Welcome to IDM malaria modeling¶
The Institute for Disease Modeling (IDM) develops disease modeling software that is thoroughly tested and shared with the research community to advance the understanding of disease dynamics. This software helps determine the combination of health policies and intervention strategies that can lead to disease eradication. If you encounter any issues while using the software, please contact support@idmod.org.
The primary 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.
Documentation structure¶
“Using the model” contains information for researchers who want to create simulations of disease dynamics using EMOD as developed by IDM.
“Advancing the model” contains information for researchers and developers who want to modify the EMOD source code to add more functionality to the model.
IDM recently separated the EMOD documentation into disease-specific sets that provide guidance for researchers modeling particular diseases. The documentation contains only the parameters, output reports, and other components of the model that are available to use with this disease model.
For example, this documentation set includes general installation and usage instructions that are common in all simulation types in addition to content specific to modeling malaria and other vector-borne diseases.
What’s new¶
This topic describes new functionality and breaking changes for recently released versions of Epidemiological MODeling software (EMOD).
Contents
EMOD v2.18¶
The EMOD v2.18 release includes many new features for all supported simulation types. In particular, the TB_SIM simulation type has been deprecated and replaced with TBHIV_SIM, which does not include HIV transmission but adds the ability to model the effect of HIV coinfection on the spread of TB.
New configuration parameters¶
For the malaria simulation type, the following new configuration parameters are available:
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Genome_Markers |
array of strings |
NA |
NA |
NA |
A list of the names (strings) of genome marker(s) that represent the genetic components in a strain of an infection. |
{
"Genome_Markers": [
"D6",
"W2"
]
}
|
Resistance |
JSON object |
NA |
NA |
NA |
Specifies the drug resistance multiplier. |
{
"parameters": {
"Genome_Markers": [
"A",
"B"
],
"Malaria_Drug_Params": {
"Artemether_Lumefantrine": {
"Bodyweight_Exponent": 0,
"Drug_Cmax": 1000,
"Drug_Decay_T1": 1,
"Drug_Decay_T2": 1,
"Drug_Dose_Interval": 1,
"Drug_Fulltreatment_Doses": 3,
"Drug_Gametocyte02_Killrate": 2.3,
"Drug_Gametocyte34_Killrate": 2.3,
"Drug_GametocyteM_Killrate": 0,
"Drug_Hepatocyte_Killrate": 0,
"Drug_PKPD_C50": 100,
"Drug_Vd": 10,
"Max_Drug_IRBC_Kill": 3.45
"Resistance": {
"A": {
"Max_IRBC_Kill_Modifier": 0.05,
"PKPD_C50_Modifier": 1.0
},
"B": {
"Max_IRBC_Kill_Modifier": 0.25,
"PKPD_C50_Modifier": 1.0
}
}
}
}
}
}
|
To view all available configuration parameters, see Configuration parameters.
New campaign parameters¶
The following campaign classes are new for the Generic model and can be used in all other models:
IncidenceEventCoordinator (generic)¶
The IncidenceEventCoordinator coordinator class distributes interventions based on the number of events counted over a period of time.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Action_List |
array of JSON objects |
NA |
NA |
NA |
List (array) of JSON objects, including the values specified in the following parameters: Threshold, Event_To_Broadcast. |
{
"Threshold_Type" : "COUNT",
"Action_List" :
[
{
"Threshold" : 20,
"Event_To_Broadcast" : "Action_Event_1"
},
{
"Threshold" : 30,
"Event_To_Broadcast" : "Action_Event_2"
}
]
}
|
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"
}
]
}
|
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 the threshold type and values and the actions to take when the specified conditions and parameters have been met, as configured in the Incidence_Counter parameter. Some of the values are specified in the following parameters: ** Threshold_Type ** ** Action_List ** ** Threshold ** ** Event_To_Broadcast ** |
{
"Responder" :
{
"Threshold_Type" : "COUNT",
"Action_List" :
[
{
"Threshold" : 5,
"Event_To_Broadcast" : "Action_Event_1"
},
{
"Threshold" : 10,
"Event_To_Broadcast" : "Action_Event_2"
}
]
}
}
|
Threshold |
float |
0 |
3.40E+03 |
0 |
The threshold value that triggers the specified action for the Event_To_Broadcast parameter. When you use the Threshold parameter you must also use the Event_To_Broadcast parameter. |
{
"Threshold_Type" : "COUNT",
"Action_List" :
[
{
"Threshold" : 20,
"Event_To_Broadcast" : "Action_Event_1"
},
{
"Threshold" : 30,
"Event_To_Broadcast" : "Action_Event_2"
}
]
}
|
Threshold_Type |
enum |
NA |
NA |
COUNT |
Threshold type. Possible values are: * COUNT, * PERCENTAGE. |
{
"Threshold_Type" : "COUNT",
"Action_List" :
[
{
"Threshold" : 20,
"Event_To_Broadcast" : "Action_Event_1"
},
{
"Threshold" : 30,
"Event_To_Broadcast" : "Action_Event_2"
}
]
}
|
Timesteps_Between_Repetitions |
integer |
-1 |
1000 |
-1 |
The repetition interval. |
{
"class": "IncidenceEventCoordinator",
"Number_Repetitions" : 3,
"Timesteps_Between_Repetitions" : 6
}
|
Trigger_Condition_List |
array of strings |
NA |
NA |
NA |
A list of events that will trigger an intervention. |
{
"Trigger_Condition_List": [
"NewClinicalCase",
"NewSevereCase"
]
}
|
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 |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
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"
}
|
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 |
NA |
NA |
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 |
NA |
NA |
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 |
array of JSON objects |
NA |
NA |
NA |
A list of nested JSON objects for the interventions to be distributed by this event coordinator. |
{
"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 |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
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 |
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"
}
|
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 |
NoTrigger |
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 |
|---|---|---|---|---|---|---|
Constant |
float |
0 |
3.40E+03 |
6 |
When Risk_Distribution_Type is set to FIXED_DURATION, this will specify the time delay (in number of days). |
{
"Intervention_Config": {
"class": "BitingRisk",
"Risk_Distribution_Type" : "FIXED_DURATION",
"Constant" : 0.1
}
}
|
Disqualifying_Properties |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
Exponential_Mean |
float |
0 |
3.40E+03 |
6 |
When Risk_Distribution_Type is set to EXPONENTIAL_DURATION, the Exponential_Mean parameter represents the exponential rate that describes the distribution of the time delay (in units of 1/days). |
{
"Risk_Distribution_Type": "EXPONENTIAL_DURATION",
"Exponential_Mean": 10
}
|
Gaussian_Mean |
float |
0 |
3.40E+03 |
6 |
When Risk_Distribution_Type is set to GAUSSIAN_DURATION, the Gaussian_Mean parameter defines the mean deviation of the Gaussian distribution for the active period. Negative values are truncated at zero. |
{
"Risk_Distribution_Type": "GAUSSIAN_DURATION",
"Gaussian_Mean": 25,
"Gaussian_Std_Dev": 5
}
|
Gaussian_Std_Dev |
float |
0 |
3.40E+03 |
1 |
When Risk_Distribution_Type is set to GAUSSIAN_DURATION, the Gaussian_Std_Dev parameter defines the standard deviation of the Gaussian distribution for the active period. Negative values are truncated at zero. |
{
"Risk_Distribution_Type": "GAUSSIAN_DURATION",
"Gaussian_Mean": 25,
"Gaussian_Std_Dev": 5
}
|
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"
}
|
Risk_Distribution_Type |
enum |
NA |
NA |
NOT_INITIALIZED |
The distribution of the time delay from when the biting risk intervention is distributed to when the individual actually receives the intervention. Possible values are:
|
{
"Risk_Distribution_Type": "GAUSSIAN_DURATION",
"Gaussian_Mean": 25,
"Gaussian_Std_Dev": 5
}
|
Uniform_Max |
float |
0 |
3.40E+03 |
1 |
The maximum time delay (in number of days) when Risk_Distribution_Type is set to UNIFORM_DURATION. |
{
"BR_Risk_Distribution_Type": "UNIFORM_DURATION",
"BR_Uniform_Min": 1,
"BR_Uniform_Max": 30
}
|
Uniform_Min |
float |
0 |
3.40E+03 |
0 |
The minimum time delay (in number of days) when Risk_Distribution_Type is set to UNIFORM_DURATION. |
{
"BR_Risk_Distribution_Type": "UNIFORM_DURATION",
"BR_Uniform_Min": 1,
"BR_Uniform_Max": 30
}
|
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 |
|---|---|---|---|---|---|---|
Antigen |
integer |
0 |
10 |
0 |
The antigenic ID of the outbreak infection. |
{
"Intervention_Config": {
"Antigen": 0,
"Genome": 0,
"Outbreak_Source": "PrevalenceIncrease",
"class": "OutbreakIndividual"
}
}
|
Create_Random_Genome |
boolean |
NA |
NA |
0 |
If set to true (1), then a random genome is created for the infection and the Genome_Markers parameter is not used. If set to false (0), then you must define the Genome_Markers parameter which allows you to then specify genetic components in a strain of infection. |
{
"OIM_Create_Random_Genome": 1
}
|
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 |
NA |
NA |
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 |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
Dose_Interval |
float |
0 |
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_Name":"Diagnostic_Sample"
}
|
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_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)
Vector-borne diseases (VECTOR_SIM)
Malaria (MALARIA_SIM)
Tuberculosis with HIV coinfection (TBHIV_SIM)
Sexually transmitted infections (STI_SIM)
HIV (HIV_SIM)
For information about building the schema, see Generate a list of all available parameters (a schema).
EMOD v2.13¶
The EMOD v2.13 release includes many new features for all supported simulation types.
New configuration parameters¶
For the malaria simulation type, the following new configuration parameters are available:
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Custom_Reports_Filename |
string |
NA |
NA |
UNINITIALIZED STRING |
The name of the file containing custom report configuration parameters. Omitting this parameter or setting it to RunAllCustomReports will load all reporters found that are valid for the given simulation type. The file must be in JSON format. |
{
"Custom_Reports_Filename": "custom_reports.json"
}
|
Cycle_Arrhenius_1 |
float |
0 |
1.00E+15 |
4.09E+10 |
The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the mosquito feeding cycle rate. This duration is a decreasing function of temperature. The variable a1 is a temperature-independent scale factor on feeding rate. Temperature_Dependent_Feeding_Cycle must be set to ARRHENIUS_DEPENDENCE. |
{
"Temperature_Dependent_Feeding_Cycle": "ARRHENIUS_DEPENDENCE",
"Vector_Species_Params": {
"arabiensis": {
"Cycle_Arrhenius_1": 99,
"Cycle_Arrhenius_2": 88
}
}
}
|
Cycle_Arrhenius_2 |
float |
0 |
1.00E+15 |
7740 |
The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the mosquito feeding cycle rate. This duration is a decreasing function of temperature. The variable a2 is a temperature-independent scale factor on feeding rate. Temperature_Dependent_Feeding_Cycle must be set to ARRHENIUS_DEPENDENCE. |
{
"Temperature_Dependent_Feeding_Cycle": "ARRHENIUS_DEPENDENCE",
"Vector_Species_Params": {
"arabiensis": {
"Cycle_Arrhenius_1": 99,
"Cycle_Arrhenius_2": 88
}
}
}
|
Cycle_Arrhenius_Reduction_Factor |
float |
0 |
1 |
1 |
The scale factor applied to cycle duration (from oviposition to oviposition) to reduce the duration when primary follicles are at stage II rather than I in the case of newly emerged females. Temperature_Dependent_Feeding_Cycle must be set to ARRHENIUS_DEPENDENCE. |
{
"Temperature_Dependent_Feeding_Cycle": "ARRHENIUS_DEPENDENCE",
"Vector_Species_Params": {
"funestus": {
"Cycle_Arrhenius_Reduction_Factor": 0.44
}
}
}
|
Drought_Egg_Hatch_Delay |
float |
0 |
1 |
0.33 |
Proportion of regular egg hatching that still occurs when habitat dries up. Enable_Drought_Egg_Hatch_Delay must be set to 1. |
{
"Enable_Drought_Egg_Hatch_Delay": 1,
"Drought_Egg_Hatch_Delay": 0.33
}
|
Egg_Arrhenius1 |
float |
0 |
1.00E+10 |
6.16E+07 |
The Arrhenius equation, math:a_1^{-a_2/T}, with T in degrees Kelvin, parameterizes the daily rate of mosquito egg hatching. This duration is a decreasing function of temperature. The variable a1 is a temperature-independent scale factor on hatching rate. Enable_Temperature_Dependent_Egg_Hatching must be set to 1. |
{
"Enable_Temperature_Dependent_Egg_Hatching": 1,
"Egg_Arrhenius1": 61599956,
"Egg_Arrhenius2": 5754
}
|
Egg_Arrhenius2 |
float |
0 |
1.00E+10 |
5754.03 |
The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the daily rate of mosquito egg hatching. This duration is a decreasing function of temperature. The variable a2 is a temperature-dependent scale factor on hatching rate. Enable_Temperature_Dependent_Egg_Hatching must be set to 1. |
{
"Enable_Temperature_Dependent_Egg_Hatching": 1,
"Egg_Arrhenius1": 61599956,
"Egg_Arrhenius2": 5754
}
|
Egg_Hatch_Density_Dependence |
enum |
NA |
NA |
NO_DENSITY_DEPENDENCE |
The effect of larval density on egg hatching rate. Possible values are:
|
{
"Egg_Hatch_Density_Dependence": "NO_DENSITY_DEPENDENCE"
}
|
Enable_Drought_Egg_Hatch_Delay |
boolean |
0 |
1 |
0 |
Controls whether or not eggs hatch at delayed rates, set by Drought_Egg_Hatch_Delay, when the habitat dries up completely. |
{
"Enable_Drought_Egg_Hatch_Delay": 1,
"Drought_Egg_Hatch_Delay": 0.33
}
|
Enable_Egg_Mortality |
boolean |
0 |
1 |
0 |
Controls whether or not to include a daily mortality rate on the egg population, which is independent of climatic factors. |
{
"Enable_Egg_Mortality": 1,
}
|
Enable_Temperature_Dependent_Egg_Hatching |
boolean |
0 |
1 |
0 |
Controls whether or not temperature has an effect on egg hatching, defined by Egg_Arrhenius_1 and Egg_Arrhenius_2. |
{
"Enable_Temperature_Dependent_Egg_Hatching": 1,
"Egg_Arrhenius1": 61599956.864,
"Egg_Arrhenius2": 5754.033
}
|
Enable_Vector_Aging |
boolean |
0 |
1 |
0 |
Controls whether or not vectors undergo senescence as they age. |
{
"Enable_Vector_Aging": 1
}
|
Incubation_Period_Log_Mean |
float |
0 |
3.40E+38 |
6 |
The mean of log normal for the incubation period distribution. Incubation_Period_Distribution must be set to LOG_NORMAL_DURATION. |
{
"Incubation_Period_Distribution": "LOG_NORMAL_DURATION",
"Incubation_Period_Log_Mean": 5.758,
"Incubation_Period_Log_Width": 0.27
}
|
Incubation_Period_Log_Width |
float |
0 |
3.40E+38 |
1 |
The log width of log normal for the incubation period distribution. Incubation_Period_Distribution must be set to LOG_NORMAL_DURATION. |
{
"Incubation_Period_Distribution": "LOG_NORMAL_DURATION",
"Incubation_Period_Log_Mean": 5.758,
"Incubation_Period_Log_Width": 0.27
}
|
Infectivity_Exponential_Baseline |
float |
0 |
1 |
0 |
The scale factor applied to Base_Infectivity at the beginning of a simulation, before the infectivity begins to grow exponentially. Infectivity_Scale_Type must be set to EXPONENTIAL_FUNCTION_OF_TIME. |
{
"Infectivity_Exponential_Baseline": 0.1,
"Infectivity_Exponential_Delay": 90,
"Infectivity_Exponential_Rate": 45,
"Infectivity_Scale_Type": "EXPONENTIAL_FUNCTION_OF_TIME"
}
|
Infectivity_Exponential_Delay |
float |
0 |
3.40E+38 |
0 |
The number of days before infectivity begins to ramp up exponentially. Infectivity_Scale_Type must be set to EXPONENTIAL_FUNCTION_OF_TIME. |
{
"Infectivity_Exponential_Baseline": 0.1,
"Infectivity_Exponential_Delay": 90,
"Infectivity_Exponential_Rate": 45,
"Infectivity_Scale_Type": "EXPONENTIAL_FUNCTION_OF_TIME"
}
|
Infectivity_Exponential_Rate |
float |
0 |
3.40E+38 |
0 |
The daily rate of exponential growth to approach to full infectivity after the delay set by Infectivity_Exponential_Delay has passed. Infectivity_Scale_Type must be set to EXPONENTIAL_FUNCTION_OF_TIME. |
{
"Infectivity_Exponential_Rate": 45
}
|
Memory_Usage_Halting_Threshold_Working_Set_MB |
integer |
0 |
1.00E+06 |
8000 |
The maximum size (in MB) of working set memory before the system throws an exception and halts. |
{
"Memory_Usage_Halting_Threshold_Working_Set_MB": 4000
}
|
Memory_Usage_Warning_Threshold_Working_Set_MB |
integer |
0 |
1.00E+06 |
7000 |
The maximum size (in MB) of working set memory before memory usage statistics are written to the log regardless of log level. |
{
"Memory_Usage_Warning_Threshold_Working_Set_MB": 3500
}
|
Nighttime_Feeding_Fraction |
float |
0 |
1 |
1 |
The fraction of feeds on humans (for a specific mosquito species) that occur during the nighttime. Thus the fraction of feeds on humans that occur during the day equals 1 - (value of this parameter). |
{
"Vector_Species_Params": {
"arabiensis": {
"Acquire_Modifier": 0.2,
"Adult_Life_Expectancy": 10,
"Anthropophily": 0.95,
"Aquatic_Arrhenius_1": 84200000000,
"Aquatic_Arrhenius_2": 8328,
"Aquatic_Mortality_Rate": 0.1,
"Cycle_Arrhenius_1": 0,
"Cycle_Arrhenius_2": 0,
"Cycle_Arrhenius_Reduction_Factor": 0,
"Days_Between_Feeds": 3,
"Egg_Batch_Size": 100,
"Immature_Duration": 4,
"Indoor_Feeding_Fraction": 0.5,
"Infected_Arrhenius_1": 117000000000,
"Infected_Arrhenius_2": 8336,
"Infected_Egg_Batch_Factor": 0.8,
"Infectious_Human_Feed_Mortality_Factor": 1.5,
"Larval_Habitat_Types": {
"TEMPORARY_RAINFALL": 11250000000
},
"Nighttime_Feeding_Fraction": 1,
"Transmission_Rate": 0.5
}
}
}
|
Serialization_Time_Steps |
array of integers |
0 |
2.15E+09 |
The list of time steps after which to save the serialized state. 0 (zero) indicates the initial state before simulation, n indicates after the nth time step. By default, no serialized state is saved. |
{
"Serialization_Time_Steps": [
0,
10
]
}
|
|
Serialized_Population_Filenames |
array of strings |
NA |
NA |
NA |
Array of filenames with serialized population data. The number of filenames must match the number of cores used for the simulation. The file must be in .dtk format. |
{
"Serialized_Population_Filenames": [
"state-00010.dtk"
]
}
|
Serialized_Population_Path |
string |
NA |
NA |
. |
The root path for the serialized population files. |
{
"Serialized_Population_Path": "../00_Generic_Version_1_save/output"
}
|
Temperature_Dependent_Feeding_Cycle |
enum |
NA |
NA |
NO_TEMPERATURE_DEPENDENCE |
The effect of temperature on the duration between blood feeds. Possible values are:
|
{
"Temperature_Dependent_Feeding_Cycle": "BOUNDED_DEPENDENCE"
}
|
To view all available configuration parameters, see Configuration parameters.
New demographics parameters¶
In all simulation types, you can now assign properties like risk or quality of care to nodes using NodeProperties, which are configured much like IndividualProperties. In addition, a new property type is available for both nodes and individuals called InterventionStatus, which is used by the campaign file to distribute interventions based on the other interventions an individual or node has received. This property type was previously available only for individuals in the HIV simulation type and was known as the CascadeState. The relevant campaign parameters are described in the next section.
For more information, see the table below.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
NodeProperties |
array of objects |
NA |
NA |
NA |
An array that contains parameters that add properties to nodes in a simulation. For example, you can define values for intervention status, risk, and other properties and assign values to different nodes. |
{
"NodeProperties": [{
"Property": "Risk",
"Values": ["HIGH", "MEDIUM", "LOW"],
"Initial_Distribution": [0.1, 0.4, 0.5]
}]
}
|
Property |
enum |
NA |
NA |
NA |
The individual or node property type for which you will assign arbitrary values to create groups. You can then move individuals or nodes into or out of different groups or target interventions to particular groups. Possible values are:
|
{
"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 |
float |
0 |
3.40E+3 |
6 |
The initial amount of stock 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. |
{
"Initial_Amount": 10
}
|
Initial_Amount_Distribution_Type |
enum |
NA |
NA |
NOT_INITIALIZED |
The distribution type to set when initializing Initial_Amount. Possible values are:
|
{
"Initial_Amount_Distribution_Type": "FIXED_DURATION"
}
|
Initial_Amount_Max |
float |
0 |
3.40E+3 |
0 |
The maximum amount of initial stock when Initial_Amount_Distribution_Type is set to UNIFORM_DISTRIBUTION. |
{
"Initial_Amount_Distribution_Type": "UNIFORM_DURATION",
"Initial_Amount_Min": 5,
"Initial_Amount_Max": 10
}
|
Initial_Amount_Mean |
float |
0 |
3.40E+3 |
6 |
The average amount of initial stock when Initial_Amount_Distribution_Type is set to GAUSSIAN_DISTRIBUTION. |
{
"Initial_Amount_Distribution_Type": "GAUSSIAN_DISTRIBUTION",
"Initial_Amount_Std_Dev": 1,
"Initial_Amount_Mean": 5
}
|
Initial_Amount_Min |
float |
0 |
3.40E+3 |
0 |
The minimum amount of initial stock when Initial_Amount_Distribution_Type is set to UNIFORM_DISTRIBUTION. |
{
"Initial_Amount_Distribution_Type": "UNIFORM_DURATION",
"Initial_Amount_Min": 5,
"Initial_Amount_Max": 10
}
|
Initial_Amount_Std_Dev |
float |
0 |
3.40E+3 |
1 |
The standard deviation for the amount of initial stock when Initial_Amount_Distribution_Type is set to GAUSSIAN_DISTRIBUTION. |
{
"Initial_Amount_Distribution_Type": "GAUSSIAN_DISTRIBUTION",
"Initial_Amount_Std_Dev": 1,
"Initial_Amount_Mean": 5
}
|
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. 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"
}
]
}
|
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. 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 |
NA |
NA |
0 |
When set to true (1), the intervention is only distributed to individuals that began the simulation in the node (i.e. those that claim the node as their residence). |
{
"Target_Residents_Only": 1
}
|
Trigger_Condition_List |
array of strings |
NA |
NA |
NoTrigger |
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 |
|---|---|---|---|---|---|---|
Antigen |
integer |
0 |
10 |
0 |
The antigenic ID of the outbreak infection. |
{
"Intervention_Config": {
"Antigen": 0,
"Genome": 0,
"Outbreak_Source": "PrevalenceIncrease",
"class": "OutbreakIndividual"
}
}
|
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 |
-2.15E+0 |
2.15E+0 |
0 |
The genetic ID of the outbreak infection. |
{
"Intervention_Config": {
"Antigen": 0,
"Genome": 0,
"Outbreak_Source": "PrevalenceIncrease",
"class": "OutbreakIndividual",
"Incubation_Period_Override": 0
}
}
|
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 |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
Intervention_Name |
string |
NA |
NA |
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"
}
|
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 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 |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
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+3 |
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_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 |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
Efficacy_Is_Multiplicative |
boolean |
NA |
NA |
1 |
The overall vaccine efficacy when individuals receive more than one vaccine. When set to true (1), the vaccine efficacies are multiplied together; when set to false (0), the efficacies are additive. |
{
"Intervention_Config": {
"class": "SimpleVaccine",
"Cost_To_Consumer": 10,
"Vaccine_Type": "AcquisitionBlocking",
"Vaccine_Take": 1,
"Efficacy_Is_Multiplicative": 0,
"Waning_Config": {
"class": "WaningEffectConstant",
"Initial_Effect": 0.3
}
}
}
|
Intervention_Name |
string |
NA |
NA |
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"
}
|
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
NoTrigger
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
string
NA
NA
NA
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time.
Dont_Allow_Duplicates
boolean
NA
NA
0
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes.
Expiration_Distribution_Type
enum
NA
NA
NOT_INITIALIZED
The type of distribution to use when calculating the time to discard a bed net. Possible values are:
- NOT_INITIALIZED
No distribution set.
- FIXED_DURATION
A constant duration.
- UNIFORM_DURATION
A uniform random draw for the duration.
- GAUSSIAN_DURATION
Duration of the active period is defined by the mean and standard deviation of the Gaussian distribution. Negative values are truncated at zero.
