place the DLL in the working directory.
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"
]
}
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.
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.
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.
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.
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Done - 0:00:02
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
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00:00:02 [0] [I] [Simulation] Update(): Time: 76.0 Rank: 0 StatPop: 10000 Infected: 0
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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.
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.
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.
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
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.
- event coordinator
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.
- 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 nodes 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.
Contents
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
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.
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.
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.
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.
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.
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.
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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 2015 (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.
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.
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. 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.
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.
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):
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:
If the number of infections at the initial stage is small then S is close to N and the condition becomes:
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.
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:
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:
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, \(\lambda = \frac{\beta I}{N}\).
Expose: Each susceptible individual becomes infected with probability \(p_i = 1-\mathrm{e}^{\lambda\delta}\), where \(\delta\) is the time step.
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.
Recovery: Each infected individual recovers in one time step with probability, \(p_r = 1 - \text{exp}(-\gamma\delta)\).
Birth: For birthrate \(\mu\), the number of new susceptible individuals will be Poisson distributed with rate \(p_b = \mu N\delta\).
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:
With these numbers in mind and assuming that only one state transition event happens to each individual in a time step:
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.
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.
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.
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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.
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):
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.
Figure 1: Growth of infection and depletion of the susceptible population in an SIR outbreak¶
Figure 2: All output channels for SIR without 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:
where \(N = S + I + R\) is the total population.
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.
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:
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.
Figure 3: Damped oscillation in SIRS outbreak¶
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.
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:
where \(N = S + I + R\) is the total population.
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.
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.
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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.
In a closed population with no births or deaths, the SEIR model becomes:
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.
Figure 1: SEIR epidemic course for 8-day incubation period¶
Figure 2: All output channels for SEIR 8-day incubation period¶
Figure 3: SEIR epidemic course for 2-day incubation period¶
Figure 4: All output channels for SEIR 2-day incubation period¶
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:
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.
Figure 5: SEIR periodic outbreaks on reintroduction in a population with vital dynamics¶
Figure 6: All output channels for SEIR outbreaks¶
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:
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.
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:
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.
Figure 7: Trajectory of the SEIRS outbreak¶
Figure 8: All output channels for SEIRS outbreak¶
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.
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.
Contents
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.
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):
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.
Figure 1: SI outbreak showing logistic grown¶
Figure 2: All output channels for an SI outbreak¶
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:
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:
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.
Figure 3: SI outbreak approaching 85% infected population at steady state¶
Figure 4: All output channels for an SI outbreak with vital dynamics¶
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.
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:
At equilibrium, solving:
There are two equilibrium states for the SIS model, the first is \(I = 0\) (disease free state), and the second is:
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.
Figure 5: SIS outbreak approaching 17% infected population at steady state¶
Figure 6: All output channels for an SIS outbreak without 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:
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.
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.
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.
Figure 1: Zoonotic disease outbreak and animal reservoir¶
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, and typhoid 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.
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.
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.
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,
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, \(N(t)\).
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,
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.
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:
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,
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.
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.
Figure 2: Node_Grid_Size = 0.1, population density 80/km2and R0 > 1¶
Figure 3: Node_Grid_Size = 0.15, population density 36/km2and R0 ~ 1¶
Figure 4: Node_Grid_Size = 0.3, population density 9/km2and R0 < 1¶
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 the generic simulation type, you can also configure heterogeneous transmission between individuals with different property values. For more information, see Property-based heterogeneous disease transmission. 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.
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.
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.
In the array, add an empty JSON object. Within it, do the following:
Add the Property parameter and set it to one of the supported values.
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.
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.
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.
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.
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.
In the array, add an empty JSON object. Within it, do the following:
Add the Property parameter and set it to “Age_Bin”.
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.
By default, Epidemiological MODeling software (EMOD) assumes disease transmission is homogeneous across a population within each node when using the generic simulation type. That is, the population is sufficiently “well- mixed” to make each individual equally likely to next encounter each other individual. Disease transmission is configured by the parameter Base_Infectivity, which determines a single transmission constant, \(\beta_0\), to calculate the transmission rate for each individual within a node. These assumptions simplify the mathematical calculations that are performed when running a simulation.
However, assumptions of homogeneity quickly become invalid when considering the complexities of disease transmission. Spatial effects, even at relatively small scales, can invalidate the well- mixed assumption, particularly if the disease is highly infectious or the population density is high. Non-uniform patterns of mixing based on age are well-documented in school-based disease transmission 1. Susceptibility and infectivity are inherently heterogeneous, as governed by both behavioral and biological factors including hand washing, viral load, and immunity. Many of these factors can be targeted by interventions that potentially have non-uniform uptake, for example due to differences in accessibility.
The Heterogeneous Intra-Node Transmission (HINT) feature enables you to use individual properties (described more in Individual and node properties) to add heterogeneous transmission within nodes due to differences in risk, accessibility, and more. Examples of individual properties, with typical values given parenthetically, include risk group (low, medium, high), age group (under 5, 5 to 15, over 15), vaccine accessibility class (easy, hard), intra-node locale (census tracts), and place (home, school, work, community, non-school). All nodes in a simulation must have the same individual properties and values, but the characteristics of the heterogeneity can vary from node to node.
Additionally, you can use HINT to approximate “contact scaling”, a particular form of heterogeneous mixing in which shedding and acquisition are symmetric. The best example of contact scaling is location-based mixing, which can be used in place of intra-day migration.
This topic describes how to use the HINT feature to add heterogeneous transmission to your generic model by defining population groups and the transmission rate between each of these groups. It also describes the mathematics underpinning HINT.
In the configuration file, set the following:
Enable_Heterogeneous_Intranode_Transmission to 1.
Base_Infectivity to your desired value.
If you have not already added property values in the demographics file, add them using the steps in Individual and node properties.
In the demographics file, within each object in the IndividualProperties array, add the TransmissionMatrix parameter and assign it an empty JSON object.
In the object, set the following:
Route to the the transmission route (currently “Contact” is the only supported value).
Matrix to an array that contains a transmission matrix that scales the base infectivity set in the configuration file. This matrix is described in more detail below.
Associated with each individual property is a values-by-values-sized matrix of multipliers, denoted \(\beta\), that scale base infectivity, \(\beta_0\). Specifically, the entry of the multiplier matrix at row \(i\) and column \(j\) determines the transmission constant from infected individuals having the \(i^{th}\) value to susceptible individuals having the \(j^{th}\) value.
For example, the following demographics file configures HINT for transmission between individuals in high-risk and low-risk groups.
{
"IndividualProperties": [{
"Property": "Risk",
"Values": ["High", "Low"],
"TransmissionMatrix": {
"Route": "Contact",
"Matrix": [
[10, 0.1],
[0.1, 1]
]
}
}]
}
Based on the order the property values are listed, the high-risk group is represented in the first column and first row; the low-risk group is represented in the second column and second row. The following matrix represents the direction of the disease transmission between the groups:
Two individual properties can be combined to achieve a higher level of transmission heterogeneity. When multiple individual properties are configured, their effects are combined independently via multiplication of the appropriate multipliers. In the following example, an individual who is high risk and suburban will have the following transmission multiplier when interacting with an individual who is a low risk and suburban: \(0.1 \times 1.4 = 0.14\). Similarly, the transmission multiplier for a low risk urban individual to a high risk rural individual will be \(0.1 \times 0.2 = 0.02\).