- EXPONENTIAL_DURATION
The active period is the mean of the exponential random draw.
- POISSON_DURATION
The active period is the mean of the random Poisson draw.
- LOG_NORMAL_DURATION
The active period is a log normal distribution defined by a mean and log width.
- BIMODAL_DURATION
The distribution is bimodal, the duration is a fraction of the active period for a specified period of time and equal to the active period otherwise.
- PIECEWISE_CONSTANT
The distribution is specified with a list of years and a matching list of values. The duration at a given year is that specified for the nearest previous year.
- PIECEWISE_LINEAR
The distribution is specified with a list of years and matching list of values. The duration at a given year is a linear interpolation of the specified values.
- WEIBULL_DURATION
The duration is a Weibull distribution with a given scale and shape.
- DUAL_TIMESCALE_DURATION
The duration is two exponential distributions with given means.
Expiration_Percentage_Period_1
float
0
1
0.5
The percentage of draws where the first time-scale is used. Expiration_Distribution_Type must be set to DUAL_TIMESCALE_DURATION.
Expiration_Period
float
0
3.40E+3
6.00E+00
The distribution period for when a bed net expires. Expiration_Distribution_Type must be set to FIXED_DURATION or EXPONENTIAL_DURATION.
Expiration_Period_1
float
0
3.40E+3
6
The decay length of the first time-scale, when Expiration_Distribution_Type is set to DUAL_TIMESCALE_DURATION.
Expiration_Period_2
float
0
3.40E+3
6
The decay length of the second time-scale, when Expiration_Distribution_Type is set to DUAL_TIMESCALE_DURATION.
Expiration_Period_Max
float
0
3.40E+3
0
The maximum duration of use for a bed net when Expiration_Distribution_Type is set to UNIFORM_DURATION.
Expiration_Period_Mean
float
0
3.40E+3
6
The mean of the duration of use for a bed net when Expiration_Distribution_Type is set to GAUSSIAN_DURATION.
Expiration_Period_Min
float
0
3.40E+3
0
The minimum duration of use for a bed net when Expiration_Distribution_Type is set to UNIFORM_DURATION.
Expiration_Period_Std_Dev
float
0
3.40E+3
1
The standard deviation for the duration of use for a bed net when Expiration_Distribution_Type is set to GAUSSIAN_DURATION.
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
NoTrigger
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
NoTrigger
This parameter broadcasts each time step in which a bed net is used. See Event list for possible values.
EMOD v2.10¶
The EMOD v2.10 release includes new and updated malaria configuration, demographic, and intervention parameters. With this release, EMOD now uses Visual Studio 2015, Boost 1.61.0, and SCons 2.5.0.
EMOD software upgrades¶
- Microsoft Visual Studio
EMOD now uses Visual Studio 2015, and Visual Studio 2012 is no longer supported. The Visual Studio solution file in the EMOD source, EradicationKernel, has been updated for Visual Studio 2015. If you have custom reporter EMODules (DLLS) that were built using Visual Studio 2012, you will need to rebuild them with Visual Studio 2015; otherwise, your simulation will crash when it attempts to load the DLLs built by Visual Studio 2012.
- Boost
EMOD now supports using Boost 1.61.0. If you continue to use Boost 1.51.0, you will get the following warning, “Unknown compiler version - please run the configure tests and report the results.”
- Environment variables
To make it easier to use Boost and Python with Visual Studio, IDM paths have been created. These two paths, IDM_BOOST_PATH and IDM_PYTHON_PATH, need to be added to your environment variables by using either the setx command from a command line or the Windows System Properties panel.
- SCons
EMOD was tested using SCons 2.5.0, as it supports Visual Studio 2015. If you do not add the new IDM environment variables for Boost and Python, you will need to modify the Boost and Python paths in the SConstruct file in the EMOD root directory.
- Python
EMOD was tested with Python 2.7.11 and 2.7.12. If you are building the EMOD executable (Eradication.exe) and have an earlier version of Python (for example, 2.7.2), you will see the following warning message on some files, “c:python27includepymath.h(22): warning C4273: ‘round’: inconsistent dll linkage.” Upgrade to Python 2.7.11 or 2.7.12 to get rid of the warning message.
For more information, see EMOD source code installation.
Malaria model¶
Several habitat parameters in the malaria model have been upgraded, creating more flexibility in the model and enabling the user to have more control over habitat customization. These changes allow the model to more accurately capture real-world habitat availability and how it affects different mosquito species.
- LINEAR_SPLINE habitat type
This new option under Larval_Habitat_Types increases model customization by allowing the user to specify an arbitrary functional form (derived from data) for larval habitat availability throughout the year, instead of relying on climatological parameters such as rainfall or temperature. For more information, see Larval habitat parameters.
- LarvalHabitatMultiplier by species
This new feature in demographics allows larval habitat availability to be modified independently for each species sharing a particular habitat type. Prior versions of EMOD applied the same modifier to all species within a shared habitat; this upgrade enables you to apply modifiers to individual species within a habitat. For more information, see NodeAttributes parameters.
- ScaleLarvalHabitat by species
This new intervention enables species-specific effects of habitat interventions within shared habitat types, such that habitat availability is modified on a per-species level.
- Breaking changes
PIECEWISE_MONTHLY has been changed to LINEAR_SPLINE.
Disease modeling with EMOD¶
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.
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. These models form a type of “microscale model,” where they simulate 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 supports different simulation types for various diseases and disease classes; the features present in the generic 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. For more information on the software architecture and inheritance, see Overview of EMOD software.
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.
This section describes the differential equations governing compartmental models and how EMOD can be configured to simulate these kinds of models. This includes how to use an agent-based model to configure heterogeneous populates and disease transmission, how to configure disease outbreaks, and how to configure campaign interventions aimed at controlling them. The figure below demonstrates the main components of the generic EMOD simulation type. Further information on aspects of these components can be found in the following sections.
Network diagram illustrating the generic model and its constituent components.¶
Simulation examples¶
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 EMODScenarios to download a zipped folder containing all required files and see the README for additional guidance for installing prerequisites. These scenarios are referenced throughout this section.
Note
Because the EMOD model is stochastic, your graphs may appear slightly different from those included in the documentation.
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 death 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 and the initial R0= 0 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. The discrete form of the previous equation at each time step can be written as (despite using the S, I, and R terms to represent the different disease states, remember that EMOD does not map precisely to the ODEs representing a compartmental model):
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 \(\lambda\) 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.
Contents
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 EMODScenarios folder. 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 allow recovered individuals 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 EMODScenarios folder. 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 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 infection 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 statue due to loss of immunity.
Contents
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 EMODScenarios folder. 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 EMODScenarios 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 allow recovered individuals 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 EMODScenarios folder. 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 SEIR and SEIRS 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 EMODScenarios 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 EMODScenarios folder. 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 and animal reservoirs¶
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 should 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.
Animal reservoir zoonosis¶
For many zoonotic diseases, there is a background animal reservoir that continuously exposes humans to infection. When this is the case, outbreaks can be introduced from this reservoir and not just by an outbreak event. Zoonotic diseases are of particular interest because typically they have not previously been in the human population, making the whole population susceptible.
The rate of animal-to-human transmission is configured with the parameter Zoonosis_Rate 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 EMODScenarios folder. Review the README files there for more information.
After the initial outbreak, there are no additional imported cases set in the campaign file. However, because of the non-zero zoonosis rate, there are several 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, and more. This heterogeneity can affect the overall course of the disease outbreak and campaign interventions aimed at controlling it.
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 of the most powerful features is the ability to assign properties to nodes or individuals which can then be used to target interventions or move individuals through a health care system. In the generic simulation type, 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. Other disease models capture heterogeneous transmission through more mechanistic parameters that control aspects like parasite density, symptoms, assortative pairing, and more.
EMOD can also simulate human and vector migration, which can be important in the transmission of many diseases. You can assign different characteristics to each geographic node to control how the disease spreads.
For more information on how you can target campaign interventions to individuals or locations based on certain criteria, see Creating campaigns.
Population density and transmission scaling¶
Disease transmission depends on the rate effective contacts as represented by parameter \(\beta_0\). But what happens to the effective contact rate as the size of the population changes? There are two commonly accepted answers: frequency-dependent and density-dependent transmission scaling.
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, \(r\) is the population density and \(r_{50}\) 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 \(r_{50}\) parameter is configured using the configuration parameter Population_Density_C50. For more information, see Infectivity and transmission parameters.
This 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 zipped EMODScenarios folder 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¶
Individual and node properties¶
One of the strengths of an agent-based model, as opposed to a compartmental model governed by ODEs, is that you can introduce heterogeneity in individuals and regions. For example, you can define property values for accessibility, age, geography, risk, and other properties and assign these values to individuals or nodes in the simulation.
These properties are most commonly used to target (or avoid targeting) particular nodes or individuals with interventions. For example, you might want to put individuals into different age bins and then target interventions to individuals in a particular age bin. Another common use is to configure treatment coverage to be higher for nodes that are easy to access and lower for nodes that are difficult to access. For more information on creating campaign interventions, see Creating campaigns.
For 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, and how to move individuals among these values. 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],
"Transitions": [{
"From": "High",
"To": "Medium",
"Type": "At_Age",
"Coverage": 1,
"Probability_Per_Timestep": 0.3,
"Timestep_Restriction": 20,
"Age_In_Years": 5,
"Timesteps_Until_Reversion": 0
}, {
"From": "Medium",
"To": "Low",
"Type": "At_Age",
"Coverage": 1,
"Probability_Per_Timestep": 0.3,
"Timestep_Restriction": 20,
"Age_In_Years": 12,
"Timesteps_Until_Reversion": 0
}]
}, {
"Property": "Place",
"Values": ["Community", "School", "Work", "Vacation"],
"Initial_Distribution": [0.3, 0.2, 0.4, 0.1],
"Transitions": [{
"From": "School",
"To": "Vacation",
"Type": "At_Timestep",
"Coverage": 1,
"Timestep_Restriction": {
"Start": 20
},
"Age_In_Years_Restriction": {},
"Probability_Per_Timestep": 1,
"Timesteps_Until_Reversion": 20
}]
}, {
"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.
Property-based heterogeneous disease transmission¶
Only generic simulations can use Heterogeneous Intra-Node Transmission (HINT) to manually configure heterogeneous disease transmission within each node. All other simulation types have preconfigured solutions for heterogeneous disease transmission based on the biological processes of the disease being modeled. For example, you can set parameters for parasite density, viral load, mosquito bites, and other relevant transmission factors.
See Infectivity and transmission parameters for more information on configuring transmission in this simulation type. Because HINT cannot be used with this simulation type, the parameter Enable_Heterogeneous_Intranode_Transmission in the configuration file must be set to 0 (zero).
Geographic migration¶
Human migration is a important factor in the spread of disease across a geographic region. EMOD represents geography using nodes. Migration occurs when individuals move from one node to another; disease transmission occurs within nodes. 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 (usually one day), each individual has a defined probability of migrating out of their current node to another. If they migrate, they can then acquire or shed an infection in that node. The mode of migration can be local (foot travel), regional (roadway or rail), air, or sea. You can also define different migration patterns, such as one-way or roundtrip. For more detailed information, see Migration parameters.
For vector-borne diseases, you can also include vector migration. Vectors can migrate locally or regionally, and you can include additional rules based on the availability of food or habitat.
You must include a separate migration file for each mode of travel that describes the migration patterns for each node. It lists the migration rate for each node. Migration rate is defined as the fraction of the node’s population that is migrating out of the node per day. Units are per person per day, meaning the number of people migrating per day divided by the total population of the node. For more information on the structure of these files, see Migration files.
The Generic/Zoonosis scenario in the downloadable EMODScenarios folder includes daily migration. Review the README files there for more information.
Creating campaigns¶
You define the initial disease outbreak and interventions used in 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. 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_Restriction_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 EMODScenarios folder. 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 how 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 the 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, only 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 EMODScenarios folder. 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 |
29% |
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 installation¶
Epidemiological MODeling software (EMOD) can be installed on computers running Windows 10, Windows Server 12, and Windows HPC Server 12 (64-bit) or CentOS 7.1 on Azure. For both operating systems, there are relatively few prerequisites required and the EMOD executable (Eradication.exe) or Eradication binary 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 yourself, see EMOD source code installation.
Install EMOD on Windows¶
Follow the steps below to install EMOD on computers running Windows 10, Windows Server 12, and Windows HPC Server 12 (64-bit). This installs the pre-built Eradication.exe and all software necessary to run simulations.
Note
If you want to download and modify the EMOD source code and build the Eradication.exe yourself, see EMOD source code installation.
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.
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.
Download the EMOD executable (Eradication.exe) (not the Eradication binary for CentOS on Azure). See EMOD releases on GitHub. Save to a local drive, such as the desktop.
Warning
Double-clicking Eradication.exe will not run the EMOD software. EMOD must be run 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 they are 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/ to install Python 3.6.
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 check the Add Python 3.6 to PATH checkbox and click Customize installation.
On the Optional Features window, leave all default values selected and click Next. The Python package manager, pip, is used to install other Python packages.
On the Advanced Options window, we recommend that you customize the installation location to “C:Python36”.
You may install Python in another location, but the Python plotting scripts included in the EMODScenarios folder 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
If you need to keep Python 2.7 installed alongside Python 3.6, we recommend that you install
virtualenvandvirtualenvwrapper, tools used to create and work in isolated Python environments. See Pipenv & Virtual Environments for installation instructions.
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 1.14.5 (64-bit) that is compatible with Python 3.6.
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.
Enter the following commands:
pip install python-dateutil pip install pyparsing pip install matplotlib
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.
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 on Azure 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 data files.
Cache the actual data on your local machine:
git lfs fetch
Replace the metadata in the files with the actual contents:
git lfs checkout
Install EMOD on CentOS on Azure¶
Follow the steps below to install EMOD on CentOS 7.1 on Azure environments. This installs the pre- built Eradication binary and all software needed to run simulations. 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 will vary based on the particular distribution you are running, instruction information is not included here.
Note
If you want to download and modify the EMOD source code and build the Eradication binary yourself, see EMOD source code installation.
The Eradication binary is tested and supported on a CentOS 7.1 on Azure virtual machine. 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
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 of installing the EMOD source code and input data files provided by IDM, but these steps will install a pre-built version of the Eradication binary. For information on building the Eradication binary 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 data files)
An Internet connection
Download and run the PrepareLinuxEnvironment.sh script on EMOD releases on GitHub.
Respond to the prompts for information while the script is running. If you choose not to download the EMOD source and input data files, do the following. This example assumes that a directory named IDM is in your home directory and contains the subdirectories EMOD, containing the EMOD source code, and EMOD-InputData, containing the input data files directory.
Set the EMOD_ROOT environment variable to the path to the EMOD source path:
EMOD_ROOT=~/IDM/EMOD
Put Scripts and . in the path:
export PATH=$PATH:.:$EMOD_ROOT/Scripts
Create a symlink from the EMOD directory to InputDataFiles:
ln -s /home/general/IDM/EMOD-InputData $EMOD_ROOT/InputData
If you run simulations using 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 CentOS 7.1 on Azure (not Eradication.exe for Windows). See on EMOD releases on GitHub. Save to a local drive, such as the desktop.
(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.
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.
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.
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 on Azure 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 data files.
Cache the actual data on your local machine:
git lfs fetch
Replace the metadata in the files with the actual contents:
git lfs checkout
Overview of EMOD software¶
The Institute for Disease Modeling (IDM) develops detailed simulations of disease transmission through the use of extensive and complex software modeling. The primary software, 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. We share this modeling software with the research community to advance the understanding of disease dynamics.
EMOD supports the following simulation types for modeling a variety of diseases:
Generic disease (GENERIC_SIM)
Vector-borne diseases (VECTOR_SIM)
Malaria (MALARIA_SIM)
Tuberculosis with HIV coinfection (TBHIV_SIM)
Sexually transmitted infections (STI_SIM)
HIV (HIV_SIM)
The illustration below shows how the simulation types are built upon one another. With a few exceptions, all parameters available 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 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 is a stochastic, agent-based model that simulates the actions and interactions of individuals within geographic areas known as nodes to understand the disease dynamics in a population over time. EMOD can produce statistically significant results over a broad set of parameters and scenarios. Quantitative analysis of the simulated output enables disease eradication efforts to make more data-driven decisions. The IDM research team has published many articles related to modeling, as well as 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 data files¶
Not all files accepted as input for running a simulation are considered “input data files”. Rather, input data files contain the relatively fixed information about a population to model. For example, climate, geography, demographics, and migration data. The other files accepted by the model are the configuration and campaign files.
Configuration file¶
The configuration file contains parameters that control many different aspects of the simulation. EMOD provides hundreds of parameters for you to configure your simulation. For example, configuration parameters can control the following:
The disease or disease class to simulate (simulation type)
The name and location for the input data files and output files
Whether to include births, deaths, and migration
Disease attributes, such as infectivity, transmission, immunity, and mortality
The computing resources to use
Campaign file¶
The campaign file contains parameters that distribute outbreaks and the interventions used to control the spread of disease. For example, campaign parameters can control the following:
Target demographic (age, location, gender, etc.) for interventions
Diagnostic tests to use
The cost and timing of interventions
Running a simulation¶
The EMOD executable (Eradication.exe) accepts the input data, configuration, and campaign files and then simulates the susceptibility and infection of individuals within each geographic node. The simulation type controls the transmission mechanism of the disease. After the simulation is complete, the Eradication.exe produces output reports that describe various aspects of the disease dynamics within the modeled population.
You can run simulations locally or on a multi-core cluster. Because the model is stochastic, you must run simulations multiple times to produce scientifically valid results.
Depending on how you run the simulation, other programs or environments external to EMOD may also output error and logging files.
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.
This section provides a detailed explanation of the EMOD software, each of these files, and how to run simulations.
Input data files¶
The input data files determine the demographics, migration, climate, and other relatively
fixed information about the population within each geographic node. For example, the number of
individuals, geographic data, climate, demographics, and migration data. These files are specified
using the -I argument when running EMOD from the command line. Input data files are in
contrast to the population, geographic, and migration parameters in the configuration file
that control simulation-wide qualities, such as enabling air migration across all nodes in the
simulation.
Generally, input data files are optional. The Institute for Disease Modeling (IDM) provides collections of input data 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.
The demographics files use JSON (JavaScript Object Notation). The other input data files use both a JSON file for metadata and an associated binary file that contains the actual data. Except for the demographics file, you will typically use these input data files in their default state. See Input data files for the configuration parameters that specify input data files to use.
All input 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 data files used in a simulation. See Demographics parameters for more information about NodeID generation.
Demographics¶
Demographics files contain information on the demographics of the population in a geographical region to simulate. For example, the number of individuals and the distribution for age, gender, immunity, risk, and mortality.
These files can include many user-defined parameter values to set individual and node characteristics and will likely require modification.
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.
Migration¶
Migration files describe the rate of migration of individuals out of a geographic node. There are four types of migration files that can be used by EMOD: local migration, regional migration, air migration, and sea migration.
For all types, migration data is contained in a set of two files, a JSON metadata file with header information and a binary data file with the actual migration data. Both files are required to include migration in a simulation. The basic file structure is identical for all types, with the only exception being the number of columns per row allowed to each type.
Climate¶
There are two general types of climate files usable by EMOD: 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.”
For both types, climate data is contained in a set of two files, a JSON metadata file with header information and a binary file that contains the actual climate data. Both files are required to include climate in a simulation.
Load-balancing¶
When running a simulation on a multi-core HPC cluster, you can include a load-balancing file to control how the computing load is distributed across the cluster. The load balancing file is a binary file that allocates the simulation of each geographic node to cores in the cluster. If no file is submitted, EMOD allocates nodes to cores according to a checkerboard pattern.
Demographics files 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.
Unlike other input data files, the demographics file usually requires modification. However, once you have it configured for a given population and location, you will likely reuse it across many different simulation scenarios. Like the configuration and campaign files, it is a simple JSON file without an accompanying binary file.
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.
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.
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 data 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 data files.
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 data 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,
"ImmunityDistributionFlag": 0,
"ImmunityDistribution1": 1,
"ImmunityDistribution2": 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],
"Transitions": [],
"TransmissionMatrix": {
"Route": "Contact",
"Matrix": [
[1.1, 0.3],
[0.3, 5.0]
]
}
},
{
"Property": "Age_Bin",
"Age_Bin_Edges_In_Years": [ 0, 5, 13, -1 ],
"Transitions": [],
"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
}
]
}
There are four types of migration files that can be used by EMOD, namely, local migration, regional migration, air migration and sea migration. For all types, 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. The structure of each of these files is nearly the same for all migration types, with the only exception being the number of columns in the binary file.
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.
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. |
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 data 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. |
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 migration rate 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. The data is laid out in rows and columns, with each row corresponding to a node and the number of columns varying based on the migration type.
The following diagram shows how migration data is laid out in rows, with each row corresponding to a source node and each column corresponding to the destination node.
Migration rate is that 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 example, if a node had 1,000 people and a migration rate of .01, there would be .01 * 1,000 = 10 people migrating out per day, but if the node had 10,000 people in the node and a migration rate of .01, it would .01 * 10,000 = 100 people migrating out per day. You can adjust this rate using configuration parameters for scaling (these begin with x).
The binary file contains a stream of 4-byte unsigned integers that identify the destination node, followed by a stream of 8-byte double floating point values that contain the rate associated with the destination node (per person per day), running from 1 to the total number of nodes.
To use the migration files, you must set Migration_Model in the configuration file to a valid migration type except “NO_MIGRATION”. Each migration types also requires you to set another parameter to enable the particular type of migration selected. There are also additional parameters in the configuration file you can use to scale or otherwise modify the data included in the climate files.
Local migration describes the foot travel movement of people into adjacent nodes. A local migration file is required for simulations that support more than one node. You must also set the Enable_Local_Migration parameter in the configuration file to 1. For each location, the local migration file represents up to eight adjacent destination nodes, with 0 used as the value for unused nodes.
The following diagram shows the format for the local migration binary file data:
Regional migration describes migration that occurs on a road or rail network for a simulation. If a node is not part of the network, the regional migration of individuals to and from that node considers the closest road hub city. A Voronoi tiling based on road hubs is constructed of the region, with each non-hub connected to the hub of its tile. These connections are created when the migration file is constructed. They are not performed at runtime.
A regional migration file is required for simulations that cover a geography that is large enough that road/rail migration is relevant. You must also set the Enable_Regional_Migration parameter in the configuration file to 1. For each location, the regional migration file represents up to 30 adjacent destination nodes, with 0 used as the value for unused nodes.
The following diagram shows the format for the regional migration binary file data:
Air migration describes migration that occurs by airplane travel. An air migration file is usually required for simulations of an entire country or larger geographies. You must also set the Enable_Air_Migration parameter in the configuration file to 1. For each location, the air migration file represents up to 60 adjacent destination nodes, with 0 used as the value for unused nodes.
The following diagram shows the format for the air migration binary file data:
Sea migration describes migration that occurs by ship travel. Unlike the other migration files, the sea migration file only contains information for the nodes that are seaports. A sea migration file does not contain every node like local, regional or air migration files do. You must also set the Enable_Sea_Migration parameter in the configuration file to 1. For each location, the sea migration file represents up to five adjacent destination nodes, with 0 used as the value for unused nodes.
The following diagram shows the format for the sea migration binary file data:
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 data 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 data 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 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:
Configuration file¶
The primary means of configuring the disease simulation is the configuration file. This required file is a JSON (JavaScript Object Notation) formatted file that is typically named config.json. The configuration file controls many different aspects of the simulation. For example,
The names of the campaign file and input data files to use
Simulation-wide demographics, climate, and migration data
General disease attributes such as infectivity, immunity, mortality, and so on
Attributes specific to the disease type being modeled, including treatment efficacy
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. The EMOD Regression directory contains many different subdirectories that contain configuration, campaign, and other associated files to run simulations. Within the each subdirectory, there is usually a hierarchical overlay file (param_overrides.json) and a flattened file (config.json), which has been created by combining param_overrides.json with one of the default files in Regression/defaults. The default files are also organized hierarchically. The naming of these files is an arbitrary convention used at IDM; you may name this files anything you choose.
The simplest method of working with the configuration files is to use a text editor to directly edit the parameters or parameter values in the JSON file. However, you may want to use Python or another scripting language to make large modifications. See Example scripts to modify a configuration file for more information.
For a complete list of configuration parameters that are available to use with this simulation type, see Configuration parameters. For more information about JSON, see EMOD parameter reference.
Flattened configuration files¶
A flattened configuration file is generally a single-depth JSON file with configuration parameters listed alphabetically. This is the configuration file format that Eradication.exe and Eradication binary both require for running simulations.
However, there may be some hierarchical elements in the flattened version. For example, Vector_Species_Params and Malaria_Drug_Params have nested JSON objects.