{
"IndividualProperties": [{
"Property": "Risk",
"Values": ["High", "Low"],
"Initial_Distribution": [0.2, 0.8],
"TransmissionMatrix": {
"Route": "Contact",
"Matrix": [
[10, 0.1],
[0.1, 1]
]
}
}, {
"Property": "Place",
"Values": ["Urban", "Suburban", "Rural"],
"Initial_Distribution": [0.55, 0.35, 0.1],
"TransmissionMatrix": {
"Route": "Contact",
"Matrix": [
[2.5, 1.0, 0.1],
[1.0, 1.4, 0.2],
[0.2, 0.4, 0.8]
]
}
}]
}
While most multiplier matrices are symmetric, asymmetries as in the above example can arise for several reasons including heterogeneity in susceptibility.
The product of the base infectivity, \(\beta_0\), and the multiplier matrix, \(\beta\), governs “who acquires infection from whom,” and thus is sometimes called the WAIFW matrix. The values in this matrix can be thought of as the “effective contact rate,” where an effective contact is defined as one that will result in disease transmission were it between a susceptible and infected individual 2.
The following examples illustrate the mathematics behind the HINT feature in an EMOD simulation. 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 examples below show the ODE form of the model with HINT enabled.
To review the mathematics underlying EMOD in a homogeneous node without HINT enabled, see Compartmental models and EMOD. That topic includes a detailed comparison of compartmental models and EMOD, including the equations used for both ODE models and EMOD.
To begin, consider a single node with one individual property having \(V\) values. The \(X\), \(Y\), and \(Z\) disease states will gain an \(v\) subscript to index into the individual property values. Using the user-provided multiplier matrix of size \(V \times V\), the ODE form of the disease dynamics are:
For \(v \in [1...V]\).
At simulation initialization, individuals are assigned stochastically to values of the individual property according to the distribution specified in the demographics file.
When configured for multiple individual properties, EMOD assigns individual property values to individuals independently based on the separate probability distributions provided by the user in the demographics file. As an example, consider two individual properties, \(p_1\) and \(p_2\), having \(V_1\) and \(V_2\) values, respectively. The multiplier matrices will be denoted \(\beta^{p_1}\) and \(\beta^{p_2}\), and have sizes \(V_1 \times V_1\) and \(V_2 \times V_2\), respectively. The resulting disease dynamics in ODE form are:
For indices \((V_1,V_2) \in [1...V_1]\times[V_1...V_2]\).
The following graph show the property report for a HINT simulation with both age and accessibility properties in which transmission is lower for hard to access individuals. There are an equal number of “easy” and “hard” to access individuals, but notice how peak prevalence is higher for individuals who are easy to access. To run this example simulation, see the Generic/HINT_AgeAndAccess scenario in the downloadable EMOD scenarios zip file. This also includes examples that show the effect of targeting vaccination to either a particular age group or accessibility group. Review the README files there for more information.
Figure 1: Baseline outbreak¶
One potential application of HINT is in configuring “contact scaling,” a particular form of heterogeneous mixing in which shedding and acquisition are symmetric. The best example of contact scaling is location-based mixing, which can be used in place of intra-day migration. Typical places for location-based mixing include home, school, and work. Contact scaling could also be used for mixing between age groups. The population at each of these different locations affect disease transmission.
Contact scaling is typically specified by a matrix, \(C=[c_{ij}]\). The entry at \(c_{ij}\) indicates that an individual with value \(i\) sheds and acquires from mixing pool \(j\) with weight \(c_{ij}\). Each row of the contact scaling matrix typically sums to one, especially for location-based mixing wherein \(c_{ij}\) can be used to represent the fraction of the day an individual with value \(i\) spends at location \(j\).
In terms of implementation, there is one main difference between contact scaling and transmission scaling. In contact scaling, individuals both shed into and acquire from pools of contagion according to the corresponding row of the \(C\) matrix. Normalization is done not by \(N\), as in frequency scaling, but instead by the “effective population” of each mixing pool, which is computed as the \(C\)-weighted sum of the population-by-value. Transmission scaling via the \(\beta\) matrix in HINT directly specifies the transmission rate per susceptible between each group of individuals, so there is no concept of place but the overall approach is more flexible, e.g. by allowing asymmetries.
Interestingly, contact scaling exhibits neither frequency-dependent nor density-dependent scaling, although it more closely resembles frequency-dependent transmission because the denominator is a function of the current population. Currently, contact scaling is not supported as an independent feature in the EMOD software, however one can simulate contact scaling to good approximation if the fraction of the population in each value \(n_i = \frac{N_i(t)}{N(t)}\) is constant or stabilizes quickly. Then the transmission matrix can be configured using,
Here, \(n\) is a \(V\)-length column vector of the equilibrium fractions of the population by value. Absent mortality factors or interventions moving individuals from one value to the next, \(n\) should be close to the probability distribution used to assign values to individuals.
The following graph show the property report for a HINT simulation that uses different property values for each census tract in Seattle to simulate daily commutes as an example of contact scaling. An influenza outbreak begins in census tract 7 and the spatial coupling of the intra-day migration allows the disease to spread across the rest of the city. To run this example simulation, see the Generic/HINT_SeattleCommuting scenario in the EMOD scenarios zip file.
Figure 2: Effect of daily commutes on disease spread¶
- 1
Mossong, N. Hens, M. Jit, P. Beutels, K. Auranen, R. Mikolajczyk, M. Massari, S. Salmaso, G. S. Tomba, J. Wallinga, et al. Social contacts and mixing patterns relevant to the spread of infectious diseases. PLos medicine, 5(3):e74, 2008.
- 2
M.J. Keeling and P. Rohani. Modeling infectious diseases in humans and animals. Princeton University Press, 2008.
Only simulations using the environmental model (or inherited from environmental, such as typhoid) can use Multi-route HINT to configure heterogeneous disease transmission within each node and across multiple routes of exposure. Generic simulations can use Heterogeneous Intra-Node Transmission (HINT), 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.
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.
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.
To simulate intra-day migration, such as daily commutes, you can instead use the “contact scaling” method described in Property-based heterogeneous disease transmission.
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 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. 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.
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.
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.
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.
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.
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.
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" }
]
}
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" }
]
}
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" }
]
}
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" }
]
}
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" }
]
}
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
}
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.
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.
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.
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:
For an outbreak to start, the following condition needs to be satisfied:
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:
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:
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:
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.
Figure 1: Baseline SIR outbreak¶
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.
Figure 3: Vaccination campaign that misses 30% of the population¶
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
}
}
}
}
]
}
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.
This reference section contains only the parameters supported for use with the generic 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 generic 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.
Contents
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.
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:
|
{
"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
]
}
]
}
}
|
TransmissionMatrix |
JSON object |
NA |
NA |
NA |
An object that contains Route and Matrix parameters that define how to scale the base infectivity from individuals with one property value to individuals with another. Route can be set to “Contact” or “Environmental” and Matrix contains a WAIFW matrix of the disease transmission multipliers. The rows and columns are in the same order that the property values were defined in Value. The rows represent the infectious individuals (the “whom”); the columns represent the susceptible individuals (the “who”). This implements the HINT feature, which is available only in the generic, environmental, and typhoid simulation types. For more information, see Property-based heterogeneous disease transmission. Enable_Heterogeneous_Intranode_Transmission in the configuration file must be set to 1 (see Infectivity and transmission parameters). Cannot be used with NodeProperties. |
{
"Defaults": {
"IndividualProperties": [
{
"TransmissionMatrix": {
"Route": "Contact",
"Matrix": [
[
10,
0.1
],
[
0.1,
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 |
2.15E+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
}
}
]
}
|
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
}
}
]
}
|
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 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.
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.
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:
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.
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.
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:
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.
|
{
"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.
|
{
"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:
|
{
"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.
|
{
"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.
|
{
"IndividualAttributes": {
"RiskDistributionFlag": 0,
"RiskDistribution1": 1,
"RiskDistribution2": 0
}
}
|
||||||||||||||||||
RiskDistributionFlag |
integer |
0 |
7 |
0 |
The type of distribution to use for risk. Possible values are:
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.
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.
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:
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 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:
|
{
"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
]
]
}
}
}
|
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
]
]
}
}
}
|
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 generic 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.
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_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_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_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_Immune_Decay |
boolean |
0 |
1 |
1 |
Controls whether or not immunity decays after an infection clears. Set to true (1) if immunity decays; set to false (0) if recovery from the disease confers complete immunity for life. Enable_Immunity must be set to true (1). |
{
"Enable_Immunity": 1,
"Enable_Immune_Decay": 0
}
|
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_Infection_Transmission |
boolean |
0 |
1 |
0 |
Controls whether or not infectious mothers infect infants at birth. Enable_Birth must be set to 1 (true). Warning Do not set both Enable_Maternal_Infection_Transmission and Enable_Strain_Tracking to true (1) - as this combination will cause an exception error. |
{
"Enable_Birth": 1,
"Enable_Maternal_Infection_Transmission": 1
}
|
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_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_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
}
|
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 |
|---|---|---|---|---|---|---|
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
}
|
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
}
|
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
}
|
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
}
|
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
}
|
Enable_Immune_Decay |
boolean |
0 |
1 |
1 |
Controls whether or not immunity decays after an infection clears. Set to true (1) if immunity decays; set to false (0) if recovery from the disease confers complete immunity for life. Enable_Immunity must be set to true (1). |
{
"Enable_Immunity": 1,
"Enable_Immune_Decay": 0
}
|
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_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"
}
|
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"
}
|
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:
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
}
|
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
}
|
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
}
|
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:
|
{
"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:
|
{
"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:
|
{
"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.
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_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 generic simulations, this is the average number of individuals per time step who will be exposed to infection by one infectious individual. In the case of super-infection, the infectiousness is summed over all infections. |
{
"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_Maternal_Infection_Transmission |
boolean |
0 |
1 |
0 |
Controls whether or not infectious mothers infect infants at birth. Enable_Birth must be set to 1 (true). Warning Do not set both Enable_Maternal_Infection_Transmission and Enable_Strain_Tracking to true (1) - as this combination will cause an exception error. |
{
"Enable_Birth": 1,
"Enable_Maternal_Infection_Transmission": 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
}
|
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_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:
|
{
"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"
}
|
Infectivity_Exponential_Delay |
float |
0 |
3.40E+38 |
0 |
The number of days before infectivity begins to ramp up exponentially. When Enable_Infectivity_Scaling_Exponential configuration parameter is enabled, Infectivity_Exponential_Delay is an expected parameter. |
{
"INFECTIOUSNESS": {
"Base_Infectious_Period": 7,
"Base_Infectivity": 0.3,
"Enable_Infectivity_Scaling": 0,
"Infectious_Period_Distribution": "EXPONENTIAL_DISTRIBUTION",
"Infectious_Period_Exponential": 7,
"Infectivity_Exponential_Baseline": 0.5,
"Infectivity_Exponential_Delay": 60,
"Infectivity_Exponential_Rate": 60
}
}
|
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. When Enable_Infectivity_Scaling_Exponential configuration parameter is enabled, Infectivity_Exponential_Rate is an expected parameter. |
{
"INFECTIOUSNESS": {
"Base_Infectious_Period": 7,
"Base_Infectivity": 0.3,
"Enable_Infectivity_Scaling": 0,
"Infectious_Period_Distribution": "EXPONENTIAL_DISTRIBUTION",
"Infectious_Period_Exponential": 7,
"Infectivity_Exponential_Baseline": 0.5,
"Infectivity_Exponential_Delay": 60,
"Infectivity_Exponential_Rate": 60
}
}
|
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
}
|
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:
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"
}
|
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:
|
{
"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
}
|
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"
}
|
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"
}
|
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"
}
|
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"
}
|
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"
}
|
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"
}
|
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:
|
{
"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:
|
{
"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
}
|
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
}
|
Mortality and survival¶
The following parameter determine mortality and survival characteristics of the disease being modeled and the population in general (non-disease 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 |
|---|---|---|---|---|---|---|
Base_Mortality |
float |
0 |
1000 |
0.001 |
The base mortality of the infection before accounting for individual immune modification factors. Depending on the setting of Mortality_Time_Course, this is either the daily probability of the disease being fatal (DAILY_MORTALITY) or the probability of death at the end of the infection duration (MORTALITY_AFTER_INFECTIOUS). Enable_Disease_Mortality must be set to 1 (true). |
{
"Enable_Vital_Dynamics": 1,
"Mortality_Time_Course": "DAILY_MORTALITY",
"Base_Mortality": 0.01
}
|
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:
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
}
|
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:
|
{
"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 Inset chart output report 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"
}
|
|||||||||||||||
Duration_Days |
float |
0 |
3.40282e+38 |
3.40282e+38 |
The duration of simulation days over which to report events. The report will stop collecting data when the simulation day is greater than Start_Day + Duration_Days. Used in all reports that are subclasses of BaseEventReport, such as: ReportEventCounter, MalariaTransmissionReport, and BaseEventReportIntervalOutput. |
{
"Duration_Days": 353,
"Nodeset_Config": {
"class": "NodeSetAll"
},
"ReportEventCounter": {
"Enabled": 1
},
"Start_Day": 12
}
|
|||||||||||||||
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"
]
}
|
|||||||||||||||
Event_Trigger_List |
array of strings |
NA |
NA |
NA |
The list of event triggers for the events included in the report. |
{
"Start_Day": 5,
"Nodeset_Config": {
"class": "NodeSetNodeList",
"Node_List": [
2
]
},
"Pretty_Format": 1,
"Reporting_Interval": 40,
"Age_Bins": [
10,
100
],
"Event_Trigger_List": [
"EveryUpdate"
]
}
|
|||||||||||||||
Max_Number_Reports |
float |
1 |
1000000 |
1 |
Used by reports and custom reports. The maximum number of report output files that will be produced for a given simulation. |
{
"Max_Number_Reports": 15,
"Report_Description": "Day0",
"Reporting_Interval": 73
}
|
|||||||||||||||
Pretty_Format |
boolean |
0 |
1 |
0 |
True (1) sets pretty JSON formatting, which includes carriage returns, line feeds, and spaces for easier readability. The default, false (0), saves space where everything is on one line. |
{
"Pretty_Format": 1
}
|
|||||||||||||||
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_Description |
string |
NA |
NA |
NA |
Used by reports and custom reports. Augments the filename of the report. If multiple CSV reports are being generated, this allows you to distinguish among the multiple reports. |
{
"Report_Description": "Annual Epidemiological Report"
}
|
|||||||||||||||
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.