Below is an example of a flattened configuration file:
{
"parameters": {
"Age_Initialization_Distribution_Type": "DISTRIBUTION_SIMPLE",
"Animal_Reservoir_Type": "NO_ZOONOSIS",
"Base_Incubation_Period": 3,
"Base_Infectious_Period": 7,
"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_Gender": 0,
"Enable_Demographics_Reporting": 0,
"Enable_Demographics_Risk": 0,
"Enable_Disease_Mortality": 0,
"Enable_Heterogeneous_Intranode_Transmission": 0,
"Enable_Immunity": 0,
"Enable_Immunity_Distribution": 0,
"Enable_Initial_Prevalence": 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_Distribution": "FIXED_DURATION",
"Individual_Sampling_Type": "TRACK_ALL",
"Infection_Updates_Per_Timestep": 1,
"Infectious_Period_Distribution": "EXPONENTIAL_DURATION",
"Infectivity_Scale_Type": "CONSTANT_INFECTIVITY",
"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
}
}
Hierarchical configuration files¶
A hierarchical file allows you to organize parameters into logical groups by nesting them within JSON objects, making them easier to manage. If you use hierarchical configuration files, you must flatten them prior to running a simulation. The names you use to label these logical categories are unimportant; the scripts used to flatten the files will search through the hierarchies and retain only the “leaf” values in the resulting flattened file. See Configuration overlay files for more information on flattening files.
Below is an example of a hierarchical configuration file:
{
"parameters": {
"CAMPAIGNS": {
"Campaign_Filename": "campaign.json",
"Enable_Interventions": 1,
"Listed_Events": [],
"PKPD_Model": "FIXED_DURATION_CONSTANT_EFFECT"
},
"CLIMATE": {
"Climate_Model": "CLIMATE_OFF"
},
"DEMOGRAPHICS": {
"Age_Initialization_Distribution_Type": "DISTRIBUTION_SIMPLE",
"Base_Population_Scale_Factor": 1,
"Birth_Rate_Dependence": "DEMOGRAPHIC_DEP_RATE",
"Birth_Rate_Time_Dependence": "NONE",
"Demographics_Filenames": ["NO_DEFAULT_DEMOGRAPHICS"],
"Default_Geography_Initial_Node_Population": 1000,
"Default_Geography_Torus_Size": 10,
"Enable_Aging": 1,
"Enable_Birth": 1,
"Enable_Demographics_Birth": 0,
"Enable_Demographics_Gender": 1,
"Enable_Demographics_Builtin": 0,
"Enable_Demographics_Risk": 0,
"Enable_Demographics_Reporting": 0,
"Enable_Natural_Mortality": 1,
"Enable_Vital_Dynamics": 1,
"Minimum_Adult_Age_Years": 15,
"IMMUNITY": {
"Acquisition_Blocking_Immunity_Decay_Rate": 0.1,
"Acquisition_Blocking_Immunity_Duration_Before_Decay": 60,
"Enable_Immune_Decay": 1,
"Enable_Immunity": 1,
"Post_Infection_Acquisition_Multiplier": 0,
"Post_Infection_Transmission_Multiplier": 0,
"Immunity_Initialization_Distribution_Type": "DISTRIBUTION_OFF",
"Susceptibility_Scaling_Type": "CONSTANT_SUSCEPTIBILITY",
"Transmission_Blocking_Immunity_Decay_Rate": 0.1,
"Transmission_Blocking_Immunity_Duration_Before_Decay": 60
},
"MORTALITY": {
"Base_Mortality": 0,
"Enable_Disease_Mortality": 0,
"Death_Rate_Dependence": "NONDISEASE_MORTALITY_BY_AGE_AND_GENDER",
"Post_Infection_Mortality_Multiplier": 0,
"Mortality_Blocking_Immunity_Decay_Rate": 0.001,
"Mortality_Blocking_Immunity_Duration_Before_Decay": 60,
"Mortality_Time_Course": "DAILY_MORTALITY"
},
"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
}
},
"DISEASE": {
"Animal_Reservoir_Type": "NO_ZOONOSIS",
"Enable_Superinfection": 0,
"INCUBATION": {
"Base_Incubation_Period": 3,
"Incubation_Period_Distribution": "FIXED_DURATION"
},
"INFECTIOUSNESS": {
"Base_Infectious_Period": 7,
"Base_Infectivity": 0.3,
"Infectious_Period_Distribution": "EXPONENTIAL_DURATION",
"Infectivity_Scale_Type": "CONSTANT_INFECTIVITY",
"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_Population_Immunity": 1,
"x_Regional_Migration": 1,
"x_Sea_Migration": 1,
"x_Temporary_Larval_Habitat": 1
},
"HPC": {
"Job_Node_Groups": "Chassis08",
"Job_Priority": "BELOWNORMAL",
"Load_Balance_Filename": "",
"Local_Simulation": 0
},
"INTRANODE_TRANSMISSION": {
"Enable_Default_Shedding_Function": 1,
"Enable_Heterogeneous_Intranode_Transmission": 0
},
"MIGRATION": {
"Migration_Model": "NO_MIGRATION"
},
"OUTPUT": {
"Custom_Reports_Filename": "NoCustomReports",
"Report_Event_Recorder": 0,
"Enable_Default_Reporting": 1,
"Enable_Property_Output": 0,
"Enable_Spatial_Output": 0
},
"POLIO": {},
"PRIMARY": {
"Config_Name": "DEFAULT_CONFIG_NAME_SHOULD_BE_SET",
"ENUMS": {
"Simulation_Type": "GENERIC_SIM"
},
"Geography": "DEFAULT_GEOGRAPHY_SHOULD_BE_SET",
"Node_Grid_Size": 0.042,
"Run_Number": 0,
"Simulation_Duration": 365,
"Simulation_Timestep": 1,
"Start_Time": 0,
"Enable_Absolute_Time": "NO"
},
"SERIALIZATION": {
"Burnin_Cache_Mode": "none",
"Burnin_Cache_Period": 0,
"Burnin_Name": "",
"Serialization_Test_Cycles": 0
},
"STRAIN_TRACKING": {
"Number_Basestrains": 1,
"Number_Substrains": 1
}
}
}
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.
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, configuration files will be flattened as part of those tests. However, this may take several hours if run locally. See Regression testing for more information.
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", "Base_Population_Scale_Factor": 1, "Birth_Rate_Dependence": "DEMOGRAPHIC_DEP_RATE", "Birth_Rate_Time_Dependence": "NONE", "Demographics_Filenames": ["NO_DEFAULT_DEMOGRAPHICS"], "Default_Geography_Initial_Node_Population": 1000, "Default_Geography_Torus_Size": 10, "Enable_Aging": 1, "Enable_Birth": 1, "Enable_Demographics_Birth": 0, "Enable_Demographics_Gender": 1, "Enable_Demographics_Builtin": 0, "Enable_Demographics_Risk": 0, "Enable_Demographics_Reporting": 0, "Enable_Natural_Mortality": 1, "Enable_Vital_Dynamics": 1, "Minimum_Adult_Age_Years": 15, "IMMUNITY": { "Acquisition_Blocking_Immunity_Decay_Rate": 0.1, "Acquisition_Blocking_Immunity_Duration_Before_Decay": 60, "Enable_Immune_Decay": 1, "Enable_Immunity": 1, "Post_Infection_Acquisition_Multiplier": 0, "Post_Infection_Transmission_Multiplier": 0, "Immunity_Initialization_Distribution_Type": "DISTRIBUTION_OFF", "Susceptibility_Scaling_Type": "CONSTANT_SUSCEPTIBILITY", "Transmission_Blocking_Immunity_Decay_Rate": 0.1, "Transmission_Blocking_Immunity_Duration_Before_Decay": 60 }, "MORTALITY": { "Base_Mortality": 0, "Enable_Disease_Mortality": 0, "Death_Rate_Dependence": "NONDISEASE_MORTALITY_BY_AGE_AND_GENDER", "Post_Infection_Mortality_Multiplier": 0, "Mortality_Blocking_Immunity_Decay_Rate": 0.001, "Mortality_Blocking_Immunity_Duration_Before_Decay": 60, "Mortality_Time_Course": "DAILY_MORTALITY" }, "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 } }, "DISEASE": { "Animal_Reservoir_Type": "NO_ZOONOSIS", "Enable_Superinfection": 0, "INCUBATION": { "Base_Incubation_Period": 3, "Incubation_Period_Distribution": "FIXED_DURATION" }, "INFECTIOUSNESS": { "Base_Infectious_Period": 7, "Base_Infectivity": 0.3, "Infectious_Period_Distribution": "EXPONENTIAL_DURATION", "Infectivity_Scale_Type": "CONSTANT_INFECTIVITY", "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_Population_Immunity": 1, "x_Regional_Migration": 1, "x_Sea_Migration": 1, "x_Temporary_Larval_Habitat": 1 }, "HPC": { "Job_Node_Groups": "Chassis08", "Job_Priority": "BELOWNORMAL", "Load_Balance_Filename": "", "Local_Simulation": 0 }, "INTRANODE_TRANSMISSION": { "Enable_Default_Shedding_Function": 1, "Enable_Heterogeneous_Intranode_Transmission": 0 }, "MIGRATION": { "Migration_Model": "NO_MIGRATION" }, "OUTPUT": { "Custom_Reports_Filename": "NoCustomReports", "Report_Event_Recorder": 0, "Enable_Default_Reporting": 1, "Enable_Property_Output": 0, "Enable_Spatial_Output": 0 }, "POLIO": {}, "PRIMARY": { "Config_Name": "DEFAULT_CONFIG_NAME_SHOULD_BE_SET", "ENUMS": { "Simulation_Type": "GENERIC_SIM" }, "Geography": "DEFAULT_GEOGRAPHY_SHOULD_BE_SET", "Node_Grid_Size": 0.042, "Run_Number": 0, "Simulation_Duration": 365, "Simulation_Timestep": 1, "Start_Time": 0, "Enable_Absolute_Time": "NO" }, "SERIALIZATION": { "Burnin_Cache_Mode": "none", "Burnin_Cache_Period": 0, "Burnin_Name": "", "Serialization_Test_Cycles": 0 }, "STRAIN_TRACKING": { "Number_Basestrains": 1, "Number_Substrains": 1 } } }
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/param_overlay.json
Open the resulting config.json file in the same folder as param_overlay.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", "Animal_Reservoir_Type": "NO_ZOONOSIS", "Base_Incubation_Period": 3, "Base_Infectious_Period": 7, "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_Gender": 0, "Enable_Demographics_Reporting": 0, "Enable_Demographics_Risk": 0, "Enable_Disease_Mortality": 0, "Enable_Heterogeneous_Intranode_Transmission": 0, "Enable_Immunity": 0, "Enable_Immunity_Distribution": 0, "Enable_Initial_Prevalence": 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_Distribution": "FIXED_DURATION", "Individual_Sampling_Type": "TRACK_ALL", "Infection_Updates_Per_Timestep": 1, "Infectious_Period_Distribution": "EXPONENTIAL_DURATION", "Infectivity_Scale_Type": "CONSTANT_INFECTIVITY", "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 } }
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)
Campaign file¶
The campaign file is an optional file that distributes outbreaks and contains all parameters that define the collection of interventions that make up a disease eradication campaign. For example,
When, how, and who to test for a disease
When, how, and who to distribute treatments to
The vaccines or other preventative interventions to use
The medications to use for treatment
Like the configuration file, the campaign file is a JSON (JavaScript Object Notation) formatted file. It is hierarchically organized into logical groups of parameters that can have arbitrary levels of nesting. For some interventions, there can be a very complex hierarchical structure, including recursion. 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:
- campaign event
A JSON object that determines when and where an intervention is distributed during a campaign.
- event coordinator
A JSON object that determines who will receive a particular intervention during a campaign.
- intervention
A JSON object that determines what will be distributed to reduce the spread of a disease. An intervention can be distributed either to an individual (such as a vaccine, drug, or bednet) or to a node (such as a larvicide). Sometimes this can be an intermediate intervention that schedules another intervention.
IDM provides complete simulation scenarios in the Regression directory on GitHub. Within each of the simulation subdirectories, there is a campaign.json file.
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.
Campaign overlay files¶
You can use two campaign files when setting up a simulation. One file contains default campaign settings and an overlay file contains additional parameters or different parameter values that override the values in the default file. The files must be flattened into a single file before running a simulation.
Overlay files allow you to easily separate a subset of parameters that are of particular interest from the rest of the parameters needed to run a simulation. You can easily modify the parameters in the overlay file without needing to maintain a complete campaign file. This can be especially helpful when you want to experiment with different interventions without modifying the rest of the settings. You can have one default file and many different overlay files for different intervention settings. It also allows you to easily update the default values across multiple simulations.
In addition to being used for model experimentation, overlay files are used when testing the software functionality after making source code changes. If you run the EMOD regression tests using regression_test.py, campaign files will be flattened as part of those tests. However, this may take several hours if run locally. More guidance on modifying the EMOD source code is in the “Advance the model” section.
The EMOD Regression directory contains many different subdirectories that contain configuration, campaign, and other associated files to run simulations used in regression testing. Some subdirectories include a campaign overlay file (campaign_overrides.json), which has been created by combining campaign_overrides.json with one of the default files in Regression/defaults. The naming of these files is an arbitrary convention used at IDM; you may name this files anything you choose. However, it may be useful to see some examples of these files to understand how they are used.
To flatten campaign files:
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).
Running a simulation¶
The EMOD executable (Eradication.exe) consumes the input data files, configuration file, and, optionally, campaign file to run simulations that model disease dynamics and campaign efficacy. 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.
Run a single simulation¶
You will generally want to run simulations 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.
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.
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 input data 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.
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 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 on CentOS on Azure.
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 |
|---|---|---|
|
|
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 input data files. If not specified, EMOD will look for files in the current directory. The specific input data 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 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. |
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 Base_Population_Scale_Factor
Alternatively, you can simply reduce the total population of the simulation using Population_Scale_Type set to “FIXED_SCALING” and Base_Population_Scale_Factor 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 file structure for more information.
- Simulation_Type
“VECTOR_SIM” will run much faster than “MALARIA_SIM” if detailed intra-host dynamics are not “required.
- Vector_Sampling_Type and Mosquito_Weight
The individual vector simulation (“TRACK_ALL_VECTORS”) will run more slowly than the cohort model (“VECTOR_COMPARTMENTS_NUMBER”). If the individual vector model must be used, you can improve performance by sampling some fraction of all vectors (“SAMPLE_IND_VECTORS”) where each mosquito has a weight configured by Mosquito_Weight, which is analogous to individual human sampling.
- Infection_Updates_Per_Timestep
Using fewer intra-day updates to the malaria intra-host model will speed up simulation time. However, updates longer than three hours, such as eight per day, result in increasingly inaccurate dynamics.
Output files¶
After running a simulation, the simulation data is extracted, aggregated, and saved as an output report to the output directory in the working directory. Depending on your configuration, one or more output reports will be created, each of which summarize different data from the simulation. Output reports can be in JSON, CSV, or binary file formats. EMOD also creates logging or error output files.
The EMOD functionality that produces an output report is known as a reporter. EMOD provides several built-in reporters for outputting data from simulations. By default, EMOD will always generate the report InsetChart.json, which contains the simulation-wide average disease prevalence by time step. If none of the provided reports generates the output report that you require, you can create a custom reporter.
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- timestep values accumulated over the simulation in a variety of reporting channels, for example, “New Infections”, “Adult Vectors”, and “Parasite Prevalence”. 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 a 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.
Use Python to parse data¶
The example below uses the Python package JSON to parse the file and the Python package matplotlib.pyplot to plot the output. This is a very simple example and not likely the most robust or elegant. Be sure to set the actual path to your working directory.
import os
import json
import matplotlib.pyplot as plt
# open and parse InsetChart.json
ic_json = json.loads( open( os.path.join( WorkingDirectoryLocation, "output", "InsetChart.json" ) ).read() )
ic_json_allchannels = ic_json["Channels"]
ic_json_birthdata = ic_json["Channels"]["Births"]
# plot "Births" channel by time step
plt.plot( ic_json_birthdata[ "Data" ], 'b-' )
plt.title( "Births" )
plt.show()
Use MATLAB to parse data¶
The example below uses the MATLAB toolbox JSONlab to parse an InsetChart.json file and plot one channel. This script uses JSONLab to parse the file into a usable form in MATLAB. This is a very simple example and not likely the most robust or elegant. Be sure to set the actual paths to JSONlab and your working directory.
% this sample uses JSONLab toolbox
addpath('PATH TO/jsonlab');
% open and parse InsetChart.json
ic_json = loadjson( fullfile( 'WorkingDirectoryLocation', 'output', 'InsetChart.json' ));
ic_json_allchannels = ic_json.Channels;
ic_json_birthinfo = ic_json_allchannels.Births;
ic_json_birthdata = ic_json_birthinfo.Data;
M = num2cell(ic_json_birthdata);
% plot "Births" channel by time step
plot(cell2mat(M));
title( 'Births' );
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 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 InsetChart.json file.
{
"Header": {
"DateTime": "Thu Dec 03 11:28:34 2015",
"DTK_Version": "5738 HIV-Ongoing 2015/11/18 13:00:39",
"Report_Type": "InsetChart",
"Report_Version": "3.2",
"Start_Time": 0,
"Simulation_Timestep": 1,
"Timesteps": 5,
"Channels": 17
},
"Channels": {
"Births": {
"Units": "Births",
"Data": [
0,
0,
0,
0,
0
]
},
"Campaign Cost": {
"Units": "USD",
"Data": [
0,
0,
0,
0,
0
]
},
"Cumulative Infections": {
"Units": "",
"Data": [
0,
0,
0,
0,
0
]
},
"Cumulative Reported Infections": {
"Units": "",
"Data": [
0,
0,
0,
0,
0
]
},
"Disease Deaths": {
"Units": "",
"Data": [
0,
0,
0,
0,
0
]
},
"Exposed Population": {
"Units": "Exposed Fraction",
"Data": [
0,
0,
0,
0,
0
]
},
"Human Infectious Reservoir": {
"Units": "Total Infectivity",
"Data": [
0,
0,
0,
0,
0
]
},
"Infected": {
"Units": "Infected Fraction",
"Data": [
0,
0,
0,
0,
0
]
},
"Infectious Population": {
"Units": "Infectious Fraction",
"Data": [
0,
0,
0,
0,
0
]
},
"Log Prevalence": {
"Units": "Log Prevalence",
"Data": [-10, -10, -10, -10, -10]
},
"New Infections": {
"Units": "",
"Data": [
0,
0,
0,
0,
0
]
},
"New Reported Infections": {
"Units": "",
"Data": [
0,
0,
0,
0,
0
]
},
"Daily (Human) Infection Rate": {
"Units": "Infection Rate",
"Data": [
0,
0,
0,
0,
0
]
},
"Recovered Population": {
"Units": "Recovered (Immune) Fraction",
"Data": [
0,
0,
0,
0,
0
]
},
"Statistical Population": {
"Units": "Population",
"Data": [
900,
900,
900,
900,
900
]
},
"Susceptible Population": {
"Units": "Susceptible Fraction",
"Data": [
1,
1,
1,
1,
1
]
},
"Waning Population": {
"Units": "Waning Immunity Fraction",
"Data": [
0,
0,
0,
0,
0
]
}
}
}
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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},
"New Infections": {
"Units": "",
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]
},
"Population": {
"Units": "",
"Data": [
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[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 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 on Azure. 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.
2 of 50 steps complete.
3 of 50 steps complete.
4 of 50 steps complete.
5 of 50 steps complete.
6 of 50 steps complete.
7 of 50 steps complete.
8 of 50 steps complete.
9 of 50 steps complete.
10 of 50 steps complete.
11 of 50 steps complete.
12 of 50 steps complete.
13 of 50 steps complete.
14 of 50 steps complete.
15 of 50 steps complete.
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42 of 50 steps complete.
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45 of 50 steps complete.
46 of 50 steps complete.
47 of 50 steps complete.
48 of 50 steps complete.
49 of 50 steps complete.
50 of 50 steps complete.
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:
C:\EMOD\CampaignTesting\00_DefaultDemographics>..\Eradication.exe -C config.json -I ../input
Intellectual Ventures(R)/EMOD Disease Transmission Kernel 2.8.1331.0
Built on Sep 30 2016 08:39:43 by cwiswell from master (482fdae) checked in on 2016-09-28 14:47:20 -0700
Supports sim_types: GENERIC, VECTOR, MALARIA, AIRBORNE, POLIO, TB, TBHIV, STI, HIV, PY.
Using config file: config.json
Using input path: ../input
Using output path: output
using dll path:
--python-script-path (-P) not on command line - not using embedded python
Initializing environment...
Log-levels:
Default -> INFO
Eradication -> INFO
00:00:00 [0] [I] [Eradication] Loaded Configuration...
00:00:00 [0] [I] [Eradication] 99 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] [Controller] DefaultController::execute_internal() populate simulation...
00:00:00 [0] [I] [Simulation] Campaign file name identified as: campaign.json
00:00:00 [0] [I] [NodeDemographics] Using default 3x3 torus geography
00:00:00 [0] [I] [Climate] Initialize
00:00:00 [0] [I] [LoadBalanceScheme] Using Checkerboard Load Balance Scheme.
00:00:01 [0] [I] [Simulation] Looking for campaign file campaign.json
00:00:01 [0] [I] [Simulation] Found campaign file successfully.
00:00:01 [0] [I] [DllLoader] ReadEmodulesJson: no file, returning.
00:00:01 [0] [I] [DllLoader] dllPath not passed in, getting from EnvPtr
00:00:01 [0] [I] [DllLoader] Trying to copy from string to wstring.
00:00:01 [0] [I] [DllLoader] DLL ws root path:
00:00:01 [0] [W] [Simulation] 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 ( "Intervention_Name" : "struct Kernel::BaseIntervention" ) for unspecified parameter.
00:00:01 [0] [I] [JsonConfigurable] Using the default value ( "Intervention_Name" : "struct Kernel::BaseIntervention" ) for unspecified parameter.
00:00:01 [0] [I] [JsonConfigurable] Using the default value ( "Intervention_Name" : "struct Kernel::BaseIntervention" ) for unspecified parameter.
00:00:01 [0] [I] [Simulation] populateFromDemographics() created 9 nodes
00:00:01 [0] [I] [Simulation] populateFromDemographics() generated 9 nodes.
00:00:01 [0] [I] [Simulation] Rank 0 contributes 9 nodes...
00:00:01 [0] [I] [Simulation] Merging node rank maps...
00:00:01 [0] [I] [Simulation] Merged rank 0 map now has 9 nodes.
00:00:01 [0] [I] [Simulation] Rank 0 map contents:
{ NodeRankMap:
[1,000000000C10A960]
[2,000000000BF4EAF0]
[3,000000000BFA2250]
[4,000000000BFF1C10]
[5,000000000BE37B60]
[6,000000000BE87380]
[7,000000000BEE74F0]
[8,000000000B721980]
[9,000000000B7AE280]
}
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: 900 Infected: 0
00:00:01 [0] [I] [SimulationEventContext] Time for campaign event. Calling Dispatch...
00:00:01 [0] [I] [SimulationEventContext] 9 node(s) visited.
00:00:01 [0] [I] [JsonConfigurable] Using the default value ( "Intervention_Name" : "struct Kernel::BaseIntervention" ) for unspecified parameter.
00:00:01 [0] [I] [JsonConfigurable] Using the default value ( "Intervention_Name" : "struct Kernel::BaseIntervention" ) for unspecified parameter.