|
{
"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"
]
}
|
|||||||||||||||
Reporting_Interval |
float |
1 |
1000000 |
1000000 |
Used by reports and custom reports. Defines the cadence of the report by specifying how many time steps to collect data before writing to the file. |
{
"Max_Number_Reports": 15,
"Report_Description": "Day0",
"Reporting_Interval": 73
}
|
|||||||||||||||
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.
|
{
"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.
|
{
"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:
|
{
"Spatial_Output_Channels": [
"Prevalence",
"New_Infections"
]
}
|
|||||||||||||||
Start_Day |
float |
0 |
3.40282e+38 |
0 |
The day to start collecting data for the report. Used in all reports that are subclasses of BaseEventReport, such as: ReportEventCounter, MalariaTransmissionReport, and BaseEventReportIntervalOutput. |
{
"ReportEventCounter": {
"Enabled": 1
},
"Start_Day": 12,
"Duration_Days": 353,
"Nodeset_Config": {
"class": "NodeSetAll"
}
}
|
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:
|
{
"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:
|
{
"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:
|
{
"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:
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_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:
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:
|
{
"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.
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:
|
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
}
|
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
}
|
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 |
|---|---|---|---|---|---|---|
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:
|
{
"Enable_Vital_Dynamics": 1,
"Enable_Birth": 1,
"Birth_Rate_Time_Dependence": "ANNUAL_BOXCAR_FUNCTION"
}
|
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. When Enable_Infectivity_Scaling_Exponential configuration parameter is enabled, Infectivity_Exponential_Delay is an expected parameter. |
{
"INFECTIOUSNESS": {
"Base_Infectious_Period": 7,
"Base_Infectivity": 0.3,
"Enable_Infectivity_Scaling": 0,
"Infectious_Period_Distribution": "EXPONENTIAL_DISTRIBUTION",
"Infectious_Period_Exponential": 7,
"Infectivity_Exponential_Baseline": 0.5,
"Infectivity_Exponential_Delay": 60,
"Infectivity_Exponential_Rate": 60
}
}
|
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. When Enable_Infectivity_Scaling_Exponential configuration parameter is enabled, Infectivity_Exponential_Rate is an expected parameter. |
{
"INFECTIOUSNESS": {
"Base_Infectious_Period": 7,
"Base_Infectivity": 0.3,
"Enable_Infectivity_Scaling": 0,
"Infectious_Period_Distribution": "EXPONENTIAL_DISTRIBUTION",
"Infectious_Period_Exponential": 7,
"Infectivity_Exponential_Baseline": 0.5,
"Infectivity_Exponential_Delay": 60,
"Infectivity_Exponential_Rate": 60
}
}
|
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:
|
{
"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
}
|
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_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
}
|
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"
]
}
|
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:
|
{
"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:
|
{
"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:
|
{
"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:
|
{
"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
}
|
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 generic simulation type.
Events and event coordinators¶
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.
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.
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
}
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 |
2.15E+0 |
0 |
A vector of integers for simulation times at which distribution of events occurs in a CalendarEventCoordinator. |
{
"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 |
3.40E+3 |
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:
|
{
"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:
|
{
"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
}
|
{
"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"
}
}
}
}]
}
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 |
3.40E+3 |
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"
}
}
}]
}
The following classes determine in which nodes the event will occur.
The event will occur in all nodes in the simulation. This class has no associated parameters. For example,
{
"Nodeset_Config": {
"class": "NodeSetAll"
}
}
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
]
}
}
|
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"
}
}
|
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 |
2.15E+0 |
2.15E+09 |
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 |
3.40E+3 |
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 |
3.40E+3 |
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:
|
{
"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 |
2.15E+0 |
2.15E+09 |
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 |
2.15E+0 |
2.15E+09 |
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 |
3.40E+3 |
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:
|
{
"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:
|
{
"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 |
3.40E+3 |
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
}
|
{
"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
}
}
}
}
]
}
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 |
1000 |
1 |
The number of times an intervention is given, used with Timesteps_Between_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 |
3.40282e+38 |
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:
|
{
"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:
|
{
"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
}
|
{
"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"
}
}
}]
}
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:
|
{
"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
}
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:
|
{
"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 |
1000 |
1 |
The number of times an intervention is given, used with Timesteps_Between_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:
|
{
"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:
|
{
"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"
]
}
|
{
"class": "IncidenceEventCoordinator",
"Number_Repetitions" : 3,
"Timesteps_Between_Repetitions" : 6
}
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 |
3.40E+3 |
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 |
2.15E+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 |
2.15E+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 |
2.15E+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 |
3.40E+3 |
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
}
}
}]
}
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
}
|
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 |
1000 |
1 |
The number of times an intervention is given, used with Timesteps_Between_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 |
3.40E+3 |
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:
|
{
"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:
|
{
"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
}
|
{
"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"
}
}
}]
}
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:
|
{
"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:
|
{
"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:
|
{
"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:
|
{
"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 |
3.40282e+38 |
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:
|
{
"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:
|
{
"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 |
3.40E+3 |
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:
|
{
"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:
|
{
"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"
}
|
{
"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
}
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 |
1000 |
1 |
The number of times an intervention is given, used with Timesteps_Between_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 |
3.40E+38 |
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:
|
{
"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:
|
{
"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"
}
}
|
{
"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 nodes 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.
The BirthTriggeredIV intervention class monitors for birth events and then distributes an actual intervention to the new individuals as specified in Actual_IndividualIntervention_Config.
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 sought. 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"
}
}
|
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"
]
}
|
Duration |
float |
-1 |
3.40E+3 |
-1 |
The number of days to continue this intervention. Note For BirthTriggeredIV, specifying a value of -1 results in indefinite persistence of the birth-triggered intervention. |
{
"Duration": -1
}
|
Intervention_Name |
string |
NA |
NA |
BirthTriggeredIV |
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": "BirthTriggeredIV",
"Intervention_Name": "Measles vaccination at 6 months"
}
}
|
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"
}
|
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_Demographic |
enum |
NA |
NA |
Everyone |
The target demographic group. Possible values are:
|
{
"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:
|
{
"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
}
|
{
"Use_Defaults": 1,
"Events": [{
"class": "CampaignEventByYear",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Start_Year": 1960,
"Event_Coordinator_Config": {
"class": "StandardInterventionDistributionEventCoordinator",
"Intervention_Config": {
"class": "BirthTriggeredIV",
"Target_Demographic": "ExplicitGender",
"Target_Gender": "Male",
"Demographic_Coverage": 1,
"Actual_IndividualIntervention_Config": {
"class": "HIVSigmoidByYearAndSexDiagnostic",
"New_Property_Value": "InterventionStatus:None",
"Ramp_Min": 0.0,
"Ramp_Max": 0.3,
"Ramp_MidYear": 2002.0,
"Ramp_Rate": 0.5,
"Positive_Diagnosis_Event": "HIVNeedsMaleCircumcision"
}
}
}
}]
}
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. 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 node event to broadcast. This event must be set in the Custom_Node_Events configuration parameter. |
{
"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"
}
}
}
|
{
"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
}
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.