00:00:01 [0] [I] [StandardEventCoordinator] UpdateNodes() gave out 0 'OutbreakIndividual' interventions at node 1
00:00:01 [0] [I] [StandardEventCoordinator] UpdateNodes() gave out 2 'OutbreakIndividual' interventions at node 2
00:00:01 [0] [I] [StandardEventCoordinator] UpdateNodes() gave out 0 'OutbreakIndividual' interventions at node 3
00:00:01 [0] [I] [StandardEventCoordinator] UpdateNodes() gave out 0 'OutbreakIndividual' interventions at node 4
00:00:01 [0] [I] [StandardEventCoordinator] UpdateNodes() gave out 0 'OutbreakIndividual' interventions at node 5
00:00:01 [0] [I] [StandardEventCoordinator] UpdateNodes() gave out 0 'OutbreakIndividual' interventions at node 6
00:00:01 [0] [I] [StandardEventCoordinator] UpdateNodes() gave out 0 'OutbreakIndividual' interventions at node 7
00:00:01 [0] [I] [StandardEventCoordinator] UpdateNodes() gave out 1 'OutbreakIndividual' interventions at node 8
00:00:01 [0] [I] [StandardEventCoordinator] UpdateNodes() gave out 1 'OutbreakIndividual' interventions at node 9
00:00:01 [0] [I] [Simulation] Update(): Time: 2.0 Rank: 0 StatPop: 900 Infected: 4
00:00:01 [0] [I] [Simulation] Update(): Time: 3.0 Rank: 0 StatPop: 900 Infected: 18
00:00:01 [0] [I] [Simulation] Update(): Time: 4.0 Rank: 0 StatPop: 900 Infected: 63
00:00:01 [0] [I] [Simulation] Update(): Time: 5.0 Rank: 0 StatPop: 900 Infected: 161
00:00:01 [0] [I] [Simulation] Update(): Time: 6.0 Rank: 0 StatPop: 900 Infected: 232
00:00:01 [0] [I] [Simulation] Update(): Time: 7.0 Rank: 0 StatPop: 900 Infected: 203
00:00:01 [0] [I] [Simulation] Update(): Time: 8.0 Rank: 0 StatPop: 900 Infected: 165
00:00:01 [0] [I] [Simulation] Update(): Time: 9.0 Rank: 0 StatPop: 900 Infected: 132
00:00:01 [0] [I] [Simulation] Update(): Time: 10.0 Rank: 0 StatPop: 900 Infected: 110
00:00:01 [0] [I] [Simulation] Update(): Time: 11.0 Rank: 0 StatPop: 900 Infected: 83
00:00:01 [0] [I] [Simulation] Update(): Time: 12.0 Rank: 0 StatPop: 900 Infected: 69
00:00:01 [0] [I] [Simulation] Update(): Time: 13.0 Rank: 0 StatPop: 900 Infected: 54
00:00:01 [0] [I] [Simulation] Update(): Time: 14.0 Rank: 0 StatPop: 900 Infected: 40
00:00:01 [0] [I] [Simulation] Update(): Time: 15.0 Rank: 0 StatPop: 900 Infected: 30
00:00:01 [0] [I] [Simulation] Update(): Time: 16.0 Rank: 0 StatPop: 900 Infected: 22
00:00:01 [0] [I] [Simulation] Update(): Time: 17.0 Rank: 0 StatPop: 900 Infected: 17
00:00:01 [0] [I] [Simulation] Update(): Time: 18.0 Rank: 0 StatPop: 900 Infected: 14
00:00:01 [0] [I] [Simulation] Update(): Time: 19.0 Rank: 0 StatPop: 900 Infected: 12
00:00:01 [0] [I] [Simulation] Update(): Time: 20.0 Rank: 0 StatPop: 900 Infected: 9
00:00:01 [0] [I] [Simulation] Update(): Time: 21.0 Rank: 0 StatPop: 900 Infected: 7
00:00:01 [0] [I] [Simulation] Update(): Time: 22.0 Rank: 0 StatPop: 900 Infected: 7
00:00:01 [0] [I] [Simulation] Update(): Time: 23.0 Rank: 0 StatPop: 900 Infected: 7
00:00:01 [0] [I] [Simulation] Update(): Time: 24.0 Rank: 0 StatPop: 900 Infected: 5
00:00:01 [0] [I] [Simulation] Update(): Time: 25.0 Rank: 0 StatPop: 900 Infected: 3
00:00:01 [0] [I] [Simulation] Update(): Time: 26.0 Rank: 0 StatPop: 900 Infected: 3
00:00:01 [0] [I] [Simulation] Update(): Time: 27.0 Rank: 0 StatPop: 900 Infected: 1
00:00:02 [0] [I] [Simulation] Update(): Time: 28.0 Rank: 0 StatPop: 900 Infected: 1
00:00:02 [0] [I] [Simulation] Update(): Time: 29.0 Rank: 0 StatPop: 900 Infected: 1
00:00:02 [0] [I] [Simulation] Update(): Time: 30.0 Rank: 0 StatPop: 900 Infected: 1
00:00:02 [0] [I] [Simulation] Update(): Time: 31.0 Rank: 0 StatPop: 900 Infected: 1
00:00:02 [0] [I] [Simulation] Update(): Time: 32.0 Rank: 0 StatPop: 900 Infected: 1
00:00:02 [0] [I] [Simulation] Update(): Time: 33.0 Rank: 0 StatPop: 900 Infected: 1
00:00:02 [0] [I] [Simulation] Update(): Time: 34.0 Rank: 0 StatPop: 900 Infected: 1
00:00:02 [0] [I] [Simulation] Update(): Time: 35.0 Rank: 0 StatPop: 900 Infected: 1
00:00:02 [0] [I] [Simulation] Update(): Time: 36.0 Rank: 0 StatPop: 900 Infected: 1
00:00:02 [0] [I] [Simulation] Update(): Time: 37.0 Rank: 0 StatPop: 900 Infected: 1
00:00:02 [0] [I] [Simulation] Update(): Time: 38.0 Rank: 0 StatPop: 900 Infected: 1
00:00:02 [0] [I] [Simulation] Update(): Time: 39.0 Rank: 0 StatPop: 900 Infected: 1
00:00:02 [0] [I] [Simulation] Update(): Time: 40.0 Rank: 0 StatPop: 900 Infected: 1
00:00:02 [0] [I] [Simulation] Update(): Time: 41.0 Rank: 0 StatPop: 900 Infected: 1
00:00:02 [0] [I] [Simulation] Update(): Time: 42.0 Rank: 0 StatPop: 900 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 43.0 Rank: 0 StatPop: 900 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 44.0 Rank: 0 StatPop: 900 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 45.0 Rank: 0 StatPop: 900 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 46.0 Rank: 0 StatPop: 900 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 47.0 Rank: 0 StatPop: 900 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 48.0 Rank: 0 StatPop: 900 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 49.0 Rank: 0 StatPop: 900 Infected: 0
00:00:02 [0] [I] [Simulation] Update(): Time: 50.0 Rank: 0 StatPop: 900 Infected: 0
00:00:02 [0] [I] [Simulation] Finalizing 'InsetChart.json' reporter.
00:00:02 [0] [I] [Simulation] Finalizing 'BinnedReport.json' reporter.
00:00:02 [0] [I] [Simulation] Finalizing 'DemographicsSummary.json' reporter.
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.
The vector species output report is a JSON-formatted file where the channel data has been sorted into bins. It is identical to the Binned output report, with the exception that the bins are based on vector species. The vector species report is generated for all malaria or vector simulations that contain values in the Vector_Species_Names configuration parameter. For example, if Vector_Species_Names was set to [“funestus”, “gambiae”], you would be able to see how many infectious vectors there were for A. funestus and A. gambiae. The file name is VectorSpeciesReport.json.
The following is a sample of an VectorSpeciesReport.json file.
{
"Header": {
"DateTime": "Thu Jul 23 13:06:54 2015",
"DTK_Version": "5004 trunk Jun 25 2015",
"Report_Version": "2.1",
"Timesteps": 100,
"Subchannel_Metadata": {
"AxisLabels": [
["Vector Species"]
],
"NumBinsPerAxis": [
[1]
],
"ValuesPerAxis": [
[
[0]
]
],
"MeaningPerAxis": [
[
["arabiensis"]
]
]
},
"Channels": 4
},
"Channels": {
"Adult Vectors": {
"Units": "",
"Data": [
[8739, 7627, 6712, 5882, 5174, 4577, 4003, 3500, 3086, 2691, 2340, 2038, 1790, 1562, 1367, 1191, 1045, 924, 801, 704, 620, 540, 472, 412, 353, 314, 1188, 2198, 2152, 3738, 3706, 3918, 6105, 6197, 6897, 6632, 6656, 6858, 6748, 6390, 6300, 5540, 5284, 5230, 5744, 5487, 5689, 5563, 5935, 6077, 6091, 6123, 5970, 5879, 5956, 6220, 6315, 6361, 6144, 5943, 5193, 6508, 6692, 8009, 8139, 7800, 8575, 8431, 8126, 9099, 8682, 8691, 8499, 8901, 8603, 8359, 8135, 8068, 7890, 6884, 6667, 7782, 6809, 7698, 7520, 7447, 6513, 8771, 7674, 7577, 8048, 8304, 8448, 8710, 8881, 10137, 9867, 9320, 9019, 9754]
]
},
"Daily EIR": {
"Units": "",
"Data": [
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]
},
"Daily HBR": {
"Units": "",
"Data": [
[5.640000343323, 4.909999847412, 4.430000305176, 3.762000083923, 3.458000183105, 2.966000080109, 2.68799996376, 2.226000070572, 2.041999816895, 1.78200006485, 1.50200009346, 1.351999998093, 1.195999979973, 1.089999914169, 0.8100000023842, 0.7920000553131, 0.6539999842644, 0.6039999723434, 0.539999961853, 0.4399999976158, 0.4039999842644, 0.3779999911785, 0.2840000092983, 0.2580000162125, 0.2320000082254, 0.2260000109673, 0.1800000071526, 0.6340000033379, 1.213999986649, 1.22000002861, 2.179999828339, 2.012000083923, 2.232000112534, 3.400000095367, 3.461999893188, 4.038000106812, 3.802000045776, 3.886000156403, 3.869999885559, 3.860000133514, 3.598000049591, 3.567999839783, 3.078000068665, 3.067999839783, 2.969999790192, 3.317999839783, 3.110000133514, 3.234000205994, 3.339999914169, 3.555999994278, 3.575999975204, 3.644000053406, 3.519999980927, 3.434000015259, 3.369999885559, 3.398000001907, 3.438000202179, 3.661999940872, 3.786000013351, 3.584000110626, 3.3140001297, 3.067999839783, 3.817999839783, 3.6859998703, 4.696000099182, 4.748000144958, 4.466000080109, 4.932000160217, 4.828000068665, 4.760000228882, 5.244000434875, 5.083999633789, 4.946000099182, 4.944000244141, 5.139999866486, 4.871999740601, 4.868000030518, 4.656000137329, 4.456000328064, 4.487999916077, 4.003999710083, 3.636000156403, 4.421999931335, 3.906000137329, 4.355999946594, 4.354000091553, 4.319999694824, 3.710000038147, 5.109999656677, 4.28200006485, 4.512000083923, 4.640000343323, 4.753999710083, 4.725999832153, 5.130000114441, 5.031999588013, 5.926000118256, 5.78600025177, 5.498000144958, 5.203999996185]
]
},
"Infectious Vectors": {
"Units": "",
"Data": [
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]
}
}
}
Malaria disease overview¶
Welcome to malaria modeling at the Institute for Disease Modeling (IDM)! We are committed to supporting data-driven malaria control and elimination efforts. This page will provide information about the disease itself. The following pages will provide information about the the vector model of Epidemiological MODeling software (EMOD), the software developed by IDM to aid in malaria elimination.
Malaria biology¶
Malaria is a mosquito-borne disease caused by plasmodium parasites transmitted through the bite of blood-feeding Anopheles mosquitos. There are roughly 30 species of Anophelenes capable of serving as malaria vectors, and only females bite (i.e. seek blood meals) to gain nutrients necessary for oviposition. After taking blood meals, females lay eggs in water sources, where the emerging larvae hatch and mature. Each species of Anopheles has different aquatic habitat preferences, ranging from small, ephemeral pools to large swampy areas, and in some cases, brackish water.
Transmission of malaria depends on both biotic and abiotic factors. Mosquito lifespan is positively correlated to transmission intensity. Mosquito populations, and therefore transmission, tend to be seasonal with dependence on climatic conditions such as rainfall patterns, humidity, and temperature. Typically, malaria transmission peaks during and after the rainy season, when conditions are prime for mosquito reproduction. Additionally, transmission is dependent on human immunity: people living in areas of high exposure to malaria can develop partial immunity, which reduces the risk of developing severe disease. Unfortunately, such partial immunity may mask symptoms of disease (and obscure infection), which may hinder control or elimination efforts.
Once an infected mosquito bites a human and transmits parasites, the parasites multiply in the host’s liver, then infect and destroy red blood cells (erythrocytes). During the blood stage, the parasites that infect and destroy erythrocytes release merozoites which then infect other erythrocytes. The gametocyte form of the blood-stage parasite are ingested by female Anophelenes during future blood meals, where the mosquito-based life cycle continues.
In the mosquito, the ingested gametocytes generate zygotes in the mosquito’s gut. Those zygotes undergo development, embed into the mosquito’s midgut wall, and mature into oocytes. Oocytes develop and in turn rupture, releasing the sporozoite form of the parasite. The sporozoites travel to and reside in the mosquito’s salivary glands. Then, when the female takes a blood meal, the sporozoites are injected into the host, starting another infection cycle. The mosquito is the vector that transmits pathogens between human hosts. See the below figure and figure caption for an illustrated description of this process.
Plasmodium life cycle, adapted from the CDC, Malaria Biology. During a blood meal, a malaria-infected female Anopheles mosquito inoculates sporozoites into the human host (1) . Sporozoites infect liver cells (2) and mature into schizonts (3), which rupture and release merozoites (4). After this initial replication in the liver (exo-erythrocytic schizogony, “A”), the parasites undergo asexual multiplication in the erythrocytes (erythrocytic schizogony, “B”). Merozoites infect red blood cells (5). Some parasites differentiate into sexual erythrocytic stages (gametocytes) (7). The gametocytes are ingested by an Anopheles mosquito during a blood meal (8). The parasites’ multiplication in the mosquito is known as the sporogonic cycle, “C”. While in the mosquito’s stomach, the gametocytes generate zygotes (9). The zygotes in turn become motile and elongated (ookinetes) (10) which invade the midgut wall of the mosquito where they develop into oocysts (11). The oocysts grow, rupture, and release sporozoites (12), which make their way to the mosquito’s salivary glands. Inoculation of the sporozoites into a new human host (1) perpetuates the malaria life cycle.¶
Symptoms¶
All the clinical symptoms associated with malaria are caused by the asexual erythrocytic (or blood stage) parasites. Malaria has a long incubation period, so symptoms do not develop immediately after a person has been bitten by an infectious mosquito; they can occur 7 days after infection, but this may vary up to 30 days.
Symptoms of malaria range in severity, but include fever, headache, body-ache, chills, and vomiting. Severe malaria can develop when the infection is not treated, and may result in organ failure or even death. Examples of severe malaria include cerebral malaria, severe anemia, respiratory distress (due to accumulated fluid in the lungs), kidney failure, metabolic acidosis, hypoglycemia, and other maladies. Death can occur from anemia, hypoglycemia, or cerebral malaria (where capillaries leading to the brain are blocked; typically resulting in coma, brain damage or learning disabilities).
Confirmed diagnosis of malaria is done through observation of parasites in the blood, as seen on the below microscopic image. In highly endemic areas, treatment typically occurs prior to diagnostic confirmation, as malaria is more easily cured with prompt treatment.
Human red blood cells infected with Plasmodium falciparum (the parasite is shown in dark purple). Photo credit Le Roch lab, UC Riverside.¶
Prevention & control efforts¶
There are numerous types of strategies used to control malaria. As a vector-borne disease, there are multiple stages at which the transmission cycle can be broken.
Vector control: strategies that take into account vector ecology. These include:
Chemical control, such as insecticide spraying or use of larvicides.
Reduction of or elimination of mosquito larval habitat, through drainage or use of biological controls.
Potential use of genetic modification (with tools such as CRISPR) to create mosquitoes that are resistant to infection from Plasmodium parasites.
Potential use of the bacterium Wolbachia to prevent mosquitoes from becoming infectious.
Personal protection: strategies that avoid infection (by avoiding bites by infectious mosquitoes), or by preventing disease. These include:
The use of insecticide-treated nets (ITN)
Administration of antimalarial drugs to particularly vulnerable groups, such as children or pregnant women
Global malaria burden¶
While progress towards reducing the malaria burden has been largely successful, malaria nevertheless remains a major health problem and target of focused, global efforts for elimination and eradication. According to the CDC, 3.2 billion people worldwide are at risk of malaria. In 2015, there were 214 million cases with 483,000 deaths. Malaria is especially harmful to children: more than 70% of all malaria deaths occur in children under the age of 5. To put that in perspective, a child dies from malaria roughly every 2 minutes.
While malaria is a global problem, the burden is not distributed equally across the globe. Sub-Saharan Africa experiences a disproportionately high burden, with about 76% of all cases and 75% of all deaths. South East Asia, Latin America, and the Middle East are also at high risk for malaria.
Benefits of mathematical models in malaria control and eradication¶
Control and eventual eradication of malaria will require multifaceted and geographically-specific intervention efforts. Heterogeneity in transmission, and transmission potential, creates a need for combinations of interventions that can adapt to the particular malaria epidemiology of the target area.
For malaria, mathematical modeling and simulations are key to achieving eradication. Species- specific vector ecology is a fundamental driver of transmission, and transmission is also impacted by the interactions of climate, human behavior, and land usage, across varying spatial scales. Modeling these factors enables accurate representations of baseline transmission, which in turn provides a platform to test various interventions (such as insecticide-treated nets (ITN), indoor residual spraying (IRS), or mass drug administration (MDA)) and combinations of interventions. These simulation results can inform policy to develop effective–and cost effective–strategies by exploring the many possible dimensions of coverage, frequency of distribution, and combinations of interventions targeted to particular locations.
It should be noted that such a multifaceted and integrated approach to vector control and health management is the most likely path towards elimination, as heavy reliance on singular approaches can be problematic. For example, vector control has been widely implemented and quite successful; however, mosquitoes are developing resistance to pyrethroids, one of the most common classes of insecticides currently recommended for ITN or IRS to control mosquito populations. Through modeling approaches, researchers will be able to develop strategies that lessen dependence on particular insecticides while maintaining successful control efforts.
A brief history of malaria modeling¶
Malaria has a long history of posing risks to public health, and as such, also has long been the target of mathematical models tasked with providing solutions to ease the burden. For a more detailed history of malaria models, see Smith et al. PLOS Pathogens 2012 8(4), and Smith et al. Trans R Soc Trop Med Hyg 2014 108(4). The EMOD malaria model builds upon this rich history of disease modeling to provide a novel and rigorous approach to help guide efforts towards malaria elimination and eradication. To fully understand the strengths of EMOD, it is helpful to understand the modeling background from which EMOD developed.
Arguably, the quantitative analysis of mosquito-borne diseases, specifically malaria, began with Ronald Ross. In 1897 Ross confirmed that mosquitoes serve as the vector for malaria parasites, and embarked on a career focused on disease prevention through vector control. Further, Ross developed the modeling framework that serves as the basis for studying malaria transmission dynamics.
Ross’s work inspired researchers focused on controlling mosquito-borne diseases. His emphasis on the development of metrics useful for measuring the intensity of transmission led to the development of :term:` entomological inoculation rate (EIR)` and motivated research aimed at understanding mosquito movement and the relevant spatial scales for mosquito ecology. Later, in the 1950s, George MacDonald formalized Ross’s models, and introduced the concepts of vectorial capacity and reproductive number. This framework is now known as the Ross-Macdonald model, and is still widely implemented in current modeling work. In fact, the vast majority of models created in the last 40 years share most of their assumptions with the Ross-Macdonald model (see Reiner et al. 2013, Journal of the Royal Society Interface.)
The Garki project¶
The Garki project was a major milestone in malaria research. From 1969 to 1976 this study was conducted by the World Health Organization (WHO) and the local government of the Jigawa State, Nigeria, to understand the impacts of indoor residual spraying (IRS) and mass drug administration (MDA) on malaria transmission, as well as to evaluate the utility of mathematical modeling. While the interventions used did not interrupt transmission at the desired level, the model proved to be a success. The epidemiology of the Plasmodium parasite was realistically replicated, even with a simplistic model, and as a result understanding of the parasite’s epidemiology was greatly increased.
While the project was largely considered a failure in terms of malaria control, much was gained from the project. Basic tenets of malaria control were learned– namely, that malaria ecology needs to be fundamentally altered: either by modification of mosquito ecology, or by changing human ecology such as by improving housing conditions. The superinfection, a fundamental malaria concept modeled by Macdonald in his first malaria model publication [Macdonald-1950], was more correctly described by Dietz [Dietz-1974]. Further, the data set from the project is publicly available, and has become a fundamental tool for use in malaria modeling. For more information on this project, see The Garki Project.
Moving forward¶
The Ross-Macdonald model, while extremely useful and influential, has some shortcomings. The model assumes homogeneous transmission within a well-mixed population. Host-vector ratios and numerous aspects of mosquito feeding and development biology is assumed to remain constant. Further, all hosts are assumed to be identical, with equal exposure to pathogens, and the probability of transmission is proportional to host and vector densities. Interestingly, despite work demonstrating that these assumptions are not realistic, many models still utilize them.
To improve on the Ross-Macdonald model, it is necessary to understand and implement increased complexity into the transmission dynamics of the model. Transmission dynamics are not linear, and instead depend on fine-scale heterogeneities; ecological as well as social contexts are extremely relevant for potential host-vector interactions, and both human and mosquito movement across relevant spatial scales also impact malaria transmission.
Modern research has continued to improve our understanding of both parasite biology (and genetics) as well as within-host immune system interactions. To fully understand the complex nature of malaria epidemiology and how to control it, it is important to include these aspects into models.
The malaria EMOD leverages the history of malaria modeling by staring with these important fundamentals and building upon them. EMOD combines detailed vector population dynamics and interactions with human populations, and includes microsimulations for human immunity and within- host parasite dynamics. The model builds on the work of Ross and MacDonald, leverages the Garki model, and incorporates current modeling efforts to model multiple vector species simultaneously interacting with a human population.
References¶
- Macdonald-1950
Macdonald G (1950) The analysis of infection rates in diseases in which superinfection occurs. Trop Dis Bull 47: 907–915.
- Dietz-1974
Dietz K, Molineaux L, Thomas A (1974) A malaria model tested in the African savannah. Bull World Health Organ 50: 347–357
Malaria model overview¶
The Epidemiological MODeling software (EMOD), malaria model, is an agent-based, discrete time, Monte Carlo method simulator used to simulate malaria conditions in order to evaluate the effectiveness of eradication or mitigation approaches. The model utilizes microsolvers to combine detailed vector population dynamics, human population dynamics, human immunity, within-host parasite dynamics, effects of antimalarial drugs, and other aspects of malaria biology to simulate malaria transmission. 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. Interventions can be deployed within simulations for a variety of transmission settings with different transmission intensities, vector behaviors, and seasonally-driven ecologies.
The following pages describe the structure of the model, and then explore each of the model components. Additionally, the specifics of the model are discussed in detail in the articles Eckhoff, Malaria Journal 2011, 10:303; Eckhoff, Malaria Journal 2012, 11:419; Eckhoff, PLoS ONE 2012, 7(9); and Eckhoff, Am. J. Trop. Med. Hyg. 2013, 88(5).
The generic software architecture of EMOD can be found on Overview of EMOD software. The malaria EMOD is based on the same architecture, with modifications that transform the generic model into a vector-based model. For example, transmission occurs via vectors (and the associated climatalogical impacts on vector biology), individuals can have multiple infections, and campaign interventions can act at the individual level or at the node level.
Typical SEIR models are described in SEIR and SEIRS models. Vectors add a level of complexity to the interactions, as pathogens are transmitted via vector - human - vector. For malaria, the SEIR model would appear as follows:
While EMOD is an agent-based model, both the simulated humans and vectors move through the various infection states analogously to the compartmental model illustrated above. To account for the real-world complexity of malaria transmission, numerous parameters have been added to EMOD to increase its predictive power. Further, these parameters and their associated microsolvers allow EMOD to model aspects of malaria infections and population dynamics that do not readily fit into the SEIRS framework (for example, within-host parasite dynamics, parasitalogical immunity, and innate and adaptive responses to antigens). Finally, as an agent- based model, EMOD enables the addition of spatial and temporal properties to the simulation. The following pages describe this complexity in detail.
Malaria model general information¶
Here we describe the broad overview of how the malaria model functions. As there are numerous parameters in the model, it is useful to categorize parameters into components, which are described in the Malaria model structure and framework section.
The core of the simulation consists of solvers for mosquito population dynamics, pathogen transmission to and from the human host population, and dynamics of the infection within the human host. While the human population is fully individual-based, the mosquito population can be represented by discrete cohorts or individual agent mosquitoes. As an agent-based model, interactions between humans and vectors advance during each time step. The mosquito population is advanced, for example, through discrete 1-day time steps during which mosquitoes may engage in host- seeking or feeding behaviors, mating, maturation through a life-stage, or even death. Successful feeds on humans can result in infection of the human host, or conversely, susceptible mosquitoes may become infected from an infectious human.
Model implementation structure¶
There are two categories of possible implementations of the basic model, each with different computational efficiencies, resolutions, and flexibilities. The first is an individual model, where it simulates every individual mosquito in the population or can utilize a sampled subset of mosquitoes to represent the population as the whole. The second is a modified cohort simulation, with or without explicit mosquito ages.
Individual mosquito model¶
This basic model can be implemented through simulation of every individual mosquito, or by simulation of a subset of individual mosquitoes to represent the full population. Each mosquito’s state contains status (susceptible, latently infected, and infectious), timers for transition to adult from immature and infected to infectious, mating status and Wolbachia infection, and age. An oviposition timer to enforce a fixed feeding cycle may be included as well. If mosquitoes are sampled and a subset used to represent the local population, each sampled mosquito will have an associated sampling weight as well. To use this model, set the Vector_Sampling_Type parameter to TRACK_ALL_VECTORS or SAMPLE_IND_VECTORS. See Sampling parameters for more information.