Warning
Be careful when configuring import pressure in multi-node simulations. Daily_Import_Pressures defines a rate of per-day importation for each node that the intervention is distributed to. In a 10 node simulation with Daily_Import_Pressures = [0.1, 5.0], the total importation rate summed over all nodes will be 1/day and 50/day during the two time periods. You must divide the per-day importation rates by the number of nodes.
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_Import_Pressures |
array of floats |
0 |
3.40E+3 |
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 |
2.15E+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 |
2.15E+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 |
2.15E+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"
}
}
|
{
"Use_Defaults": 1,
"Campaign_Name": "Initial Seeding",
"Events": [{
"class": "CampaignEvent",
"Start_Day": 1,
"Event_Name": "Outbreak",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Event_Coordinator_Config": {
"class": "StandardInterventionDistributionEventCoordinator",
"Target_Demographic": "Everyone",
"Demographic_Coverage": 1.0,
"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"
}
}
}]
}
The MigrateFamily intervention class tells residents of the targeted node to go on a family trip. The duration of time residents wait before migration can be configured; the “timer” for this duration does not start until all residents are “home”.
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 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:
|
{
"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 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 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 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 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 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 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 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 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 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 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 intervention-based migration. Each assigned value is a random draw from the distribution. Possible values are:
|
{
"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 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 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 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 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 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 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 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 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 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 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 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 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 familes 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": "MigrateFamily",
"Intervention_Name": "Move family for seasonal work"
}
}
|
Is_Moving |
boolean |
0 |
1 |
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 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 |
2.15E+0 |
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
}
}
}
}
]
}
The MultiNodeInterventionDistributor intervention class distributes multiple node-level interventions when the distributor only allows specifying one 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 |
|---|---|---|---|---|---|---|
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 |
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": "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
}
]
}
}
The NodeLevelHealthTriggeredIV intervention class distributes an intervention to an individual when a specific event occurs. 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.
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 sought. 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 |
“” |
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 |
0 |
1 |
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+3 |
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 |
0 |
1 |
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 |
3.40E+3 |
-1 |
The number of days to continue this intervention. Note For BirthTriggeredIV, specifying a value of -1 results in indefinite persistence of the birth-triggered intervention. |
{
"Duration": -1
}
|
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": "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_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 |
3.40E+3 |
3.40E+38 |
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_Demographic |
enum |
NA |
NA |
Everyone |
The target demographic group. Possible values are:
|
{
"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:
|
{
"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 |
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 Listed_Events in the simulation configuration file. |
{
"Trigger_Condition_List": [
"OnART3"
]
}
|
{
"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,
"Actual_IndividualIntervention_Config": {
"class": "OutbreakIndividual",
"Antigen": 0,
"Genome": 0,
"Outbreak_Source": "PrevalenceIncrease"
}
}
}
}]
}
The NodeLevelHealthTriggeredIVScaleUpSwitch intervention class transitions from one intervention to another over time. Generally this is used if one type of diagnostic tool is being phased out but the transition to replace it with the new diagnostic takes place over a few years. The individuals who are included by Demographic_Coverage will receive the new intervention and those that aren’t will receive the older “not covered” 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 individual intervention sought. 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 |
“” |
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 |
0 |
1 |
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+3 |
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
}
|
Demographic_Coverage_Time_Profile |
enum |
NA |
NA |
Immediate |
The profile for the ramp-up time to demographic coverage. Possible values are:
|
{
"Demographic_Coverage_Time_Profile": "Linear"
}
|
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 |
0 |
1 |
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 |
3.40E+3 |
-1 |
The number of days to continue this intervention. Note For BirthTriggeredIV, specifying a value of -1 results in indefinite persistence of the birth-triggered intervention. |
{
"Duration": -1
}
|
Initial_Demographic_Coverage |
float |
0 |
1.00E+0 |
0 |
The initial level of demographic coverage when using linear scale-up. |
{
"Initial_Demographic_Coverage": 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": "NodeLevelHealthTriggeredIVScaleUpSwitch",
"Intervention_Name": "Transition to CDC-recommended diagnostic"
}
}
|
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"
}
]
}
|
Not_Covered_IndividualIntervention_Configs |
array of JSON objects |
NA |
NA |
NA |
The array of interventions that is distributed if an individual qualifies according to all parameters except Demographic_Coverage. Generally, this is used to specify a diagnostic tool that is being phased out. |
{
"Not_Covered_IndividualIntervention_Configs": [
{
"class": "DelayedIntervention",
"Delay_Distribution": "FIXED_DURATION",
"Delay_Period": 10,
"Coverage": 1,
"Actual_IndividualIntervention_Configs": [
{
"class": "AntiTBDrug",
"Cost_To_Consumer": 90,
"Drug_Type": "FirstLineCombo",
"Durability_Profile": "FIXED_DURATION_CONSTANT_EFFECT",
"Primary_Decay_Time_Constant": 180,
"Remaining_Doses": 1,
"TB_Drug_Cure_Rate": 5e-11,
"TB_Drug_Inactivation_Rate": 0
}
]
}
]
}
|
Primary_Time_Constant |
float |
0 |
3.65E+0 |
1 |
The time to full scale-up demographic coverage. |
{
"Primary_Time_Constant": 150
}
|
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_Demographic |
enum |
NA |
NA |
Everyone |
The target demographic group. Possible values are:
|
{
"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:
|
{
"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 |
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 Listed_Events in the simulation configuration file. |
{
"Trigger_Condition_List": [
"OnART3"
]
}
|
{
"Use_Defaults": 1,
"Campaign_Name": "Illustration of NodeLevelScaleUp: Outbreak to smear- and smear+ at day 100, then diagnostic and treatment of only smear+ cases at day 200",
"Events": [
{
"Event_Name": "when someone broadcasts a positive test using smear, give them the drug",
"class": "CampaignEvent",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Start_Day": 99,
"Event_Coordinator_Config": {
"class": "StandardInterventionDistributionEventCoordinator",
"Demographic_Coverage": 1,
"Number_Repetitions": 1,
"Property_Restrictions": [],
"Target_Demographic": "Everyone",
"Timesteps_Between_Repetitions": -1,
"Intervention_Config": {
"class": "NodeLevelHealthTriggeredIVScaleUpSwitch",
"Demographic_Coverage": 1,
"Demographic_Coverage_Time_Profile": "Linear",
"Initial_Demographic_Coverage": 0,
"Primary_Time_Constant": 150,
"Property_Restrictions_Within_Node": [],
"Not_Covered_IndividualIntervention_Configs": [
{
"class": "DelayedIntervention",
"Delay_Period_Distribution": "CONSTANT_DISTRIBUTION",
"Delay_Period_Constant": 10,
"Coverage": 1,
"Actual_IndividualIntervention_Configs": [
{
"class": "SmearDiagnostic",
"Base_Sensitivity": 1,
"Base_Specificity": 1,
"Cost_To_Consumer": 10,
"Days_To_Diagnosis": 0,
"Treatment_Fraction": 1,
"Event_Or_Config": "Event",
"Positive_Diagnosis_Event": "TestPositiveOnSmear"
}
]
}
],
"Actual_IndividualIntervention_Config": {
"class": "ActiveDiagnostic",
"Base_Sensitivity": 1,
"Base_Specificity": 1,
"Cost_To_Consumer": 8,
"Days_To_Diagnosis": 0,
"Treatment_Fraction": 1,
"Event_Or_Config": "Event",
"Positive_Diagnosis_Event": "TestPositiveOnActive"
}
}
}
}
]
}
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 a node 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 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 |
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"
}
|
Maximum_Duration |
float |
-1 |
3.40E+38 |
3.40E+38 |
The maximum amount of time 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 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_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
}
}
}]
}
The Outbreak class allows the introduction of a disease outbreak event by the addition of new infected individuals or infection of existing individuals in 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 |
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 (set in the configuration file). |
{
"Clade": 1
}
|
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": "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 infected import cases. |
{
"Import_Age": 10000
}
|
Incubation_Period_Override |
integer |
-1 |
2.15E+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 |
2.15E+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"
}
}
|
{
"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"
}
]
}
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.