Cohort model¶
In the modified cohort simulation, rather than representing the entire population by three compartments for susceptible, latently infected, and infectious mosquitoes, the simulation dynamically allocates a cohort for every distinct state, and the cohort maintains the count of all mosquitoes in that state. This allows temperature-dependent progression through sporogony, even with a different mean temperature each day, with no mosquitoes passing from susceptible to infectious before the full discrete latency. For the cohort simulation with explicit ages, in order to allow modeling of senescence, mosquito age is part of the state definition, and many more cohorts are required to represent the population. To use this model, set the Vector_Sampling_Type parameter to VECTOR_COMPARTMENTS_NUMBER or VECTOR_COMPARTMENTS_PERCENT. See Sampling parameters for more information.
Discrete and continuous processes¶
The model implements a hybrid of discrete and continuous processes that work together to capture system latencies and discrete events inherent in the population dynamics of humans, mosquitoes, and parasites. Discrete events, such as latencies in the infected hepatocyte stage, the length of the asexual cycle from merozoite invasion to schizont rupture, and gametocyte maturation have particular time durations. State changes occur after the completion of the required amount of time (with specific time durations set for each state). See the figure of the Plasmodium life-cycle in Malaria disease overview for more information regarding the life-cycle stages used in this example.
Other processes, such as the decay of antibodies and the clearance of parasites, are represented by continuous-time processes. These are solved with a one-hour time step Euler method. All parasite quantities, such as the number of hepatocytes, number of merozoites, number of infected red blood cells of each antigenic variant, and gametocytes of each stage are represented as discrete integers. The infection is not cleared until each category is reduced to zero. This allows resolution of model dynamics at subpatent levels.
Spatial-scale dynamics and migration¶
Extensive multi-node simulations with location-specific climate, intervention deployments, larval habitat, and migration may be configured within the EMOD framework. Nodes can be configured to best represent the spatial scale being simulated: a node can be the household level, village level, county level, country level, or other desired geographic scale. Migration can be enabled between nodes, such that EMOD can most accurately represent the dynamics of the location. By varying spatial-scale resolution, relevant factors for transmission dynamics can be more clearly elucidated.
The following visualization shows an example of a gridded representation of malaria transmission on the island of Madagascar.
For information on configuring nodes and migration parameters, see Migration.
Malaria model structure and framework¶
The malaria EMOD is complex, with numerous configurable parameters. The following network diagram breaks down the model into various model components, and illustrates how they interact with one another. The components on the network diagram correspond to the structural components listed below. Note that there is not perfect overlap between the labels on the network diagram and the structural components; this is because the network is drawn with increased detail in order to provide clarity in how the model functions and the components interact. The following pages will describe in detail how the structural components function.
Network diagram illustrating the malaria model and its constituent components.¶
See the EMOD parameter reference for a complete description of all configurable parameters for the malaria model.
Malaria model structural components¶
The model’s modular framework includes the following components:
Climate and demographics spatial data¶
Climate is especially important in vector models, as it is a key determinant of the geographic distribution and seasonality of malaria. Climate, which includes rainfall, temperature, and humidity, directly influences availability of larval habitat, determines mosquito developmental and survival rates, and impacts human behavior that leads to contact with infectious mosquitoes.
This framework explores the impact of different locations and weather patterns in single node and multi-node geographies. It explores demographics data for births, deaths, age initialization and immunity. Climate and demographics spatial data is configured for a geographic location, such as a rainfall-driven larval habitat, to create a climate-based distribution model of malaria transmission. The seasonality of species abundances are considered, as are rainfall, temperature, humidity and larval dynamics. Mosquito species and seasonality are depicted by habitat preferences that are, for example, temporary, semi-permanent or permanent.
Clearly, climate and habitat type are inextricably linked. However, it is possible to have several types of habitat within a given climate region; for this reason, climate is configured separately from habitat type. You may choose from a variety of options to configure climate, ranging from pre-set classification systems to uploaded user data. Further, it is possible to enable stochasticity in climate as well as in rainfall patterns. For example, rainfall data may be based off of monthly totals averaged per day. In the simulation configuration file, the parameter Enable_Rainfall_Stochasticity will set up daily rainfalls drawn from an exponential distribution with the same daily mean. This preserves approximate monthly totals, but produces a more realistic rainfall pattern for the simulation. For more information on how to configure climate, see Climate files. And for more information on the particular habitat types that are configurable in EMOD see Larval habitat.
The EMOD vector model utilizes site-specific climate and demographics data to accurately simulate vector transmission in a given geographic location.¶
For a complete list of demographics parameters, go to Demographics parameters.
Larval habitat¶
Available larval habitat is a primary driver of local mosquito populations, and different mosquito species can have different habitat preferences. Rainfall and humidity can strongly affect available larval habitat, although this depends on the mosquito species and its particular habitat preference. For example, Anopheles funestus, which prefers more semi-permanent larval habitat, exhibits rainfall dependence, partly due to vegetation on edges of water and the interaction of rainfall with agricultural schedules for crops such as rice. Further, preference can vary within species: An. funestus exhibits differences in population responses to rainfall which are correlated with chromosomal diversity.
In addition to available habitat, mosquito populations depend on larval development and mortality rates. These in turn are affected by a variety of factors, including climate, short term weather, and densities of other larvae.
In the present model, different models for larval habitat are developed for temporary, semi- permanent, permanent, and human-driven habitats. The duration of larval development is a decreasing function of temperature, and the present model utilizes an Arrhenius temperature-dependent rate a1(a2/TK). In some cases, this temperature-dependent rate must be modified by local larval density. Rainfall and temperature then combine through habitat creation and larval development to create varying local patterns of distribution by larval instar, and larval mortality and development duration determine pupal rates.
The creation of the habitat model is described in further detail in the articles Eckhoff, Malaria Journal 2011, 10:303, Eckhoff, Malaria Journal 2012, 11:419, and Eckhoff, Am. J. Trop. Med. Hyg. 2013, 88(5). The following sections provide information regarding specific larval habitat types and how to configure these parameters in EMOD.
See Larval habitat and Vector life cycle parameters for more information on configuring the various vector species.
The framework facilitates simulating multiple species of Anopheles mosquitoes simultaneously. This allows for a mechanistic description of vector abundances through the effects of climate and weather on different preferred larval habitats. Each species is configured separately according to its ecological and behavioral preferences. For example, the female An. arabiensis deposits eggs primarily in temporary, rainfall-driven habitat and has a higher propensity to feed outdoors or on livestock. In contrast, An. farauti larvae live in brackish lagoons in the Solomon Islands, where lagoons fill with rain, and larvae are washed out into the ocean due to excess rain. The temporary rainfall relationship between larval habitat and rainfall cannot reproduce abrupt (sub-week) changes in biting rates, and an additional rainfall-driven larval mortality term has been implemented to capture the non-linear rain-to-habitat relationship.
Vector abundance depends on larval habitat availability¶
Larval habitat type determines how rainfall and temperature influence vector populations¶
The parameter Larval_Habitat_Types is a dictionary that contains one or more string and integer pairs. The user indicates the vector species, habitat type(s), and the array of scaling factors used to determine the volume of that habitat needed for larvae to develop. Note that multiple species may utilize the same habitat, with each species existing independently. The scaling factor represents the number of larvae in a 1x1-degree area. The factor multiplicatively scales the resulting weather or population dependent functional form. The following example shows how to use this parameter, using Anopheles funestus as the vector and CONSTANT as the scaled habitat type.
{
"Vector_Species_params": {
"funestus": {
"Larval_Habitat_Types": {
"CONSTANT": 10000000
},
"Acquire_Modifier": 0.2
}
}
}
The following are possible values for Larval_Habitat_Types. They are described in detail below.
TEMPORARY_RAINFALL
WATER_VEGETATION
CONSTANT
BRACKISH_SWAMP
HUMAN_POPULATION
LINEAR_SPLINE
TEMPORARY_RAINFALL habitat corresponds to mosquitoes such as Anopheles arabiensis or Anopheles gambiae which breed primarily in temporary puddles which are replenished by rainfall and drain through evaporation and infiltration. We have developed a series of update equations for the larval carrying capacity which scale the functional form for evaporation rates. This creates a relationship in which evaporation and infiltration are higher when the weather is hot and dry.
The basic update equations appear in the article Eckhoff, Malaria Journal 2011, 10:303 and are as follows: Temporary habitat Htemp in a grid of diameter Dcell increases with rainfall Prain and decays with a rate \(\tau\)temp proportional to the evaporation rate driven by temperature T(K) and humidity RH:
in which the exponential results from the Clausius-Clayperon relation, the root is from the expression for vapor evaporation rates due to molecular mass given a partial pressure, and the constant is the Clausius-Clayperon integration constant multiplied by a factor ktempdecay to relate mass evaporation per unity area to habitat loss.
Examining the two equations more closely, the first is a time step update equation, in which available habitat is increased by a multiple of the rainfall in the past time step, scaled by the size of the node in the simulation. For example, a 5 km square node will have 25 times the habitat of a 1 km square node. Then existing habitat decays with a time constant defined by the second equation. The basic functional form is based on an equation for evaporation rates, which is not fully realistic in that it neglects boundary layer effects. However, we are looking for a rate of habitat loss, not the rate of evaporation, and we also want to take infiltration into account. The parameter ktempdecay converts raw evaporation rates in kg/m^2/s into habitat loss per day.
The value of ktempdecay is initially chosen to set the habitat half-lives near 1 day for hot and dry conditions and 2-3 weeks for more tropical conditions. The parameter ktempdecay in the article Eckhoff, Malaria Journal 2011, 10:303 corresponds to Temporary_Habitat_Decay_Factor in the simulation configuration file, and it applies to all local species using temporary habitat. Each species in a given location can adjust the parameter ktemp from the paper, or equivalently the scaling factor value in Larval_Habitat_Types in the simulation configuration file (nested under the species name), to adjust the overall scaling of the time series. So ktemp is specific for each species, but ktempdecay is a single value for all temporary habitat species in that location. Basically, the local weather and ktempdecay set the overall time profile for larval habitat which can feed-forward into adult population levels and biting rates. ktempdecay can be adjusted to achieve a best fit for the time profile for a given location. Factors such as how sandy or clay-like the soil is will affect this value. Once the time profile is correct, ktemp can be adjusted to yield the correct annual entomological inoculation rate (EIR) for that species.
The second type of larval habitat, WATER_VEGETATION, is a semi-permanent habitat, which corresponds to developing vegetation on the edges of semi-permanent habitat. This could be seen for swamp-like or rice cultivation settings, in which the development of vegetation lags the rainfall and will be closest to its peak towards the end of the rainy season. The decay is specified as a loss of habitat per day, and this slower decay constant means that species with this habitat type will tend to have a proportionately higher dry-season population relative to the temporary habitat species.
In the model, semi-permanent habitat increases with a constant KsemiDcell2Prain and decays with a longer time constant \(\tau\)semi(Semipermanent_Habitat_Decay_Rate in the simulation configuration file). The parameter Ksemi is the scale factor value in Larval_Habitat_Types for species with a Larval_Habitat_Types of “WATER_VEGETATION”. Permanent habitat is fixed at KpermDcell2, and human population-driven habitat is calculated as population N* Kpop. The values of ktempdecay, \(\tau\)semi, Ksemi, and Ktemp can be fit to local data on vector abundance by species over time or to local data on EIR to tailor a simulation to a specific setting.
For habitat type CONSTANT, larval carrying capacity is constant throughout the year and does not depend on weather. However, there will be a seasonal signal in adult population levels due to the effects of temperature upon aquatic development times. For a given carrying capacity, a faster development time will allow a local habitat to have a higher larval “through-put” with corresponding impacts on the adult population. The scaling factor value in the Larval_Habitat_Types parameter is used to specify the carrying capacity per unit area Dcell2.
The BRACKISH_SWAMP habitat setting deals with the dynamics of how rain fills a brackish swamp, how it decays and the associated parameter for rainfall-driven mortality threshold. This habitat type is observable in the Solomon Islands where an entire larval generation is flushed away as a result of the overflowing lagoon geography. Since this is the case, the TEMPORARY_RAINFALL relationship between larval habitat and rainfall cannot reproduce these abrupt changes (on 0 week timescales) of biting rates. So, an additional rainfall-driven larval mortality term has been implemented that captures the non-linear rain-to- habitat relationship. This habitat uses the same decay rate as the semi-permanent water vegetation habitat.
Rainfall-driven mortality during swamp flushing¶
The habitat type HUMAN_POPULATION scales with correlates of urban development, for example, water that is available due to water pots in urban areas. This type is configured by multiplying the number of people in the node’s population times the capacity value set in Larval_Habitat_Types. Further, climate is not involved in setting the capacity, as available water is not dependent on rainfall (it is instead dependent on human-provided water sources).
LINEAR_SPLINE is a recent addition to the types of habitat supported. Instead of specifying a habitat type, users may utilize data that tracks the number of larvae measured throughout the year to estimate the daily larval population using linear interpolation. In other words, this option enables the user to use data collected from a specific site, or create data files to match a particular location. This option does not replace the climatological parameters, but it instead adds a more flexible option in which you do not need climate data.
LINEAR_SPLINE has different syntax than the other habitat types, as the user is now required to provide a distribution of the larval population. The following example provides the syntax for configuring this habitat type:
{
"Vector_Species_params": {
"funestus": {
"Larval_Habitat_Types": {
"LINEAR_SPLINE": {
"Capacity_Distribution_Per_Year": {
"Times": [0.0, 60.833, 121.667, 182.5, 243.333, 304.167],
"Values": [0.0, 0.0, 0.2, 1.0, 0.5, 0.0]
},
"Max_Larval_Capacity": 10000000000.0
}
}
}
}
}
Ultimately, habitat is configured in order to create mosquito populations that realistically emulate observed entomological inoculation rate (EIR). Briefly, available habitat is directly related to mosquito abundance, and mosquito abundance in turn is directly related to biting rate. In order to calibrate the model there are several options for configuring habitat. You can first set habitat parameters and modify them directly using the habitat scalar and decay rate. Then, after those initial parameters are set, you can modify habitat with overall scaling parameters.
The following two figures demonstrate the effects of varying the habitat scalar (Larval_Habitat_Types) and the Temporary_Habitat_Decay_Factor (ktempdecay) for a single species with temporary habitat. Changing the habitat scalar will scale the resulting adult population size and biting rate by a similar factor.
Effect of varying the habitat scalar, Larval_Habitat_Types¶
Lowering ktempdecay causes the resulting rainfall-driven habitat to decay at a slower rate and thus increases \(\tau\)temp. A slower decay rate will result in higher larval habitat on average, and higher resulting adult population biting rates.
Effect of varying the decay rate, Temporary_Habitat_Decay_Factor (ktempdecay)¶
These two parameters can be co-varied to produce an appropriate temporal profile. If rainfall is constant, there is only one degree of freedom, as the habitat is always at equilibrium. The two degrees of freedom become important when there is a distinct rainy season. For example, scaling habitat produces the same ratio between biting rates in the wet season and dry season as seen in Effect of Varying Habitat Scalar.
However, when varying the decay rate, the ratio between habitats is different in the wet season versus the dry season. As seen in the graph “Effect of Varying Temporary Habitat Decay Scalar”, a given ratio in rainy season will become exaggerated in the start of dry season (with a higher ratio the drier the season), as the slower decay rate extends habitat longer into the dry season.
Often, it is desired to study a similar location, with the same species, temporal profile, etc., but with a different annual entomological inoculation rate (EIR). EIR depends not only on climate, but on larval habitat availability as well. So as an alternative for modifying all climate and habitat parameters to change EIR, it is simpler to use the habitat scaling parameter, x_Temporary_Larval_Habitat found in the configuration file. This parameter will scale all habitat parameters without changing the temporal dynamics, so that a new transmission is achieved with the same ratios among the species, and same time profile. For example, setting x_Temporary_Larval_Habitat to 0.1 would simulate low EIR (or a low transmission setting) by reducing available habitat to 10%; a value of 1 could be used to simulate high EIR (or a high transmission setting), and there would be no reduction in available habitat.
An alternative to x_Temporary_Larval_Habitat is LarvalHabitatMultiplier. LarvalHabitatMultiplier is a parameter in the demographics file, and can be applied to all habitat types configured for the simulation, specific habitat types, or individual mosquito species within particular habitat types in the Nodes array of the demographics file (see NodeAttributes in Demographics parameters). The following example shows the syntax:
{
"Nodes": {
"NodeID": 340461476,
"NodeAttributes": {
"LarvalHabitatMultiplier": 10.0
}
}
}
It should be noted that LarvalHabitatMultiplier enables habitat availability to be modified independently for each species within a shared habitat. This is an upgrade over previous versions of EMOD in which the modifier would be applied equally to all species within a shared habitat.
The simulation configuration file has a field Vector_Species_Names which specifies the vector species that will be present in the simulation. For example, the following specifies that Anopheles arabiensis, An. funestus, and An. gambiae will be added to the simulation:
{
"Vector_Species_Names": [
"arabiensis",
"funestus",
"gambiae"
]
}
For each species listed, a “VectorPopulation” object will be added to the simulation at each node. Each species will be defined by parameters in the simulation configuration file for the vector ecology and behavior of the species. This allows for a mechanistic description of vector abundances and behavior through the effects of climate and weather on different preferred larval habitats.
The following example shows the syntax for configuring species parameters for Anopheles arabiensis. Note that you would also need to configure parameters for An. fuenstus and An. gambiae, if you are using a three species model as from the above example.
{
"Vector_Species_Params": {
"arabiensis": {
"Acquire_Modifier": 0.2,
"Adult_Life_Expectancy": 10,
"Anthropophily": 0.95,
"Aquatic_Arrhenius_1": 84200000000,
"Aquatic_Arrhenius_2": 8328,
"Aquatic_Mortality_Rate": 0.1,
"Days_Between_Feeds": 3,
"Egg_Batch_Size": 100,
"Immature_Duration": 2,
"Indoor_Feeding_Fraction": 1,
"Infected_Arrhenius_1": 117000000000,
"Infected_Arrhenius_2": 8336,
"Infected_Egg_Batch_Factor": 0.8,
"Infectious_Human_Feed_Mortality_Factor": 1.5,
"Larval_Habitat_Types": {
"TEMPORARY_RAINFALL": 11250000000
},
"Transmission_Rate": 0.5
}
}
}
While the above example shows the mosquito residing in one habitat, it is worth noting that mosquitoes can reside in multiple habitat types. This linear-combination of habitat types allows for greater control over local vector ecology (especially during dry seasons as habitat availability may be drastically different). The following example demonstrates the syntax to include several habitat types.
{
"Vector_Species_Params": {
"arabiensis": {
"Acquire_Modifier": 0.2,
"Adult_Life_Expectancy": 10,
"Anthropophily": 0.95,
"Aquatic_Arrhenius_1": 84200000000,
"Aquatic_Arrhenius_2": 8328,
"Aquatic_Mortality_Rate": 0.1,
"Days_Between_Feeds": 3,
"Egg_Batch_Size": 100,
"Immature_Duration": 2,
"Indoor_Feeding_Fraction": 1,
"Infected_Arrhenius_1": 117000000000,
"Infected_Arrhenius_2": 8336,
"Infected_Egg_Batch_Factor": 0.8,
"Infectious_Human_Feed_Mortality_Factor": 1.5,
"Larval_Habitat_Types": {
"TEMPORARY_RAINFALL": 11250000000,
"WATER_VEGETATION": 6000000000
},
"Transmission_Rate": 0.5
}
}
}
Vector transmission model¶
The vector model can be run in one of two modes: as a cohort model or as an individual mosquito model (see Malaria model overview for more information). The vector model inherits the same human-infection model structure from the generic simulation type: uninfected, latent incubation, infectious, multiple immune variables, and super-infection. However, the transmission of infections is not between individual humans, but rather via the human-to-vector and vector-to-human pathways. The model is a closed-loop feeding cycle where a successful vector (mosquito) feeds (both indoors and outdoors), mates, then produce eggs which become larva and then adult mosquitoes.
For example, vector blood-feeding branches into various probabilities that are calculated once per time step. These calculations are based on species parameters and aggregated vector interventions (see feeding decision tree).
Vector transmission can be thought of in terms of vector ecology, and in the EMOD framework is comprised of several components: 1. Vector tracking and the mosquito life-cycle, 2. mosquito behavior, which involves feeding (see Modeling feeding cycle outcomes), and 3. transmission of malaria-causing Plasmodium parasites between humans and mosquitoes (see Transmission between humans and vectors). Each of these components is described in more detail below.
For a complete list of configuration parameters that are used in the malaria model, see the Configuration parameters.
Populations are tracked as cohorts throughout the full mosquito life-cycle:
From eggs to larvae with a temperature-dependent development period preceding emergence
A brief immature phase including sugar-feeding and mating
Repeating multi-day gonotrophic cycles during which mosquitoes may be exposed to infection by gametocytes
A temperature-dependent latency for sporogony
Life-cycle progression is modulated through a set of required species-specific parameters.
Vector life cycle¶
Adult vectors enter a cycle of host-seeking, feeding, and egg-laying that continues until death. Successful vector blood-feeding branches into various probabilities that are calculated once per time step. These calculations are based on species parameters (for example, An. arabiensis has a higher propensity to feed outdoors or on livestock) and aggregated vector interventions.
Various feeding cycle outcomes are calculated from branching trees of conditional probabilities, and individual interventions modulate the probability of choosing between branches. Feeding cycle outcomes include:
Death before, during, or after feeding
Host unavailable
Successful human feed
The allocation of mosquitoes to feeding-cycle outcomes is based on end-state probabilities that have been aggregated over the individual humans in the simulation.
Multiple simultaneous interventions can target various branches in the vector feeding tree. For example, when both indoor residual spraying (IRS) and insecticide-treated nets (ITN) are applied against indoor host-seeking mosquitoes, IRS can discourage mosquitoes from entering the house and kill mosquitoes before feeding. The fraction of mosquitoes that survive can be blocked by the ITN, which may also kill a subset of the blocked fraction. Those mosquitoes who survive the feeding attempt may be killed by IRS post-feed. This is how deterrent and toxic effects of multiple interventions can be represented simultaneously.
To interact with these parameters and visualize the workings of this microsolver, see the decision tree visualization below:
To get started, press the play button. You can also pause the visualization at any time. Parameters in blue are vector species parameters, while parameters in green are types of campaign interventions. Information on these parameters can be found in Configuration parameters and Campaign parameters. The two pink points on the tree illustrate when transmission of malaria parasites is possible.
When the simulation starts, the initial mosquito population is set at 100 individuals. The starting population for day two has an initial seeding of 50 mosquitoes, and also includes all mosquitoes that either live without feeding or feed and oviposit. The simulation includes parameters that determine the lifespan of mosquitoes and the time it takes for oviposited eggs to hatch and mature to adulthood. As time progresses, the population will be comprised of only mosquitoes that are generated through the oviposition cycle in the model.
The counters on the right side of the visualization keep track of current and total mosquitoes that have “spawned” (generated in the simulation), died, lived without feeding, and fed and oviposited.
As an example, let’s simulate Anopheles gambiae. Set Life_Expectancy to 10 (most are thought to live approximately 1-2 weeks in nature), Egg_to_Adult to 5 (this is their minimum duration in the aquatic phase), Days_between_Feeds to 3, and Anthropophily and Indoor_Feeding_Fraction to 0.8. These mosquitoes prefer to primarily feed on humans, and preferentially feed indoors. Now, by changing the interventions, you can see how effective interventions (or combinations of interventions) need to be in order to disrupt (and reduce) mosquito feeding and oviposition. Note that the slider bars for interventions range from 0 - 1, with 1 conferring 100% effectiveness. When mosquito ecology is sufficiently disrupted, malaria transmission can be controlled. You can also manipulate the species parameters to see how mosquito ecology impacts the need for particular types of interventions.
If you are interested in simulating other mosquito species, more information on their relevant attributes can be found in the articles Made-to-measure malaria vector control strategies: rational design based on insecticide properties and coverage of blood resources for mosquitoes, by Killeen et al., 2014, Malaria Journal 13:146, and A global bionomic database for the dominant vectors of human malaria, by Massey et al., 2016, Nature.
Transmission between humans and vectors can only occur when mosquitoes successfully feed on humans (see Modeling feeding cycle outcomes). Infectious humans can infect susceptible mosquitoes when mosquitoes ingest gametocytes during the blood meal. Conversely, infectious mosquitoes can infect susceptible humans by injecting sporozoites into the blood during feeding. Infectiousness of individuals to mosquitoes is proportional to the number of mature gametocytes absorbed in a blood meal, but modulated by a factor that inactivates gametocytes at high cytokine densities. Peak infectiousness typically follows peak parasitemia by about ten days. See the Malaria infection and immune model for more information regarding within-host parasite dynamics, and the figure on the parasite life-cycle in the Malaria disease overview for more information on the parasite life stages.
Transmission between humans and vectors¶
Malaria infection and immune model¶
To study aspects of malaria infections and population dynamics that do not fit into the framework of a simple SEIRS model with enhancements, EMOD contains a detailed microsolver using within-host parasite dynamics. In the microsolver, the development of clinical and parasitological immunity is tracked through innate and adaptive immune responses to specific antigens. A detailed parasite count over time is tracked for each infected individual. This permits the simulation of measured prevalence over time by slide microscopy, RDTs (Rapid Diagnostic Tests) or other diagnostics. Gametocyte production and decay is included to study the human infectious reservoir.