The BroadcastEvent intervention class 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 |
“” |
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 |
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 |
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 |
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": "4C: HIVMuxer",
"Events": [
{
"Description": "Drive initial population into a loop",
"class": "CampaignEvent",
"Start_Day": 1,
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Event_Coordinator_Config": {
"class": "StandardInterventionDistributionEventCoordinator",
"Intervention_Config": {
"class": "BroadcastEvent",
"Broadcast_Event": "Loop_Entry_InitialPopulation"
}
}
},
{
"Description": "Wait one year, only one entry allowed at a time per individual",
"class": "CampaignEvent",
"Start_Day": 1,
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Event_Coordinator_Config": {
"class": "StandardInterventionDistributionEventCoordinator",
"Intervention_Config": {
"class": "NodeLevelHealthTriggeredIV",
"Trigger_Condition_List": [
"Loop_Entry_InitialPopulation",
"Loop_Entry_Birth",
"Done_Waiting"
],
"Actual_IndividualIntervention_Config": {
"class": "HIVMuxer",
"Disqualifying_Properties": [
"InterventionStatus:Abort_Infinite_Loop"
],
"New_Property_Value": "InterventionStatus:Infinite_Loop",
"Delay_Period_Distribution": "CONSTANT_DISTRIBUTION",
"Delay_Period_Constant": 365,
"Muxer_Name": "Delay_Loop_Muxer",
"Max_Entries": 1,
"Broadcast_Event": "Done_Waiting"
}
}
}
}
]
}
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.
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 |
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_Trigger |
enum |
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 |
0 |
1 |
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+3 |
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 |
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"
}
|
Node_Selection_Type |
enum |
NA |
NA |
DISTANCE_ONLY |
The method by which to select nodes to receive the event. Possible values are:
|
{
"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
}
}
}
}
]
}
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
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. |
{
"Distributed_Event_Trigger": "Vaccinated"
}
|
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
}
|
Duration_To_Wait_Before_Revaccination |
float |
0 |
3.40E+3 |
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 |
0 |
1 |
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 |
“” |
The name of the event to be broadcast when the intervention is distributed to an individual. See Event list for possible values. |
{
"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:
|
{
"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
}
}
}
]
}
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. |
{
"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:
|
{
"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
}
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
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,
"Event_Or_Config": "Event",
"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,
"Event_Or_Config": "Event",
"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,
"Event_Or_Config": "Event",
"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,
"Event_Or_Config": "Event",
"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"
],
"Event_Or_Config": "Event",
"Negative_Diagnosis_Event": "TestedNegative_IamSusceptible",
"Positive_Diagnosis_Event": "TestedPositive_IamImmune",
"Positive_Threshold_AcquisitionImmunity": 0.99,
"Treatment_Fraction": 1,
"class": "ImmunityBloodTest"
}
}
}
|
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. |
{
"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,
"Event_Or_Config": "Event",
"Negative_Diagnosis_Event": "TestedNegative_IamSusceptible",
"Positive_Diagnosis_Event": "TestedPositive_IamImmune",
"Positive_Threshold_AcquisitionImmunity": 0.99,
"Treatment_Fraction": 1,
"class": "ImmunityBloodTest"
}
}
}
|
Enable_IsSymptomatic |
boolean |
0 |
1 |
0 |
If true (1), requires an infection to be symptomatic to return a positive test. |
{
"Enable_IsSymptomatic": 1,
"Base_Specificity": 0.85,
"Base_Sensitivity": 0.92
}
|
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. |
{
"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": "ImmunityBloodTest"
}
}
}
|
Negative_Diagnosis_Event |
enum |
NA |
NA |
“” |
If an individual tests negative (does not have immunity), then an individual type event is broadcast. This may trigger another intervention when the event occurs. Only used when Event_Or_Config is set to Event. |
{
"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",
"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 distributed. 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_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": {
"Demographic_Coverage": 1,
"Intervention_Config": {
"Base_Sensitivity": 1,
"Base_Specificity": 1,
"Cost_To_Consumer": 0,
"Days_To_Diagnosis": 0,
"Event_Or_Config": "Event",
"Negative_Diagnosis_Event": "TestedNegative_IamSusceptible",
"Positive_Diagnosis_Event": "TestedPositive_IamImmune",
"Positive_Threshold_AcquisitionImmunity": 0.99,
"Treatment_Fraction": 1,
"class": "ImmunityBloodTest"
}
}
}
|
Positive_Diagnosis_Event |
enum |
NA |
NA |
“” |
If the test is positive (has immunity), then an individual type event is broadcast. This may trigger another intervention when the event occurs. Only used if Event_Or_Config is set to Event. |
{
"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",
"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,
"Event_Or_Config": "Event",
"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,
"Event_Or_Config": "Event",
"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,
"Event_Or_Config": "Event",
"Positive_Diagnosis_Event": "TestedPositive_IamImmune",
"Negative_Diagnosis_Event": "TestedNegative_IamSusceptible",
"Treatment_Fraction": 1.0,
"Positive_Threshold_AcquisitionImmunity": 0.99,
"class": "ImmunityBloodTest"
}
}
}],
"Use_Defaults":1
}
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 |
10 |
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
}
}
}]
}
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"
}
}
]
}
}
|
Age |
float |
0 |
45625 |
NA |
An array of ages (in days). 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. |
{
"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
}
]
}
|
Calendar |
array of JSON objects |
NA |
NA |
NA |
An array of ages, days and the probabilities of receiving the list of interventions at each age. Calendar has two parameters: Probability and 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 |
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
}
|
Dropout |
boolean |
0 |
1 |
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 |
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": "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"
}
|
Probability |
float |
0 |
1 |
NA |
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
}
]
}
|
{
"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"
}
}]
}
}
}
}]
}
The MigrateIndividuals intervention class is used to force migration and is separate from the normal migration system. However, it does require that human migration is enabled.