Immune variables, such as antibody levels for each antigen, inflammatory cytokine levels, and immunological memory are represented by continuous floating-point variables. Most model parameters have a specific physical interpretation and can be rationally constrained by defined experimental measurements.
For a complete list of configuration parameters that are used in the malaria model, see the Configuration parameters.
Within the microsolver framework, each new infection begins with a hepatic (liver-stage) latency of fixed duration (7 days) and proceeds through cycles of asexual replication (with a fixed duration of 2 days) and gametocyte production (which takes 10 days). The model accounts for several antigenic components to which the immune system may develop immunity: the merozoite surface protein (MSP) variant, the Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) presented on the surface of the infected red-blood cell (IRBC), and less immunogenic minor surface epitopes (nonspecific epitopes).
Within-host dynamics¶
The model advances gametocytes through five stages of development that are characterized by different drug susceptibilities. A fraction of gametocytes die at each developmental stage, and gametocytes can be inactivated by cytokines after uptake in a blood meal. Gametocytes reach maturity after ten days and remain in the bloodstream with a 2.5-day half-life. EMOD does not model acquired immunity to gametocytes.
Gametocyte development¶
A single clonal infection is modeled with an antigenic repertoire of 50 unique PfEMP-1 variants where each is associated with one of five repeating minor epitopes. In the first asexual cycle, the first five variants are expressed in equal numbers. At each update, the immune system is stimulated by the Infected Red Blood Cell (IRBC) count of each antigenic variant, and concurrently, the IRBC counts are decremented on account of immune and drug killing effects.
At the end of each asexual cycle, the model calculates the fraction of merozoites (16 for each previous IRBC) killed by specific recognition of the MSP variant, and the fraction that is differentiated into male and female gametocytes. To capture the dynamics of the parasite’s immune evasion strategy, the model imposes a constant per-parasite switching rate on the remaining merozoites to advance to subsequent antigenic variants in the repertoire.
Parasite strain switching¶
The number of population-level variants affect the age-pattern of natural immunity acquisition. The full set of population-level antigenic variants (out of which a single infection’s repertoire is randomly drawn), consists of 100 MSP variants, 20 sets of five minor epitopes, and 1000 PfEMP-1 variants. Some parameters drive the asymptotic levels of adult detected parasitemia while others primarily govern the transition between child and adult detected prevalence rates, and provide that the number of PfEMP-1 variants is substantially more than an individual would experience in a year. A multi-year burn-in period ensures immune initialization dynamics approximate long-term asymptotic behavior where individuals in the simulation build antibody responses to a broad repertoire of parasite antigens appropriate for their age.
The immune response to infection is characterized by innate inflammatory and specific antibody components that limit maximum parasite density and work to clear infection. As the antibody response increases on a dimensionless scale from 0 to 1, the initial inflammatory response is suppressed. After the antibody response appears, continued antigenic stimulation will drive up the adapted response until that antigenic variant is cleared, at which point both the antibody levels and the capacity to respond will decay over time. The larger the antigenic population, the more infections, and thus time, it takes to acquire broad parasitological immunity.
Innate and adaptive immunity work together to limit asexual parasite numbers¶
The innate immune response is modeled to depend on a temporary contribution from a rupturing schizont at the end of each asexual cycle, as well as the concentration of IRBC surface antigens to which an antibody response has not yet been developed. The innate response that is suppressed by the presence of specific antibodies is responsible for driving febrile symptoms and broad-spectrum parasite suppression.
Innate immune response¶
The capacity to generate specific antibodies grows in response to the concentration of each novel antigen. Above a capacity threshold level, antibodies are produced in increasing concentration until the corresponding antigenic variant is cleared. At this time, the capacity will decay to a non-zero memory level. The mechanism by which the antibody capacity evolves captures the time delay of specific antibody response on re-infection.
Adaptive immune response to PfEMP1 and minor epitopes¶
Adaptive immune response to PfEMP1 and minor epitopesAdaptive immune response to MSP antigens¶
Adaptive immune response to CSP¶
Malaria symptoms and diagnostics¶
In EMOD, parameters for multiple types of malaria symptoms are included. Symptoms indicate the presence of disease, and one of the most prevalent types of data used to inform models is case data. By leveraging such data, models can help elucidate the processes that cause disease and enable calculations of the likely percent of the population that is infected (or infectious). Similarly, in order to model treatment-seeking behaviors (which will change the transmission dynamics of malaria), it is necessary to have a spectrum of disease severity (and hence, a spectrum of symptom parameters). Finally, disease severity is related to past infection and immune response, so inclusion of symptoms can help to further inform transmission dynamics of the target population.
For a complete list of configuration parameters that are used in the malaria model, see the Configuration parameters.
Fever is triggered by cytokine production through the innate immune response. A clinical case begins when fever surpasses a certain threshold and the clinical incident continues until fever subsides below another threshold.
Fever and clinical case definition¶
Rupture of infected red blood cells (RBCs) destroys nearby RBCs and leads to anemia. Erythropoiesis is stimulated in anemic individuals to increase the rate of RBC production.
Anemia¶
Antimalarial drugs¶
Antimalarial drugs are a powerful tool for malaria control and elimination. Modeling mass antimalarial campaigns can elucidate how to most effectively deploy drug-based interventions and quantitatively compare the effects of cure, prophylaxis, and transmission-blocking in suppressing parasite prevalence.
Individuals in a simulation can receive antimalarial drugs as treatment for clinical or severe malaria or during a mass drug administration (MDA) campaign. The following information explains how to configure antimalarial drug parameters in a configuration file. For a complete list of configuration parameters that are used in the malaria model, see the Configuration parameters.
To specify where, when, and to whom the antimalarial drugs are administered, you must create an AntiMalarialDrug intervention in the campaign file. See Campaign parameters for more information.
Pharmacokinetics (PK) of antimalarial drugs can be modeled with a box profile or with a double exponential decay. Pharmacodynamics (PD) of antimalarial drugs are modeled as an on/off switch for box PK or with a simple sigmoid for double exponential PK. If multiple drugs are given in a treatment, each drug is considered independently for both PK and PD.
To model a box PK, set the parameter PKPD_Model to FIXED_DURATION_CONSTANT_EFFECT. Drugs will have full killing efficacy for the duration set by Drug_Decay_T1. Dosing multiple times leads to multiple pulses of drug killing if dose separation is longer than Drug_Decay_T1. If dose separation (Drug_Dose_Interval) is shorter than Drug_Decay_T1, the box time is reset by each new dose such that drug killing is switched off after time Drug_Decay_T1 after the last dose.
Modeling drug killing effects with a box profile. (A) A single dose of drug kills parasites at maximum efficacy for duration set by Drug_Decay_T1. (B) A drug given in multiple doses results in multiple parasite killing pulses of duration Drug_Decay_T1 if the dose separation Drug_Dose_Interval is longer than Drug_Decay_T1. (C) If a drug’s dosing regimen results in doses taken before time Drug_Decay_T1 has elapsed, the drug kills parasites at maximum efficacy the entire time between the first dose and time Drug_Decay_T1 after the last dose.¶
The following example provides the syntax:
{
"Malaria_Drug_Params": {
"Artemether_Lumefantrine": {
"Drug_Cmax": 1000,
"Drug_Decay_T1": 1,
"Drug_Decay_T2": 1,
"Drug_Dose_Interval": 1,
"Drug_Fulltreatment_Doses": 3,
"Drug_Gametocyte02_Killrate": 2.3,
"Drug_Gametocyte34_Killrate": 2.3,
"Drug_GametocyteM_Killrate": 0,
"Drug_Hepatocyte_Killrate": 0,
"Drug_PKPD_C50": 100,
"Drug_Vd": 10,
"Max_Drug_IRBC_Kill": 4.61,
"Bodyweight_Exponent": 0
},
"Artemisinin": {},
"Chloroquine": {},
"GenPreerythrocytic": {},
"GenTransBlocking": {},
"Primaquine": {},
"Quinine": {},
"SP": {},
"Tafenoquine": {}
}
}
To model a double exponential PK, set the parameter PKPD_Model to CONCENTRATION_VERSUS_TIME. The double exponential PK approximates a one- or two-compartment PK model. To model a drug with 1-compartment PK, set Drug_Decay_T2 equal to Drug_Decay_T1 (Drug_Vd becomes irrelevant). For PD, the Drug_PKPD_C50 determines the drug concentrations where parasite killing is effective (drug concentration > C50) and ineffective (drug concentration < C50). EMOD sets the shape of the parasite killing curve based on parasite kill rate and C50. Drug_Cmax, Drug_Decay_T1, Drug_Decay_T2, Drug_Vd, and Drug_PKPD_C50 together determine when the drug is effectively killing parasites. Changing each of these parameters will affect how long the parasites are exposed to a strong killing effect. Adding additional doses will also increase the duration of the parasite- killing window.
Children can be dosed with a fraction of the adult dose according to Fractional_Dose_By_Upper_Age, where each dose fraction is paired with the maximum age of children receiving that dose. Depending on specific PK characteristics of each drug, bodyweight may affect the rate of drug clearance. In the double exponential PK model, bodyweight is directly determined by age, and Drug_Cmax is multiplied by the inverse of the bodyweight raised to the power of Bodyweight_Exponent. See the paper on drug PK and PD ( Mass campaigns with antimalarial drugs: a modelling comparison of artemether-lumefantrine and DHA-piperaquine with and without primaquine as tools for malaria control and elimination) for more details on how to set PK and PD parameter values in EMOD.
Modeling drug killing effects with a double exponential PK and sigmoid PD. (A) Two-compartment PK models include both distribution and elimination of drug. One-compartment models do not have a distribution phase. (B) A double exponential PK approximates the distribution and elimination phases of a two- compartment model. (C) Parasite killing is determined by drug concentration, Drug_PKPD_C50, and parasite stage- specific maximum kill rates. (D) PK and PD together determine the duration over which a dose of drug is effective.¶
Malaria research at IDM¶
Malaria research at IDM is progressive and dynamic. For the current research, research team profiles, and a full list of published papers from IDM’s malaria group, see the IDM Malaria Research Website.
IDM malaria publications¶
Information specific to the components of the model described previously can be found in the following list of select IDM malaria publications.
Vector model¶
Eckhoff, 2013. Mathematical models of within-host and transmission dynamics to determine effects of malaria interventions in a variety of transmission settings. Am J Trop Med Hyg. 88(5):817-27
Eckhoff, 2011. A malaria transmission-directed model of mosquito life cycle and ecology. Malaria Journal. 10:303
Infection and immunity: Within-host parasite dynamics¶
Lawniczak & Eckhoff, 2016. A computational lens for sexual-stage transmission, reproduction, fitness and kinetics in Plasmodium falciparum. Malaria Journal. 15:487
Cameron, et al., 2015. Defining the relationship between infection prevalence and clinical incidence of Plasmodium falciparum malaria. Nature Communications. 6:8170
Ouedraogo, et al., 2015. Dynamics of the Human Infectious Reservoir for Malaria Determined by Mosquito Feeding Assays and Ultrasensitive Malaria Diagnosis in Burkina Faso. Journal of Infectious Diseases. 213(1):90-99
Gerardin, Ouedraogo, McCarthy, Eckhoff and Wenger, 2015. Characterization of the infectious reservoir of malaria with an agent-based model calibrated to age-stratified parasite densities and infectiousness. Malaria Journal. 14:231
Eckhoff, 2012. Malaria parasite diversity and transmission intensity affect development of parasitological immunity in a mathematical model. Malaria Journal. 11:419
Eckhoff, 2012. P. falciparum Infection Durations and Infectiousness Are Shaped by Antigenic Variation and Innate and Adaptive Host Immunity in a Mathematical Model. PLOS one. 7(9)
Outcrossing of parasite genetics¶
Daniels, et al., 2015. Modeling malaria genomics reveals transmission decline and rebound in Senegal. PNAS. 112(22):7067-7072
Antimalarial drugs¶
Bellinger, et al., 2016. Oral, ultra–long-lasting drug delivery: Application toward malaria elimination goals. Science Translational Medicine. 8(365).
Gerardin, Eckhoff and Wenger, 2015. Mass campaigns with antimalarial drugs: a modeling comparison of artemether-lumefantrine and DHA-piperaquine with and without primaquine as tools for malaria control and elimination. BMC Infectious Diseases. 15:144
Vaccines¶
Penny, et al., 2016. Public health impact and cost-effectiveness of the RTS,S/AS01 malaria vaccine: a systematic comparison of predictions from four mathematical models. The Lancet. 387(100016): 367-375
McCarthy, Wenger, Huynh and Eckhoff, 2015. Calibration of an intrahost malaria model and parameter ensemble evaluation of a pre-erythrocytic vaccine. Malaria Journal. 14:6
Wenger & Eckhoff, 2013. A mathematical model of the impact of present and future malaria vaccines. Malaria Journal. 12:126
Spatial models¶
Nikolov, et al., 2016. Malaria Elimination Campaigns in the Lake Kariba Region of Zambia: a Spatial Dynamical Model. PLOS Computational Biology.
Marshall, et al., 2016. Key traveler groups of relevance to spatial malaria transmission: a survey of movement patterns in four sub-Saharan African countries. Malaria Journal. 15(200)
Gerardin, et al., 2016. Optimal Population-Level Infection Detection Strategies for Malaria Control and Elimination in a Spatial Model of Malaria Transmission. PLOS Computational Biology.
Eckhoff, et al., 2015. From puddles to planet: modeling approaches to vector-borne diseases at varying resolution and scale. Current Opinion in Insect Science. 10:118-123
Interventions¶
Eckhoff, Wenger, Godfray and Burt, 2016. Impact of mosquito gene drive on malaria elimination in a computational model with explicit spatial and temporal dynamics. PNAS. 114(2)
Bhatt, et al., 2015. The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015. Nature. 526:207-211
EMOD parameter reference¶
The configurable parameters described in this reference section determine the behavior of a particular simulation and interventions. The parameter descriptions are intended to guide you in correctly configuring simulations to match your disease scenario of interest.
The reference section contains only the parameters supported for use with the malaria simulation type. To see the parameters for a different simulation type, see the corresponding documentation.
These parameters include three major groups: parameters for the demographics file, parameters for the configuration file, and parameters for the campaign file.
If you would rather use a text file for parameter reference than this web documentation, you can also generate a schema with the EMOD executable (Eradication.exe) that defines all configuration and campaign parameters that can be used in a simulation, including names, data types, defaults, ranges, and short descriptions.
JSON formatting overview¶
All of these parameters are contained in JSON (JavaScript Object Notation) formatted files. JSON is a data format that is human-readable and easy for software to read and generate. It can be created and edited using a simple text editor.
JSON has two basic structures: a collection of key-value pairs or an ordered list of values (an array). These structures are within a JSON object, which is enclosed in a set of braces. You will notice that each of these files begins with a left brace ({) and ends with a right brace (}).
A value can be a string, number, Boolean, array, or an object. The campaign and data input files often use nested objects and arrays. See www.json.org for more information on JSON.
A few important details to call out when creating JSON files:
Add a comma after every key-value pair or array except for the last key-value pair in an array or object.
The keys (parameters) are case-sensitive. For example, “NodeID” is not the same as “NodeId”.
Decimals require a 0 (zero) before the decimal point.
EMOD does not support Booleans (“True”, “False”). Instead, EMOD use the integer 1 for “True” and 0 for “False”.
Every opening brace or 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 ]
]
}
}
]
}
}
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 other input data files for a particular region. By convention, these are named using the name of the region appended with “_demographics.json”, but you may name the file anything you like.
Additionally, you can use more than one demographics file, with one serving as the base layer and the one or more others acting as overlays that override the values in the base layer. This can be helpful if you want to experiment with different values in the overlay without modifying your base file. For more information, see Demographics file.
At least one demographics file is required for every simulation unless you set the parameter Enable_Demographics_Builtin to 1 (one) in the configuration file. This setting does not represent a real location and is generally only used for testing and validating code pathways rather than actual modeling of disease.
Demographics files are organized into four main sections: Metadata, NodeProperties, Defaults, and Nodes. The following example shows the skeletal format of a demographics file.
{
"Metadata": {
"DateCreated": "dateTime",
"Tool": "scriptUsedToGenerate",
"Author": "author",
"IdReference": "Gridded world grump2.5arcmin",
"NodeCount": 2
},
"NodeProperties": [
{}
],
"Defaults": {
"NodeAttributes": {},
"IndividualAttributes": {},
"IndividualProperties": {},
"Society": {}
},
"Nodes": [{
"NodeID": 1,
"NodeAttributes": {},
"IndividualAttributes": {},
"IndividualProperties": {},
"Society": {}
}, {
"NodeID": 2,
"NodeAttributes": {},
"IndividualAttributes": {},
"IndividualProperties": {},
"Society": {}
}]
}
All parameters except those in the Metadata and NodeProperties sections below can appear in either the Defaults section or the Nodes section of the demographics file. Parameters under Defaults will be applied to all nodes in the simulation. Parameters under Nodes will be applied to specific nodes, overriding the values in Defaults if they appear in both. Each node in the Nodes section is identified using a unique NodeID.
The tables below contain only parameters available when using the malaria simulation type.
Note
Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
Contents
Metadata¶
Metadata provides information about data provenance. NodeCount and IdReference are the only parameters used by EMOD, but you are encouraged to included information for your own reference. For example, author, date created, tool used, 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 data 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 data files used in a simulation must have the same IdReference value. The value must be greater than 0. If the input data 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 data 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.
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.
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. |
{
"Nodes": [{
"NodeID": 25,
"IndividualProperties": [{
"Initial_Distribution": [0.2, 0.4, 0.4]
}]
}]
}
{
"NodeProperties": [{
"Property": "InterventionStatus",
"Values": [ "NONE", "RECENT_SPRAY" ],
"Initial_Distribution": [ 1.0, 0.0 ]
}]
}
|
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]
}]
}
|
Transitions |
array |
NA |
NA |
NA |
An array that contains multiple JSON objects that each define how an individual transitions from one property value to another. See the transitions array table for information about the parameters to include in the Transitions object. For Age_Bin property types, set to an empty array, as individuals will transition to the next age bin based on the passing of time. Cannot be used with NodeProperties. |
{
"Defaults": {
"IndividualProperties": [{
"Transitions": [{
"From": "High",
"To": "Medium",
"Type": "At_Age",
"Coverage": 1,
"Probability_Per_Timestep": 0.3,
"Age_In_Years": 5,
"Timesteps_Until_Reversion": 0
}, {
"From": "Medium",
"To": "Low",
"Type": "At_Age",
"Coverage": 1,
"Probability_Per_Timestep": 0.3,
"Age_In_Years": 12,
"Timesteps_Until_Reversion": 0
}]
}]
}
}
|
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 must be set to Contact 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 simulation type. 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. |
{
"Defaults": {
"IndividualProperties": [{
"Values": ["Low", "Medium", "High"]
}]
}
}
{
"NodeProperties": [
{
"Property": "InterventionStatus",
"Values": [ "NONE", "RECENT_SPRAY" ],
"Initial_Distribution": [ 1.0, 0.0 ]
}
]
}
|
The Transitions array under IndividualProperties section controls how individuals transition from one property value to another. It cannot be used with NodeProperties. Alternatively, similar transitions can be configured in the campaign file as the result of campaign events.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Age_In_Years |
float |
0 |
125 |
NA |
The age at which individuals are eligible to transition. Do not use when Type is set to At_Timestep. Required when Type is set to At_Age. |
{
"IndividualProperties": [{
"Transitions": [{
"Type": "At_Age",
"Age_In_Years": 10
}]
}]
}
|
Age_In_Years_Restriction |
JSON object |
0 |
100 |
3.40E+38 |
The age when an individual is eligible to transition. Min is optional and Max is required. Do not use this with Type set to At_Age because it will conflict with other age parameters in Age_In_Years. This is required when Type is set to At_Timestep, though it can be empty for no age restriction. |
{
"IndividualProperties": [{
"Transitions": [{
"Type": "At_Timestep",
"Age_In_Years_Restriction": {
"Min": 5,
"Max": 40
}
}]
}]
}
|
Coverage |
float |
0 |
1 |
1 |
The proportion of the population that EMOD will attempt to transition. The calculation used with Probability_Per_Timeset only uses the proportion of the population specified in Coverage to calculate the probability of an individual transitioning at a given time step. Coverage has no effect when the value is set to 1. Required when Type is set to either At_Age or At_Timestep. |
{
"Individual_Properties": [{
"Transitions": [{
"From": "Bad",
"To": "Good",
"Coverage": 0.5
}]
}]
}
|
From |
string |
NA |
NA |
NA |
The property value that an individual will transition from. |
{
"Individual_Properties": [{
"Transitions": [{
"From": "Bad",
"To": "Good",
"Coverage": 0.5,
"Type": "At_Timestep"
}]
}]
}
|
Probability_Per_Timestep |
float |
0 |
1 |
NA |
The daily probability of an individual transitioning given the number of individuals remaining with the same property value. Required when Type is set to At_Age or At_Timestep. |
{
"Individual_Properties": {
"Transitions": [{
"From": "Low",
"To": "High",
"Coverage": 0.9,
"Type": "At_Timestep",
"Probability_Per_Timestep": 0.25
}]
}
}
|
Timesteps_Until_Reversion |
integer |
0 |
NA |
NA |
The number of time steps after the start of transitioning when individuals revert back to their original property value. The start of transitioning is specified by the Start parameter in Timestep_Restriction. |
{
"Individual_Properties": [{
"Transitions": [{
"Timestep_Restriction": {
"Start": 20,
"Duration": 60
},
"Timesteps_Until_Reversion": 10
}]
}]
}
|
Timestep_Restriction |
JSON object |
NA |
NA |
NA |
The time step when transitioning starts and stops. Required when Type is set to either At_Age or At_Timestep. Start is required and Duration is optional. |
{
"Individual_Properties": [{
"Transitions": [{
"Timestep_Restriction": {
"Start": 20,
"Duration": 60
}
}]
}]
}
|
To |
string |
The property value an individual will transition to. |
{
"Individual_Properties": [{
"Transitions": [{
"From": "Bad",
"To": "Good",
"Coverage": 0.5,
"Type": "At_Timestep"
}]
}]
}
|
|||
Type |
enum |
NA |
NA |
NA |
The type of condition that starts transitioning individuals. Possible values are At_Age or At_Timestep. The other parameters you must set depend on the condition type set here. |
{
"Individual_Properties": [{
"Transitions": [{
"From": "Bad",
"To": "Good",
"Coverage": 0.5,
"Type": "At_Timestep"
}]
}]
}
|
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
}
}]
}
|
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
}
}]
}
|
InitialVectorsPerSpecies |
JSON object |
0 |
2.15e+09 |
10,000 |
The number of vectors per species that will be populated into the node at the beginning of the simulation. Population responds to habitat availability that can be scaled by LarvalHabitatMultiplier. Vector_Sampling_Type in the configuration file must be set to TRACK_ALL_VECTORS or SAMPLE_IND_VECTORS. |
{
"Nodes": [{
"NodeID": 340461476,
"NodeAttributes": {
"InitialVectorsPerSpecies": {
"aegypti": 100,
"funestus" : 0,
"gambiae" : 0
}
}
}]
}
|
LarvalHabitatMultiplier |
float or nested JSON object |
NA |
NA |
NA |
The value by which to scale the larval habitat availability specified in the configuration file with Larval_Habitat_Types across all habitat types, for specific habitat types, or for specific mosquito species within each habitat type. |
The following example scales the larval habitat equally across all habitat types and mosquito species. {
"Defaults": {
"NodeAttributes": {
"LarvalHabitatMultiplier": 2.0
}
}
}
The following example scales the larval habitat only in the temporary rainfall habitat for all mosquito species. {
"Defaults": {
"NodeAttributes": {
"LarvalHabitatMultiplier": {
"TEMPORARY_RAINFALL": 2.0
}
}
}
}
The following example scales the larval habitat independently for A. gambiae in the temporary rainfall habitat and for A. arabiensis in the brackish swamp habitat. {
"Defaults": {
"NodeAttributes": {
"LarvalHabitatMultiplier": {
"TEMPORARY_RAINFALL": {
"gambiae": 2.0
},
"BRACKISH_SWAMP": {
"arabiensis": 2.5
}
}
}
}
}
|
Latitude |
float |
3.40282e+038 |
-3.40282e+038 |
-1 |
Latitude of the node in decimal degrees. This can be used for several things, including determining infectiousness by latitude and defining the size of grid cells. |
{
"Nodes": [{
"NodeID": 25,
"NodeAttributes": {
"Latitude": 12.4,
"Longitude": 9.35
}
}]
}
|
Longitude |
float |
-3.40282e+38 |
3.40282e+38 |
-1 |
Longitude of the node in decimal degrees. This can be used for several things, including defining the size of grid cells. |
{
"Nodes": [{
"NodeID": 254,
"NodeAttributes": {
"Latitude": 25.4,
"Longitude": 9.1
}
}]
}
|
NodeAttributes |
JSON object |
NA |
NA |
NA |
The structure that contains parameters that add or modify information regarding the location, migration, habitat, and population of a simulation. Some NodeAttributes depend on values set in the configuration parameters. |
{
"Nodes": [
{
"NodeID": 1487548419,
"NodeAttributes": {
"Latitude": 12.4208,
"Longitude": 9.15417
}
}
]
}
|
PercentageVectorsBySpecies |
JSON object |
NA |
NA |
NA |
A list of key-value pairs of the vector species and percentage of the total they each make up. |
{
"Defaults": {
"NodeAttributes": {
"PercentageVectorsBySpecies": {
"arabiensis": 0.30,
"funestus": 0.30,
"gambiae": 0.40
}
}
}
}
|
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
}
}]
}
|
Zoonosis |
float |
0 |
1 |
0 |
The daily rate of zoonotic infection per individual. In the configuration file, Animal_Reservoir_Type must be set to ZOONOSIS_FROM_DEMOGRAPHICS to use this value (see General disease parameters). |
{
"Defaults": {
"NodeAttributes": {
"Zoonosis": 0
}
}
}
|
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. |
{
"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 immunity distribution, the meaning of which depends upon the value set in ImmunityDistributionFlag. Enable_Immunity in the configuration file must be set to 1. The table below shows the flag value and corresponding distribution value.