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 |
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
}
|
Duration_At_Node_Constant |
float |
0 |
3.40282E+38 |
-1 |
The duration at 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:
|
{
"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 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 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 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 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 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 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 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 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 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 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 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 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 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:
|
{
"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 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 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 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 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 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 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 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 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 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 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 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 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 |
0 |
1 |
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 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 |
2.15E+0 |
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
}
}
}
]
}
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 |
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 |
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": "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 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_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"
}]
}
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 |
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 |
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": "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 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"
}
|
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
}
The MultiInterventionDistributer 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 |
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_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 |
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": "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 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"
}
|
Number_Repetitions |
integer |
-1 |
1000 |
1 |
The number of times an intervention is given, used with Timesteps_Between_Repetitions. |
{
"Event_Coordinator_Config": {
"Intervention_Config": {
"class": "Outbreak",
"Num_Cases": 1
},
"Number_Repetitions": 10,
"Timesteps_Between_Repetitions": 50,
"class": "StandardInterventionDistributionEventCoordinator"
}
}
|
Timesteps_Between_Repetitions |
integer |
-1 |
10000 |
-1 |
The repetition interval. |
{
"Timesteps_Between_Repetitions": 50
}
|
{
"Events": [
{
"class": "CampaignEvent",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Start_Day": 0,
"Event_Coordinator_Config": {
"class": "CommunityHealthWorkerEventCoordinator",
"Demographic_Coverage": 1,
"Target_Demographic": "Everyone",
"Target_Residents_Only": 0,
"Trigger_Condition_List": [
"HappyBirthday"
],
"Amount_In_Shipment": 1000,
"Days_Between_Shipments": 30,
"Max_Distributed_Per_Day": 50,
"Duration": 700,
"Waiting_Period": 7,
"Max_Stock": 1000,
"Initial_Amount": 500,
"Initial_Amount_Distribution_Type": "CONSTANT_DISTRIBUTION",
"Intervention_Config": {
"class": "MultiInterventionDistributor",
"Intervention_List": [
{
"class": "SimpleVaccine",
"Cost_To_Consumer": 1,
"Vaccine_Type": "AcquisitionBlocking",
"Vaccine_Take": 1,
"Efficacy_Is_Multiplicative": 1,
"Waning_Config": {
"class": "WaningEffectConstant",
"Initial_Effect": 0.9
}
},
{
"class": "BroadcastEvent",
"Broadcast_Event": "Received_Treatment"
}
]
}
}
}
],
"Use_Defaults": 1
}
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
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 |
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 (set in the configuration file). |
{
"Clade": 1
}
|
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: {
"Genome": 3
}
|
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 |
2.15E+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"
},
"Event_Name": "OutbreakIndividual",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Start_Day": 30,
"class": "CampaignEvent"
}
]
}
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 |
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 |
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": "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 |
10000 |
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 |
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 |
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
}
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 |
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
}
|
Efficacy_Is_Multiplicative |
boolean |
0 |
1 |
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 |
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": "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:
|
{
"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"
}
}
}
}]
}
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+3 |
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+3 |
0 |
The number of days from test until 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 |
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_IsSymptomatic |
boolean |
0 |
1 |
0 |
If true (1), requires an infection to be symptomatic to return a positive test. |
{
"Enable_IsSymptomatic": 1,
"Base_Specificity": 0.85,
"Base_Sensitivity": 0.92
}
|
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"
}
|
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": "MultiInterventionDistributor",
"Intervention_List": [
{
"Cost_To_Consumer": 0.333,
"Secondary_Decay_Time_Constant": 1,
"Vaccine_Take": 1,
"Vaccine_Type": "AcquisitionBlocking",
"class": "SimpleVaccine",
"Waning_Config": {
"Box_Duration": 3650,
"Initial_Effect": 0.1,
"class": "WaningEffectBox"
}
}
]
}
}
|
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. |
{
"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": "SimpleDiagnostic"
}
}
|
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": "SimpleDiagnostic"
}
}
|
{
"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
}
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 |
“” |
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 |
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_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": {
"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 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"
}
|
Single_Use |
boolean |
0 |
1 |
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": ["TBActivation"],
"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
}
}
}
}
}]
}
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 |
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
}
|
Efficacy_Is_Multiplicative |
boolean |
0 |
1 |
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
}
}
}
|
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_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": {
"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 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:
|
{
"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
}
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
}
Contents
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"
}
}
|
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
}
|
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"
}
}
|
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"
}
}
|
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"
}
}
|
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 |
2.15E+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 |
3.40E+3 |
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
]
}
|
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 |
3.40E+3 |
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
]
}
}
|
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 |
3.40E+3 |
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
]
}
|
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 |
2.15E+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 |
3.40E+3 |
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
]
}
|
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"
}
}
|
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 Listed_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
HIVNonPreARTToART
HIVPreARTToART
HIVTestedNegative
HIVTestedPositive
Immigrating
InterventionDisqualified
NewClinicalCase
NewExternalHIVInfection
NewInfectionEvent
NewlySymptomatic
NewSevereCase
NodePropertyChange
NonDiseaseDeaths
OpportunisticInfectionDeat
Pregnant
PropertyChange
ProviderOrdersTBTest
SixWeeksOld
StartedART
STIDebut
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
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.
Long form |
Short form |
Description |
|---|---|---|
|
|
Shows help options for Eradication.exe. |
|
|
Shows the version information and supported simulation types. Note capitalization of short alternative. |
|
|
Outputs the configuration and campaign parameters. Unless |
|
|
When used with |
Usage¶
Open a Command Prompt window and navigate to the directory where Eradication.exe is installed.
To output the schema to the Command Prompt window, enter:
Eradication.exe --get-schema
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.
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.
Contents
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.
- disease-specific build¶
A build of the EMOD executable (Eradication.exe) built using SCons without any dynamic link libraries (DLLs).
- dynamic link library (DLL)¶
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.
- 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.
- Link-Time Code Generation (LTCG)¶
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.
- 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.
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 v8 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.
Install the Microsoft HPC Pack 2012 Client Utilities Redistributable Package (64-bit). See http://www.microsoft.com/en-us/download/details.aspx?id=36044 for instructions.
Install the Microsoft MPI v8. See https://www.microsoft.com/en-us/download/details.aspx?id=54607 for instructions, being sure to run the MSMpiSetup.exe file.
Install the Microsoft Visual C++ 2015 Redistributable (64-bit). See https://www.microsoft.com/en-us/download/details.aspx?id=53840 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¶
Purchase a license from Microsoft or use an MSDN subscription to install Visual Studio 2015 (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.
Select Desktop development with C++ during installation.
Python¶
Python is required for building the disease-specific Eradication.exe and running Python scripts.
In a web browser, go to https://www.python.org/downloads/release/python-366/ to install Python 3.6 64-bit.
Scroll down and download one of the x86-64 bit installers (you may use the executable installer or the web-based installer.)
Double-click the executable file and, in the installer window, select the Add Python 3.6 to PATH checkbox and click Customize installation.
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.
If you are running EMOD locally, IDM recommends that you select the Advanced Options window to customize the installation directory to “C:\Python36”.
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.
Click Install. When installation is complete, click Close.
To verify installation, open a Command Prompt window and type the following:
python --version
From Control Panel, select Advanced system settings, and then click Environment Variables.
To build the source code using Python 3.6 64-bit, add a new variable called IDM_PYTHON3_PATH and set it to the directory where you installed Python 3.6 64-bit, and then click OK.
To build the source using Python 2.7, add a new variable called IDM_PYTHON_PATH and set it to the directory where you installed Python 2.7, and then click OK.
Restart Visual Studio if it was open when you set the environment variables.
HPC SDK¶
Install the Microsoft HPC Pack 2012 SDK (64-bit). See https://www.microsoft.com/en-us/download/details.aspx?id=36043 for instructions.
Boost¶
Go to https://sourceforge.net/projects/boost/files/boost/1.61.0/ and select one of the compressed files.
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_61_0//boost_1_61_0), delete the extra folder.
From Control Panel, select Advanced system settings, and then click Environment Variables.
Add a new variable called IDM_BOOST_PATH and set it to the directory where you installed Boost, and then click OK.
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.
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 “cp36-cp36m”, 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.
Go to http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy and select the WHL file for NumPy 1.16.2 (64-bit) that is compatible with Python 3.6 64-bit.
Save the file to your Python installation directory.
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-cp36-cp36m-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.
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 3.2.0 (64-bit) for regression testing, but it is considered optional.
R is a free software environment for statistical computing and graphics.
Go to https://www.r-project.org/ and install R 3.2.0 (64-bit).
(Optional) MATLAB¶
The IDM test team uses MATLAB R2015a and the MATLAB Statistics and Machine Learning Toolbox™ R2015a 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.
Go to http://www.mathworks.com/products/matlab/ and install MATLAB R2015a.
If desired, go to https://www.mathworks.com/products/statistics.html and install the MATLAB Statistics and Machine Learning Toolbox™ R2015a.
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 v8 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
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.
Set the EMOD_ROOT environment variable to the path to the EMOD source path:
EMOD_ROOT=~/IDM/EMOD
Include Scripts and . in the path:
export PATH=$PATH:.:$EMOD_ROOT/Scripts
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 “cp36-cp36m”, and the Windows bit version after “win”, for example “win_amd64”.
(Optional) R¶
The IDM test team uses R 3.2.0 (64-bit) for regression testing, but it is considered optional.
R is a free software environment for statistical computing and graphics.
Go to https://www.r-project.org/ and install R 3.2.0 (64-bit).
(Optional) MATLAB¶
The IDM test team uses MATLAB R2015a and the MATLAB Statistics and Machine Learning Toolbox™ R2015a 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.
Go to http://www.mathworks.com/products/matlab/ and install MATLAB R2015a.
If desired, go to https://www.mathworks.com/products/statistics.html and install the MATLAB Statistics and Machine Learning Toolbox™ R2015a.
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¶
Launch the Git GUI application and click Clone Existing Repository.
From the Clone Existing Repository window:
In Source Location, enter https://github.com/InstituteforDiseaseModeling/EMOD
In Target Directory, enter the location and target directory name: C:/IDM/EMOD
Click Clone. Git GUI will create the directory and download the source code.
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.
Launch the Git Bash application. From the command line:
Navigate to the location where you want to save your copy of the EMOD source code:
cd C:/IDM
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.
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.
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
Navigate to the directory where you downloaded the metadata for the input files.
Download the actual data on your local machine:
git lfs fetch
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 2015 (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.
In Visual Studio, navigate to the directory where the EMOD repository is cloned and open the EradicationKernel solution.
If prompted to upgrade the C++ toolset used, do so.
From the Solution Configurations ribbon, select Debug or Release.
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.
Open a Command Prompt window.
Go to the directory where EMOD is installed:
cd C:\IDM\EMOD
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
--Diseaseflag:scons --Release --Disease=Vector
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”.
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
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.
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.
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”.
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
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.
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.
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.
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.
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.
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.
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 generic simulations, each node has a homogeneous contagion pool. Every individual in the node sheds disease into the pool and acquires disease from the pool. You can add heterogeneous disease transmission within a node by enabling and configuring the Heterogeneous Intra-Node Transmission (HINT) feature. For more information, see Property-based heterogeneous disease transmission.
The relationship between these classes is captured in the following figure.
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
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. |
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.
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.
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. |
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:
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:
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 \(X_i\).
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.
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.
Contents
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.
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.
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.
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 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 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.
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.
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.
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 for more information about the different output reports available.
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().
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 IDM 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.
In the C++ files that make up the EMOD source code, open the file for which you want to set the log level.
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.
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.
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.
Open the EradicationKernel solution in Visual Studio.
On the Solution Explorer pane, right-click the Eradication project and select Properties.
On the Property Pages window, in the Configuration Properties pane, click Debugging.
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.
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 1.61.0.
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 3.6 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 3.6 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.
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.
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.
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
--perfFalse
Measure Eradication.exe performance.
--hidegraphsFalse
Suppress pop-up graphs in case of validation failures.
--debugFalse
Use the debug path for EMODules.
--labelAdd a custom suffix for HPC job name.
--configregression_test.cfg
The regression test configuration file.
--disable-schema-testTrue
Include to suppress schema testing, which is on by default.
--use-dllsFalse
Use EMODules when running tests.
--all-outputsFalse
Use all output JSON files for validation, not just InsetChart.json.
--dll-pathThe path to the root directory of the EMODules to use.
--skip-emodule-checkFalse
Skip checking if EMODules on the cluster are up-to-date, which can be slow.
--sconsFalse
Indicate that this is a SCons build so custom DLLs are found in the build/64/Release directory.
--localFalse
Run all simulations locally.
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.
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.
Under the Regression directory, create a new subdirectory.
Copy the contents of the regression test that you want to base the new test on into the subdirectory.
Modify the configuration, campaign, and demographic files as desired.
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¶
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.
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.
From the Regression directory in a command prompt window, run the following, adding
--sconsif you built the Eradication.exe using SCONS:python regression_test.py my_sfts.json --local
Note
Enter
python regression_test.py --helpfor a list of all arguments you can use with the testing script.
Run on Windows using run_test.cmd¶
Generate a single config.json file from a base file and the parameter_overrides.json that contains (see Configuration overlay files for instructions).
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.
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¶
Generate a single config.json file from a base file and the parameter_overrides.json that contains (see Configuration overlay files for instructions).
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.
In a command prompt window, enter the following, substituting your path to the testing script as necessary:
../../run_test.sh