In the configuration file, Enable_Demographics_Risk must be set to 1 (true) and Immunity_Initialization_Distribution_Type must be set to DISTRIBUTION_SIMPLE (see Immunity parameters). |
{
"IndividualAttributes": {
"ImmunityDistributionFlag": 0,
"ImmunityDistribution1": 1,
"ImmunityDistribution2": 0
}
}
|
||||||||||||||||||
SusceptibilityDistribution2 |
float |
-3.40282e+038 |
3.40282e+038 |
0 |
The second value in the distribution, the meaning of which depends upon the value set in ImmunityDistributionFlag. Enable_Immunity in the configuration file must be set to 1. The table below shows the flag value and corresponding distribution value.
In the configuration file, Enable_Demographics_Risk must be set to 1 (true) and Immunity_Initialization_Distribution_Type must be set to DISTRIBUTION_SIMPLE (see Immunity parameters). |
{
"IndividualAttributes": {
"ImmunityDistributionFlag": 0,
"ImmunityDistribution1": 1,
"ImmunityDistribution2": 0
}
}
|
||||||||||||||||||
SusceptibilityDistributionFlag |
integer |
0 |
7 |
0 |
The type of distribution to use for immunity. Enable_Immunity in the configuration file must be set to 1 and Immunity_Initialization_Distribution_Type must be set to DISTRIBUTION_SIMPLE (see Immunity parameters). Possible values are:
|
{
"IndividualAttributes": {
"ImmunityDistributionFlag": 0,
"ImmunityDistribution1": 1,
"ImmunityDistribution2": 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:
[“age”] is the only value accepted for all malaria-specific distributions:
|
{
"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 floats |
0 |
3.40E+3 |
1 |
An array of the scale factors to use to convert the units used for the axes into 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": 0.000002739726027397,
"ResultValues": [
[0, 28.4, 190.3, 222.4, 155.4, 68, 21.9, 3.6, 0],
[0, 28.4, 190.3, 222.4, 155.4, 68, 21.9, 3.6, 0]
]
}
}
}
|
ImmunityDistribution |
JSON object |
NA |
NA |
NA |
The structure defining a complex immunity distribution. Immunity_Initialization_Distribution_Type in the configuration file must be set to DISTRIBUTION_COMPLEX (see Immunity parameters). |
{
"IndividualAttributes": {
"ImmunityDistribution": {
"AxisNames": ["age"],
"AxisUnits": ["years"],
"AxisScaleFactors": [365],
"NumPopulationGroups": [1],
"PopulationGroups": [
[0]
],
"ResultScaleFactor": 3.6952,
"ResultValues": [
[0]
]
}
}
}
|
MortalityDistribution |
JSON object |
NA |
NA |
NA |
The distribution of non-disease mortality for a population. Death_Rate_Dependence in the configuration file must be set to NONDISEASE_MORTALITY_BY_AGE_AND_GENDER or NONDISEASE_MORTALITY_BY_YEAR_AND_AGE_FOR_EACH_GENDER (see Mortality and survival parameters). Warning Mortality is sampled every 30 days. To correctly attribute neonatal deaths to days 0-30, you must indicate that the threshold for the first age group in PopulationGroups is less than 30 days. |
{
"IndividualAttributes": {
"MortalityDistribution": {
"AxisNames": [
"gender", "age"
],
"AxisScaleFactors": [
1, 1
],
"NumDistributionAxes": 2,
"NumPopulationGroups": [
2, 4
],
"PopulationGroups": [
[
0, 1
],
[
0.0, 29.99, 365, 1826
]
],
"ResultScaleFactor": 1,
"ResultValues": [
[
0.0016, 0.000107, 6.3e-05, 100.0
],
[
0.0016, 0.000107, 6.3e-05, 100.0
]
]
}
}
}
|
MSP_mean_antibody_distribution |
JSON object |
NA |
NA |
NA |
The mean of the fraction of the antigenic variants of the anti-MSP antibody that the immune system has been exposed to, binned by age using PopulationGroups. ResultValues are bounded between 0 and 1, typically increasing with age. Immunity_Initialization_Distribution_Type in the configuration file must be set to DISTRIBUTION_COMPLEX. |
{
"IndividualAttributes": {
"MSP_mean_antibody_distribution": {
"AxisNames": [ "age" ],
"AxisUnits": [ "years" ],
"AxisScaleFactors": [ 365 ],
"NumPopulationGroups": [ 11 ],
"PopulationGroups": [
[ 0, 0.5, 1.5, 2.5, 3.5, 4.5, 7.5, 15, 25, 35, 60 ]
],
"ResultUnits": "mean fraction of antibody variants",
"ResultScaleFactor": 1,
"ResultValues": [ 0, 0.0668, 0.1499, 0.2279, 0.2988, 0.3743, 0.5576, 0.8248, 0.8866, 0.8748, 0.8723 ]
}
}
}
|
MSP_variance_antibody_distribution |
JSON object |
NA |
NA |
NA |
The variance of the fraction of the antigenic variants of the anti-MSP antibody that the immune system has been exposed to, binned by age using PopulationGroups. ResultValues are bounded between 0 and 1, typically increasing with age. Immunity_Initialization_Distribution_Type in the configuration file must be set to DISTRIBUTION_COMPLEX. |
{
"IndividualAttributes": {
"MSP_variance_antibody_distribution": {
"AxisNames": [ "age" ],
"AxisUnits": [ "years" ],
"AxisScaleFactors": [ 365 ],
"NumPopulationGroups": [ 11 ],
"PopulationGroups": [
[ 0, 0.5, 1.5, 2.5, 3.5, 4.5, 7.5, 15, 25, 35, 60 ]
],
"ResultUnits": "mean fraction of antibody variants",
"ResultScaleFactor": 1,
"ResultValues": [ 0, 0.0270, 0.0290, 0.0271, 0.0309, 0.0323, 0.0899, 0.0726, 0.0285, 0.0267, 0.0279 ]
}
}
}
|
nonspec_mean_antibody_distribution |
JSON object |
NA |
NA |
NA |
The mean of the fraction of the antigenic variants of non-specific malaria antibodies that the immune system has been exposed to, binned by age using PopulationGroups. ResultValues are bounded between 0 and 1, typically increasing with age. Immunity_Initialization_Distribution_Type in the configuration file must be set to DISTRIBUTION_COMPLEX. |
{
"IndividualAttributes": {
"nonspec_mean_antibody_distribution": {
"AxisNames": [ "age" ],
"AxisUnits": [ "years" ],
"AxisScaleFactors": [ 365 ],
"NumPopulationGroups": [ 11 ],
"PopulationGroups": [
[ 0, 0.5, 1.5, 2.5, 3.5, 4.5, 7.5, 15, 25, 35, 60 ]
],
"ResultUnits": "mean fraction of antibody variants",
"ResultScaleFactor": 1,
"ResultValues": [ 0, 0.2720, 0.5547, 0.7226, 0.8429, 0.9186, 0.9782, 0.9991, 1.0000, 1.0000, 1.0000 ]
}
}
}
|
nonspec_variance_antibody_distribution |
JSON object |
NA |
NA |
NA |
The variance of the fraction of the antigenic variants of non-specific malaria antibodies that the immune system has been exposed to, binned by age using PopulationGroups. ResultValues are bounded between 0 and 1, typically increasing with age. Immunity_Initialization_Distribution_Type in the configuration file must be set to DISTRIBUTION_COMPLEX. |
{
"IndividualAttributes": {
"nonspec_variance_antibody_distribution": {
"AxisNames": [ "age" ],
"AxisUnits": [ "years" ],
"AxisScaleFactors": [ 365 ],
"NumPopulationGroups": [ 11 ],
"PopulationGroups": [
[ 0, 0.5, 1.5, 2.5, 3.5, 4.5, 7.5, 15, 25, 35, 60 ]
],
"ResultUnits": "mean fraction of antibody variants",
"ResultScaleFactor": 1,
"ResultValues": [ 0, 0.1146, 0.1008, 0.0795, 0.0781, 0.0550, 0.0337, 0.0067, 0, 0, 0 ]
}
}
}
|
NumDistributionAxes |
integer |
1 |
NA |
NA |
The number of axes to use for a complex distribution. EMOD does not use this value; it is only informational. |
{
"IndividualAttributes": {
"MortalityDistribution": {
"NumDistributionAxes": 2,
"AxisNames": ["gender", "age"],
"AxisScaleFactors": [1, 365]
}
}
}
|
NumPopulationGroups |
array of integers |
NA |
NA |
NA |
An array of population groupings for each independent variable for a complex distribution. This variable defines the number of columns for each row in the population group table. The number of values in the array is often two, representing the values for gender and number of age bins. EMOD does not use this value; it is only informational. |
{
"IndividualAttributes": {
"MortalityDistribution": {
"AxisNames": ["gender", "age"],
"AxisUnits": ["male=0,female=1", "years"],
"AxisScaleFactors": [1, 365],
"NumPopulationGroups": [2, 1],
"PopulationGroups": [
[0, 1],
[0]
],
"ResultUnits": "annual deaths per 1000 individuals",
"ResultScaleFactor": 2.739726027397e-06,
"ResultValues": [
[0],
[0]
]
}
}
}
|
PfEMP1_mean_antibody_distribution |
JSON object |
NA |
NA |
NA |
The mean of the fraction of the antigenic variants of the PfEMP1 antibody that the immune system has been exposed to, binned by age using PopulationGroups. ResultValues are bounded between 0 and 1, typically increasing with age. Immunity_Initialization_Distribution_Type in the configuration file must be set to DISTRIBUTION_COMPLEX. |
{
"IndividualAttributes": {
"PfEMP1_mean_antibody_distribution": {
"NumDistributionAxes": 1,
"AxisNames": [ "age" ],
"AxisUnits": [ "years" ],
"AxisScaleFactors": [ 365 ],
"NumPopulationGroups": [ 11 ],
"PopulationGroups": [
[ 0, 0.5, 1.5, 2.5, 3.5, 4.5, 7.5, 15, 25, 35, 60 ]
],
"ResultUnits": "mean fraction of antibody variants",
"ResultScaleFactor": 1,
"ResultValues": [ 0, 0.1265, 0.3071, 0.4547, 0.5680, 0.6627, 0.8090, 0.9405, 0.9772, 0.9796, 0.9801 ]
}
}
}
|
PfEMP1_variance_antibody_distribution |
JSON object |
NA |
NA |
NA |
The variance of the fraction of the antigenic variants of the PfEMP1 antibody that the immune system has been exposed to, binned by age using PopulationGroups. ResultValues are bounded between 0 and 1, typically increasing with age. Immunity_Initialization_Distribution_Type in the configuration file must be set to DISTRIBUTION_COMPLEX. |
{
"IndividualAttributes": {
"PfEMP1_variance_antibody_distribution": {
"AxisNames": [ "age" ],
"AxisUnits": [ "years" ],
"AxisScaleFactors": [ 365 ],
"NumPopulationGroups": [ 11 ],
"PopulationGroups": [
[ 0, 0.5, 1.5, 2.5, 3.5, 4.5, 7.5, 15, 25, 35, 60 ]
],
"ResultUnits": "mean fraction of antibody variants",
"ResultScaleFactor": 1,
"ResultValues": [ 0, 0.0606, 0.0509, 0.0386, 0.0334, 0.0274, 0.0575, 0.0261, 0.0050, 0.0045, 0.0044 ]
}
}
}
|
PopulationGroups |
matrix of integers |
NA |
NA |
NA |
An array in which each row represents one of the distribution axes and contains the values that the independent variable can take. The values must be listed in ascending order and each defines the left edge of the bin. Warning Mortality is sampled every 30 days. To correctly attribute neonatal deaths to days 0-30, you must indicate that the threshold for the first age group in PopulationGroups is less than 30 days. |
The following example configures relatively high infant mortality and lower mortality at ages 10 and 40, with everyone dead by age 120. {
"IndividualAttributes": {
"MortalityDistribution": {
"AxisNames": ["gender", "age"],
"AxisUnits": ["male=0,female=1", "years"],
"AxisScaleFactors": [1, 365],
"NumPopulationGroups": [2, 1],
"PopulationGroups": [
[0, 1],
[0, 10, 40, 120]
],
"ResultUnits": "annual deaths per 1000 individuals",
"ResultScaleFactor": 2.739726027397e-06,
"ResultValues": [
[51.6, 3.7, 5.3, 1000],
[60.1, 4.1, 4.8, 1000]
]
}
}
}
|
ResultScaleFactor |
float |
-3.40282e+038 |
3.40282e+038 |
1 |
The scale factor used to convert ResultUnits to number of births, deaths, or another variable per individual per day. |
{
"IndividualAttributes": {
"AgeDistribution": {
"AxisScaleFactors": 1,
"DistributionValues": [
0.99,
1.0
],
"ResultScaleFactor": 365,
"ResultUnits": "years",
"ResultValues": [
0.0027,
0.0027
]
}
}
}
|
ResultUnits |
string |
NA |
NA |
NA |
A string that indicates the units used for the ResultValues parameter of a complex distribution. EMOD does not use this value; it is only informational. The values here are scaled by the value in ResultScaleFactor before being passed to EMOD as a daily rate. |
{
"IndividualAttributes": {
"MortalityDistribution": {
"NumPopulationGroups": [2, 1],
"PopulationGroups": [
[0, 1],
[0]
],
"ResultUnits": "annual deaths per 1000 individuals",
"ResultScaleFactor": 2.739726027397e-06,
"ResultValues": [
[0],
[0]
]
}
}
}
|
ResultValues |
array of floats |
NA |
NA |
NA |
An array in which each row represents one of the distribution axes and contains the dependent variable values. The units are configurable; the values are scaled by the value in ResultScaleFactor before being passed to EMOD in units of days. For age distributions, it lists in ascending order the ages at which to bin the population. The corresponding values in DistributionValues represent the proportion of the population that is below that age. If the first member of the array is non-zero, the first bin is defined as those with that exact value (EMOD does not assume the bins start at zero). For all other distributions, an array in which each row represents the values for a combination of axes. For example, a mortality distribution that includes both gender and age axes will have a row for males and a row for females that each contain the mortality rate at various ages set in PopulationGroups. |
The following example shows an age distribution in which 10% of individuals are newborn, 25% are under age 5, 50% are between 5 and 20, and 25% are between 20 and 35. {
"IndividualAttributes": {
"AgeDistribution": {
"DistributionValues": [0.1, 0.25, 0.75, 1],
"ResultValues": [0, 5, 20, 35]
}
}
}
The following example configures relatively high infant mortality and lower mortality at ages 10 and 40, with everyone dead by age 120. {
"IndividualAttributes": {
"MortalityDistribution": {
"AxisNames": ["gender", "age"],
"AxisUnits": ["male=0,female=1", "years"],
"AxisScaleFactors": [1, 365],
"NumPopulationGroups": [2, 1],
"PopulationGroups": [
[0, 1],
[0, 10, 40, 120]
],
"ResultUnits": "annual deaths per 1000 individuals",
"ResultScaleFactor": 2.739726027397e-06,
"ResultValues": [
[51.6, 3.7, 5.3, 1000],
[60.1, 4.1, 4.8, 1000]
]
}
}
}
|
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 tables below contain only parameters available when using the malaria simulation type. Some parameters may appear in multiple categories.
Note
Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
Contents
Drugs and treatments¶
The following parameters determine the efficacy of drugs and other treatments.
For more information on the drugs used to treat malaria, see Antimalarial drugs.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Bodyweight_Exponent |
float |
0 |
100000 |
0 |
The effect of bodyweight on maximum drug concentration in an individual patient. Drug_Cmax is divided by patient bodyweight raised to the power of Bodyweight_Exponent to account for the influence of body size on volume of distribution. |
{
"Malaria_Drug_Params": {
"Artemether_Lumefantrine": {
"Bodyweight_Exponent": 2
}
}
}
|
Drug_Cmax |
float |
0 |
100000 |
1000 |
The maximum drug concentration used. Note This parameter and the Drug_PKPD_C50 parameter must use the same units. |
{
"Malaria_Drug_Params": {
"Artemether_Lumefantrine": {
"Drug_Cmax": 1000,
"Drug_PKPD_C50": 100
}
}
}
|
Drug_Decay_T1 |
float |
0 |
100000 |
1 |
The primary drug decay rate, in days. |
{
"Malaria_Drug_Params": {
"Artemisinin": {
"Drug_Decay_T1": 1,
"Drug_Decay_T2": 1
}
}
}
|
Drug_Decay_T2 |
float |
0 |
100000 |
1 |
The secondary drug decay rate, in days. |
{
"Malaria_Drug_Params": {
"Artemisinin": {
"Drug_Decay_T1": 1,
"Drug_Decay_T2": 1
}
}
}
|
Drug_Dose_Interval |
float |
0 |
100000 |
1 |
The interval between doses of drugs, in days. |
{
"Malaria_Drug_Params": {
"Artemisinin": {
"Drug_Dose_Interval": 1
}
}
}
|
Drug_Fulltreatment_Doses |
float |
1 |
100000 |
3 |
The number of doses for a full treatment of a drug. |
{
"Malaria_Drug_Params": {
"Chloroquine": {
"Drug_Fulltreatment_Doses": 3
}
}
}
|
Drug_Gametocyte02_Killrate |
float |
0 |
100000 |
0 |
The log reduction per day in early-stage gametocytes. |
{
"Malaria_Drug_Params": {
"Chloroquine": {
"Drug_Gametocyte02_Killrate": 0,
"Drug_Gametocyte34_Killrate": 0,
"Drug_GametocyteM_Killrate": 0
}
}
}
|
Drug_Gametocyte34_Killrate |
float |
0 |
100000 |
0 |
The log reduction per day in late-stage gametocytes. |
{
"Malaria_Drug_Params": {
"Chloroquine": {
"Drug_Gametocyte02_Killrate": 0,
"Drug_Gametocyte34_Killrate": 0,
"Drug_GametocyteM_Killrate": 0
}
}
}
|
Drug_GametocyteM_Killrate |
float |
0 |
100000 |
0 |
The log reduction per day in mature gametocyte numbers at saturated drug concentrations. |
{
"Malaria_Drug_Params": {
"Chloroquine": {
"Drug_Gametocyte02_Killrate": 0,
"Drug_Gametocyte34_Killrate": 0,
"Drug_GametocyteM_Killrate": 0
}
}
}
|
Drug_Hepatocyte_Killrate |
float |
0 |
100000 |
0 |
The log reduction in hepatocyte numbers per day. |
{
"Malaria_Drug_Params": {
"Chloroquine": {
"Drug_Hepatocyte_Killrate": 0
}
}
}
|
Drug_PKPD_C50 |
float |
0 |
100000 |
100 |
The concentration at which drug killing rates are half of the maximum. Note This parameter and the Drug_Cmax parameter must use the same units. |
{
"Malaria_Drug_Params": {
"Artemether_Lumefantrine": {
"Drug_Cmax": 1000,
"Drug_PKPD_C50": 100
}
}
}
|
Drug_Vd |
float |
0 |
100000 |
10 |
The volume of drug distribution in a pharmacokinetic two compartment model. The first compartment comprises central organs and tissues and the second compartment comprises peripheral tissues. This value is the ratio of the volume of the second compartment to that of the first. |
{
"Malaria_Drug_Params": {
"Chloroquine": {
"Drug_Vd": 3
}
}
}
|
Fractional_Dose_By_Upper_Age |
array of JSON objects |
NA |
NA |
NA |
An array to specify the fraction of the adult dose to use for children at various ages. Contains Upper_Age_In_Years and Fraction_Of_Adult_Dose values. |
{
"Malaria_Drug_Params": {
"Artemether": {
"Fractional_Dose_By_Upper_Age": [{
"Fraction_Of_Adult_Dose": 0.25,
"Upper_Age_In_Years": 3
}, {
"Fraction_Of_Adult_Dose": 0.5,
"Upper_Age_In_Years": 6
}, {
"Fraction_Of_Adult_Dose": 0.75,
"Upper_Age_In_Years": 10
}]
}
}
}
|
Fraction_Of_Adult_Dose |
float |
0 |
1 |
NA |
The fraction of the adult drug dose given to children below the age defined in Upper_Age_In_Years. Set in the Fractional_Dose_By_Upper_Age array. |
{
"Malaria_Drug_Params": {
"Artemether": {
"Fractional_Dose_By_Upper_Age": [{
"Fraction_Of_Adult_Dose": 0.25,
"Upper_Age_In_Years": 3
}, {
"Fraction_Of_Adult_Dose": 0.5,
"Upper_Age_In_Years": 6
}, {
"Fraction_Of_Adult_Dose": 0.75,
"Upper_Age_In_Years": 10
}]
}
}
}
|
Genome_Markers |
array of strings |
NA |
NA |
NA |
A list of the names (strings) of genome marker(s) that represent the genetic components in a strain of an infection. |
{
"Genome_Markers": [
"D6",
"W2"
]
}
|
Malaria_Drug_Params |
JSON object |
NA |
NA |
NA |
This JSON structure contains the names of anti-malarial drugs and the parameters that define them. |
{
"Malaria_Drug_Params": {
"Artemether_Lumefantrine": {
"Bodyweight_Exponent": 0,
"Drug_Cmax": 1000,
"Drug_Decay_T1": 1,
"Drug_Decay_T2": 1,
"Drug_Dose_Interval": 1,
"Drug_Fulltreatment_Doses": 3,
"Drug_Gametocyte02_Killrate": 2.3,
"Drug_Gametocyte34_Killrate": 2.3,
"Drug_GametocyteM_Killrate": 0,
"Drug_Hepatocyte_Killrate": 0,
"Drug_PKPD_C50": 100,
"Drug_Vd": 10,
"Max_Drug_IRBC_Kill": 4.6
},
"Artemisinin": {
"Bodyweight_Exponent": 0,
"Drug_Cmax": 1000,
"Drug_Decay_T1": 1,
"Drug_Decay_T2": 1,
"Drug_Dose_Interval": 1,
"Drug_Fulltreatment_Doses": 3,
"Drug_Gametocyte02_Killrate": 2.3,
"Drug_Gametocyte34_Killrate": 2.3,
"Drug_GametocyteM_Killrate": 0,
"Drug_Hepatocyte_Killrate": 0,
"Drug_PKPD_C50": 100,
"Drug_Vd": 10,
"Max_Drug_IRBC_Kill": 4.61
}
}
}
|
Max_Drug_IRBC_Kill |
float |
5 |
100000 |
5 |
The maximum log reduction in IRBCs per day due to treatment. |
{
"Malaria_Drug_Params": {
"Artemether_Lumefantrine": {
"Bodyweight_Exponent": 0,
"Drug_Cmax": 1000,
"Drug_Decay_T1": 1,
"Drug_Decay_T2": 1,
"Drug_Dose_Interval": 1,
"Drug_Fulltreatment_Doses": 3,
"Drug_Gametocyte02_Killrate": 2.3,
"Drug_Gametocyte34_Killrate": 2.3,
"Drug_GametocyteM_Killrate": 0,
"Drug_Hepatocyte_Killrate": 0,
"Drug_PKPD_C50": 100,
"Drug_Vd": 10,
"Max_Drug_IRBC_Kill": 4.61
}
}
}
|
PKPD_Model |
enum |
NA |
NA |
FIXED_DURATION_CONSTANT_EFFECT |
Determines which pharmacokinetic pharmacodynamic model to use. Possible values are:
|
{
"PKPD_Model": "FIXED_DURATION_CONSTANT_EFFECT"
}
|
Resistance |
JSON object |
NA |
NA |
NA |
Specifies the drug resistance multiplier. This parameter is used with the infection strain indicated in the Genome_Markers parameter. The value specified for the parameter modifier, which is part of the Resistance JSON object, is multiplied with associated parameter. If the infection strain contains different markers, then the same modifiers for each marker are multiplied together before being multiplied with the associated parameter. |
{
"parameters": {
"Genome_Markers": [
"A",
"B"
],
"Malaria_Drug_Params": {
"Artemether_Lumefantrine": {
"Bodyweight_Exponent": 0,
"Drug_Cmax": 1000,
"Drug_Decay_T1": 1,
"Drug_Decay_T2": 1,
"Drug_Dose_Interval": 1,
"Drug_Fulltreatment_Doses": 3,
"Drug_Gametocyte02_Killrate": 2.3,
"Drug_Gametocyte34_Killrate": 2.3,
"Drug_GametocyteM_Killrate": 0,
"Drug_Hepatocyte_Killrate": 0,
"Drug_PKPD_C50": 100,
"Drug_Vd": 10,
"Max_Drug_IRBC_Kill": 3.45
"Resistance": {
"A": {
"Max_IRBC_Kill_Modifier": 0.05,
"PKPD_C50_Modifier": 1.0
},
"B": {
"Max_IRBC_Kill_Modifier": 0.25,
"PKPD_C50_Modifier": 1.0
}
}
}
}
}
}
|
Upper_Age_In_Years |
float |
0 |
125 |
NA |
The age, in years, below which children are given a fraction of the adult drug dose, as defined in Fraction_Of_Adult_Dose. Note that this parameter can be specified for each drug included in the configuration file, and different fractional doses may be used for different age groups. |
{
"Malaria_Drug_Params": {
"Artemether": {
"Fractional_Dose_By_Upper_Age": [{
"Fraction_Of_Adult_Dose": 0.25,
"Upper_Age_In_Years": 3
}, {
"Fraction_Of_Adult_Dose": 0.5,
"Upper_Age_In_Years": 6
}, {
"Fraction_Of_Adult_Dose": 0.75,
"Upper_Age_In_Years": 10
}]
}
}
}
|
Enable or disable features¶
The following parameters enable or disable features of the model, such as allowing births, deaths, or aging. Set to false (0) to disable; set to true (1) to enable.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Enable_Aging |
boolean |
0 |
1 |
1 |
Controls whether or not individuals in a population age during the simulation. Enable_Vital_Dynamics must be set to true (1). |
{
"Enable_Vital_Dynamics": 1,
"Enable_Aging": 1
}
|
Enable_Air_Migration |
boolean |
0 |
1 |
0 |
Controls whether or not migration by air travel will occur. Migration_Model must be set to FIXED_RATE_MIGRATION. |
{
"Migration_Model": "FIXED_RATE_MIGRATION",
"Enable_Air_Migration": 1,
"Air_Migration_Filename": "../inputs/air_migration.bin"
}
|
Enable_Birth |
boolean |
0 |
1 |
1 |
Controls whether or not individuals will be added to the simulation by birth. Enable_Vital_Dynamics must be set to true (1). If you want new individuals to have the same intervention coverage as existing individuals, you must add a BirthTriggeredIV to the campaign file. |
{
"Enable_Vital_Dynamics": 1,
"Enable_Birth": 1
}
|
Enable_Climate_Stochasticity |
boolean |
0 |
1 |
0 |
Controls whether or not the climate has stochasticity. Climate_Model must be set to CLIMATE_CONSTANT or CLIMATE_BY_DATA. Set the variance using the parameters Air_Temperature_Variance, Land_Temperature_Variance, Enable_Rainfall_Stochasticity, and Relative_Humidity_Variance. |
{
"Climate_Model": "CLIMATE_BY_DATA",
"Enable_Climate_Stochasticity": 1,
"Air_Temperature_Variance": 2,
"Enable_Rainfall_Stochasticity": 1,
"Land_Temperature_Variance": 2,
"Relative_Humidity_Variance": 0.05
}
|
Enable_Default_Reporting |
boolean |
0 |
1 |
1 |
Controls whether or not the default InsetChart.json report is created. |
{
"Enable_Default_Reporting": 1
}
|
Enable_Demographics_Birth |
boolean |
0 |
1 |
0 |
Controls whether or not newborns have identical or heterogeneous characteristics. Set to false (0) to give all newborns identical characteristics; set to true (1) to allow for heterogeneity in traits such as sickle-cell status. Enable_Birth must be set to true (1). |
{
"Enable_Birth": 1,
"Enable_Demographics_Birth": 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_Gender |
boolean |
0 |
1 |
1 |
Controls whether or not gender ratios are drawn from a Gaussian distribution or a 50/50 draw. When set to 1 (true), the ratio of males to females is drawn from a Gaussian distribution with a mean of 0.5 and a standard deviation of 0.01. When set to 0 (false), the gender ratio is based on a 50/50 draw. |
{
"Enable_Demographics_Gender": 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_Demographics_Risk |
boolean |
0 |
1 |
0 |
Controls whether or not the simulation includes the impact of disease risk in demographics. This parameter is not used in this simulation type. |
{
"Enable_Demographics_Risk": 1
}
|
Enable_Disease_Mortality |
boolean |
0 |
1 |
1 |
Controls whether or not individuals are removed from the simulation due to disease deaths. This parameter is not used with this simulation type. |
{
"Enable_Disease_Mortality": 1
}
|
Enable_Egg_Mortality |
boolean |
0 |
1 |
0 |
Controls whether or not to include a daily mortality rate on the egg population, which is independent of climatic factors. |
{
"Enable_Egg_Mortality": 1,
}
|
Enable_Family_Migration |
boolean |
0 |
1 |
0 |
Controls whether or not all members of a household can migrate together when a MigrateFamily event occurs. All residents must be home before they can leave on the trip. Migration_Model must be set to FIXED_RATE_MIGRATION. |
{
"Enable_Migration": "FIXED_RATE_MIGRATION",
"Enable_Family_Migration": 1,
"Family_Migration_Filename": "../inputs/family_migration.bin"
}
|
Enable_Heterogeneous_Intranode_Transmission |
boolean |
0 |
1 |
0 |
Controls whether or not individuals experience heterogeneous disease transmission within a node. When set to true (1), individual property definitions and the \(\beta\) matrix must be specified in the demographics file (see NodeProperties and IndividualProperties parameters). The \(\beta\) values are multiplied with the \(\beta\) 0 value configured by Base_Infectivity. This is used only in generic 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). This parameter should always be set to 1 (on) in this simulatin type. |
{
"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. This parameter should always be set to 1 (on) for this simulation type. |
{
"Enable_Immunity": 1
}
|
Enable_Immunity_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_Immunity_Distribution": 1,
"Immunity_Initialization_Distribution_Type": "DISTRIBUTION_SIMPLE"
}
|
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. This parameter is not used in this simulation type. |
{
"Enable_Initial_Prevalence": 1
}
|
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). This parameter is not used in this simulation type. |
{
"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. Set to true (1) to use a migration rate distribution in the demographics file (see NodeAttributes parameters); set to false (0) to use the same migration rate applied to all individuals. Migration_Model must be set to FIXED_RATE_MIGRATION. |
{
"Migration_Model": "FIXED_RATE_MIGRATION",
"Enable_Migration_Heterogeneity": 1
}
|
Enable_Natural_Mortality |
boolean |
0 |
1 |
0 |
Controls whether or not individuals are removed from the simulation due to natural (non-disease) deaths. Enable_Vital_Dynamics must be set to 1 (true). Use Death_Rate_Dependence to set the natural death rate. |
{
"Enable_Natural_Mortality": 1,
"Enable_Vital_Dynamics": 1
}
|
Enable_Property_Output |
boolean |
0 |
1 |
0 |
Controls whether or not to create property output reports, which detail groups as defined in IndividualProperties in the demographics file (see NodeProperties and IndividualProperties parameters). When there is more than one property type, the report will display the channel information for all combinations of the property type groups. |
{
"Enable_Property_Output": 1
}
|
Enable_Rainfall_Stochasticity |
boolean |
0 |
1 |
1 |
Controls whether or not there is stochastic variation in rainfall; set to true (1) for stochastic variation of rainfall that is drawn from an exponential distribution (with a mean value as the daily rainfall from the Climate_Model values CLIMATE_CONSTANT or CLIMATE_BY_DATA), or set to false (0) to disable rainfall stochasticity. |
{
"Enable_Climate_Stochasticity": 1,
"Air_Temperature_Variance": 2,
"Enable_Rainfall_Stochasticity": 1,
"Land_Temperature_Variance": 2,
"Relative_Humidity_Variance": 0.05
}
|
Enable_Regional_Migration |
boolean |
0 |
1 |
0 |
Controls whether or not there is migration by road vehicle into and out of nodes in the road network. 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_Sexual_Combination |
boolean |
0 |
1 |
0 |
Controls whether or not male and female gametocytes undergo sexual combination. Set to true (1) to allow for sexual combination; set to false (0) for transmission of each strain only. Note This parameter is currently not in use. |
{
"Enable_Sexual_Combination": 0
}
|
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_Superinfection |
boolean |
0 |
1 |
0 |
Controls whether or not an individual can have multiple infections simultaneously. Set to true (1) to allow for multiple simultaneous infections; set to false (0) if multiple infections are not possible. Set the Max_Individual_Infections parameter. |
{
"Enable_Superinfection": 1,
"Max_Individual_Infections": 2
}
|
Enable_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"
}
|
Enable_Vector_Aging |
boolean |
0 |
1 |
0 |
Controls whether or not vectors undergo senescence as they age. |
{
"Enable_Vector_Aging": 1
}
|
Enable_Vector_Migration |
boolean |
0 |
1 |
0 |
Controls whether or not vectors can migrate. Vector_Sampling_Type must be set to TRACK_ALL_VECTORS or SAMPLE_IND_VECTORS. Specific migration types must be enabled or disabled. |
{
"Vector_Sampling_Type": "TRACK_ALL_VECTORS",
"Enable_Vector_Migration": 1,
"Enable_Vector_Migration_Local": 1,
"Vector_Migration_Filename_Local": "../inputs/vector_local.bin"
}
|
Enable_Vector_Migration_Local |
boolean |
0 |
1 |
0 |
Controls whether or not vectors may migrate to adjacent nodes. Enable_Vector_Migration must be set to true (1). |
{
"Vector_Sampling_Type": "TRACK_ALL_VECTORS",
"Enable_Vector_Migration": 1,
"Enable_Vector_Migration_Local": 1,
"Vector_Migration_Filename_Local": "../inputs/vector_local.bin"
}
|
Enable_Vector_Migration_Regional |
boolean |
0 |
1 |
0 |
Controls whether or not vectors can migration to non-adjacent nodes. Enable_Vector_Migration must be set to true (1). |
{
"Vector_Sampling_Type": "TRACK_ALL_VECTORS",
"Enable_Vector_Migration": 1,
"Enable_Vector_Migration_Regional": 1,
"Vector_Migration_Filename_Regional": "../inputs/vector_regional.bin"
}
|
Enable_Vector_Mortality |
boolean |
0 |
1 |
1 |
Controls whether or not vectors can die. Vector_Sampling_Type must be set to TRACK_ALL_VECTORS. |
{
"Vector_Sampling_Type": "TRACK_ALL_VECTORS",
"Enable_Vector_Mortality": 1
}
|
Enable_Vital_Dynamics |
boolean |
0 |
1 |
1 |
Controls whether or not births and deaths occur in the simulation. Births and deaths must be individually enabled and set. |
{
"Enable_Vital_Dynamics":1,
"Enable_Birth": 1,
"Death_Rate_Dependence": "NONDISEASE_MORTALITY_OFF",
"Base_Mortality": 0.002
}
|
General disease¶
The following parameters determine general disease characteristics.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Animal_Reservoir_Type |
enum |
NA |
NA |
NO_ZOONOSIS |
The type of animal reservoir and configuration for zoonosis. Use of the animal reservoir sets a low constant baseline of infectivity beyond what is present in the human population. It allows a more random introduction of cases in continuous time, which is more applicable for various situations such as zoonosis. Possible values are:
|
{
"Animal_Reservoir_Type": "CONSTANT_ZOONOSIS"
}
|
Number_Basestrains |
integer |
1 |
10 |
1 |
The number of base strains in the simulation, such as antigenic variants. The specific base strain of an outbreak is specified using Antigen in the campaign file. |
{
"Number_Basestrains": 1
}
|
Number_Substrains |
integer |
1 |
16777200 |
256 |
The number of disease substrains for each base strain, such as genetic variants. The specific substrain of an outbreak is specified using Genome in the campaign file. |
{
"Number_Substrains": 16777216
}
|
Zoonosis_Rate |
float |
0 |
1 |
0 |
The daily rate of zoonotic infection per individual. Animal_Reservoir_Type must be set to CONSTANT_ZOONOSIS or ZOONOSIS_FROM_DEMOGRAPHICS. If Animal_Reservoir_Type is set to ZOONOSIS_FROM_DEMOGRAPHICS, the value for the Zoonosis NodeAttribute in the demographics file will override the value set for Zoonosis_Rate. |
{
"Zoonosis_Rate": 0.005
}
|
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.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Air_Temperature_Filename |
string |
NA |
NA |
air_temp.json |
The path to the input data file that defines air temperature data measured two meters above ground. Climate_Model must be set to CLIMATE_BY_DATA. The file must be in .bin format. |
{
"Climate_Model": "CLIMATE_BY_DATA",
"Air_Temperature_Filename": "Namawala_single_node_air_temperature_daily.bin"
}
|
Air_Temperature_Offset |
float |
-20 |
20 |
0 |
The linear shift of air temperature in degrees Celsius. Climate_Model must be set to CLIMATE_BY_DATA. |
{
"Air_Temperature_Offset": 1
}
|
Air_Temperature_Variance |
float |
0 |
5 |
2 |
The standard deviation (in degrees Celsius) for normally distributed noise applied to the daily air temperature values when Climate_Model is configured as CLIMATE_CONSTANT or CLIMATE_BY_DATA. Enable_Climate_Stochasticity must be set to true (1). |
{
"Enable_Climate_Stochasticity": 1,
"Air_Temperature_Variance": 2,
}
|
Base_Air_Temperature |
float |
-55 |
45 |
22 |
The air temperature, in degrees Celsius, where Climate_Model is set to CLIMATE_CONSTANT. |
{
"Climate_Model": "CLIMATE_CONSTANT",
"Base_Air_Temperature": 30
}
|
Base_Land_Temperature |
float |
-55 |
60 |
26 |
The land temperature, in degrees Celsius, where Climate_Model is set to CLIMATE_CONSTANT. |
{
"Climate_Model": "CLIMATE_CONSTANT",
"Base_Land_Temperature": 20
}
|
Base_Rainfall |
float |
0 |
150 |
10 |
The value of rainfall per day in millimeters when Climate_Model is set to CLIMATE_CONSTANT. |
{
"Climate_Model": "CLIMATE_CONSTANT",
"Base_Rainfall": 20
}
|
Base_Relative_Humidity |
float |
0 |
1 |
0.75 |
The value of humidity where Climate_Model is set to CLIMATE_CONSTANT. |
{
"Base_Relative_Humidity": 0.1
}
|
Climate_Model |
enum |
NA |
NA |
CLIMATE_OFF |
How and from what files the climate of a simulation is configured. The possible values are:
|
{
"Climate_Model": "CLIMATE_CONSTANT"
}
|
Climate_Update_Resolution |
enum |
NA |
NA |
CLIMATE_UPDATE_YEAR |
The resolution of data in climate files. Climate_Model must be set to CLIMATE_CONSTANT, CLIMATE_BY_DATA, or CLIMATE_KOPPEN. Possible values are: CLIMATE_UPDATE_YEAR CLIMATE_UPDATE_MONTH CLIMATE_UPDATE_WEEK CLIMATE_UPDATE_DAY CLIMATE_UPDATE_HOUR |
{
"Climate_Update_Resolution": "CLIMATE_UPDATE_DAY"
}
|
Default_Geography_Initial_Node_Population |
integer |
0 |
1000000 |
1000 |
When using the built-in demographics for default geography, the initial number of individuals in each node. Note that the built-in demographics feature does not represent a real geographical location and is mostly used for testing. Enable_Demographics_Builtin must be set to true (1). |
{
"Enable_Demographics_Builtin": 1,
"Default_Geography_Initial_Node_Population": 1000,
"Default_Geography_Torus_Size": 3
}
|
Default_Geography_Torus_Size |
integer |
3 |
100 |
10 |
When using the built-in demographics for default geography, the square root of the number of nodes in the simulation. The simulation uses an N x N square grid of nodes with N specified by this parameter. If migration is enabled, the N x N nodes are assumed to be a torus and individuals can migrate from any node to all four adjacent nodes. To enable migration, set Migration_Model to FIXED_RATE_MIGRATION. Built-in migration is a form of “local” migration where individuals only migrate to the adjacent nodes. You can use the x_Local_Migration parameter to control the rate of migration. The other migration parameters are ignored. Note that the built-in demographics feature does not represent a real geographical location and is mostly used for testing. Enable_Demographics_Builtin must be set to true (1). |
{
"Enable_Demographics_Builtin": 1,
"Default_Geography_Initial_Node_Population": 1000,
"Default_Geography_Torus_Size": 3
}
|
Enable_Climate_Stochasticity |
boolean |
0 |
1 |
0 |
Controls whether or not the climate has stochasticity. Climate_Model must be set to CLIMATE_CONSTANT or CLIMATE_BY_DATA. Set the variance using the parameters Air_Temperature_Variance, Land_Temperature_Variance, Enable_Rainfall_Stochasticity, and Relative_Humidity_Variance. |
{
"Climate_Model": "CLIMATE_BY_DATA",
"Enable_Climate_Stochasticity": 1,
"Air_Temperature_Variance": 2,
"Enable_Rainfall_Stochasticity": 1,
"Land_Temperature_Variance": 2,
"Relative_Humidity_Variance": 0.05
}
|
Enable_Rainfall_Stochasticity |
boolean |
0 |
1 |
1 |
Controls whether or not there is stochastic variation in rainfall; set to true (1) for stochastic variation of rainfall that is drawn from an exponential distribution (with a mean value as the daily rainfall from the Climate_Model values CLIMATE_CONSTANT or CLIMATE_BY_DATA), or set to false (0) to disable rainfall stochasticity. |
{
"Enable_Climate_Stochasticity": 1,
"Air_Temperature_Variance": 2,
"Enable_Rainfall_Stochasticity": 1,
"Land_Temperature_Variance": 2,
"Relative_Humidity_Variance": 0.05
}
|
Koppen_Filename |
string |
NA |
NA |
koppen_climate.json |
The path to the input file used to specify Koppen climate classifications. Climate_Model must be set to CLIMATE_KOPPEN. |
{
"Koppen_Filename": "Mad_2_5arcminute_koppen.dat"
}
|
Land_Temperature_Filename |
string |
NA |
NA |
land_temp.json |
The path of the input file defining temperature data measured at land surface; used only when Climate_Model is set to CLIMATE_BY_DATA. The file must be in .bin format. |
{
"Land_Temperature_Filename": "Namawala_single_node_land_temperature_daily.bin"
}
|
Land_Temperature_Offset |
float |
-20 |
20 |
0 |
The linear shift of land surface temperature in degrees Celsius; only used when Climate_Model is set to CLIMATE_BY_DATA. |
{
"Land_Temperature_Offset": 0
}
|
Land_Temperature_Variance |
float |
0 |
7 |
2 |
The standard deviation (in degrees Celsius) for normally distributed noise applied to the daily land temperature values when Climate_Model is configured to CLIMATE_CONSTANT or CLIMATE_BY_DATA; only used if the Enable_Climate_Stochasticity is set to true (1). |
{
"Land_Temperature_Variance": 1.5
}
|
Node_Grid_Size |
float |
0.00416 |
90 |
0.004167 |
The spatial resolution indicating the node grid size for a simulation in degrees. |
{
"Node_Grid_Size": 0.042
}
|
Rainfall_Filename |
string |
NA |
NA |
rainfall.json |
The path of the input file which defines rainfall data. Climate_Model must be set to CLIMATE_BY_DATA. The file must be in .bin format. |
{
"Rainfall_Filename": "Namawala_single_node_rainfall_daily.bin"
}
|
Rainfall_In_mm_To_Fill_Swamp |
float |
1 |
10000 |
1000 |
Millimeters of rain to fill larval habitat to capacity. This is only used for vector species with Larval_Habitat_Types set to BRACKISH_SWAMP. |
{
"Rainfall_In_mm_To_Fill_Swamp": 1000.0
}
|
Rainfall_Scale_Factor |
float |
0.1 |
10 |
1 |
The scale factor used in multiplying rainfall value(s). Climate_Model must be set to CLIMATE_BY_DATA. |
{
"Rainfall_Scale_Factor": 1
}
|
Relative_Humidity_Filename |
string |
NA |
NA |
rel_hum.json |
The path of the input file which defines relative humidity data measured 2 meters above ground. Climate_Model must be set to CLIMATE_BY_DATA. The file must be in .bin format. |
{
"Relative_Humidity_Filename": "Namawala_single_node_relative_humidity_daily.bin"
}
|
Relative_Humidity_Scale_Factor |
float |
0.1 |
10 |
1 |
The scale factor used in multiplying relative humidity values. Climate_Model must be set to CLIMATE_BY_DATA. |
{
"Relative_Humidity_Scale_Factor": 1
}
|
Relative_Humidity_Variance |
float |
0 |
0.12 |
0.05 |
The standard deviation (in percentage) for normally distributed noise applied to the daily relative humidity values when Climate_Model is configured as CLIMATE_CONSTANT or CLIMATE_BY_DATA. Enable_Climate_Stochasticity must be set to true (1). |
{
"Relative_Humidity_Variance": 0.05
}
|
Immunity¶
The following parameters determine the immune system response for the disease being modeled, including waning immunity after an infection clears.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Acquisition_Blocking_Immunity_Decay_Rate |
float |
0 |
1 |
0.001 |
The rate at which acquisition-blocking immunity decays after the initial period indicated by the base acquisition-blocking immunity offset. Only used when Enable_Immunity and Enable_Immune_Decay parameters are set to true (1). |
{
"Acquisition_Blocking_Immunity_Decay_Rate": 0.05
}
|
Acquisition_Blocking_Immunity_Duration_Before_Decay |
float |
0 |
45000 |
0 |
The number of days after infection until acquisition-blocking immunity begins to decay. Enable_Immunity and Enable_Immune_Decay must be set to true (1). |
{
"Acquisition_Blocking_Immunity_Duration_Before_Decay": 10
}
|
Antibody_Capacity_Growth_Rate |
float |
0 |
1 |
0.1 |
The maximum daily rate of specific antibody capacity increase. Antibody production begins at a capacity of 0.3., and hyperimmunity results at 0.4. The actual growth rate tends to be lower and is a function of antigen concentrations. |
{
"Antibody_Capacity_Growth_Rate": 0.09
}
|
Antibody_CSP_Decay_Days |
float |
1 |
3.40E+38 |
90 |
Exponential decay time for the circumsporozoite protein (CSP) antibody concentration (in days) when boosted above natural maximum concentrations. |
{
"Antibody_CSP_Decay_Days": 80
}
|
Antibody_CSP_Killing_Inverse_Width |
float |
1.00E-0 |
1000000 |
1.5 |
The inverse width of the sigmoidal sporozoite killing function of circumsporozoite protein (CSP) antibody concentration. |
{
"Antibody_CSP_Killing_Inverse_Width": 1.7
}
|
Antibody_CSP_Killing_Threshold |
float |
1.00E-0 |
1000000 |
10 |
The threshold value on circumsporozoite protein (CSP) antibody concentration for sporozoite killing. |
{
"Antibody_CSP_Killing_Threshold": 20
}
|
Antibody_IRBC_Kill_Rate |
double |
NA |
NA |
2 |
The scale factor multiplied by antibody level to produce the rate of clearance of the infected red blood cell (IRBC) population. |
{
"Antibody_IRBC_Kill_Rate": 1.595
}
|
Antibody_Memory_Level |
float |
0 |
0.35 |
0.2 |
The limiting level of antibody capacity that remains after a prolonged absence of a specific antigen. The antibody capacity decays to this level gradually after infection is cleared. The decay rate in antibody capacity is set so that hyperimmunity is lost within 4 months, and capacity continues to decay to this level. The antibody memory level is relevant for year-scale dynamics, but not for long-term dynamics (10-20 years). |
{
"Antibody_Memory_Level": 0.3
}
|
Antibody_Stimulation_C50 |
float |
0.1 |
10000 |
10 |
The concentration of an antigen, measured in IRBC/ul at which growth in antibody capacity against the antigen increases at half the maximum rate specified by Antibody_Capacity_Growth_Rate. |
{
"Antibody_Stimulation_C50": 30,
"Antibody_Capacity_Growth_Rate": 0.09
}
|
Antigen_Switch_Rate |
double |
NA |
NA |
2.00E-09 |
The antigenic switching rate per infected red blood cell per asexual cycle. See Parasite_Switch_Type for different switching patterns. |
{
"Antigen_Switch_Rate": 5e-09
}
|
Cytokine_Gametocyte_Inactivation |
float |
0 |
1 |
0.02 |
The strength of inflammatory response inactivation of gametocytes. |
{
"Cytokine_Gametocyte_Inactivation": 0.0167
}
|
Enable_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). This parameter should always be set to 1 (on) in this simulatin type. |
{
"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. This parameter should always be set to 1 (on) for this simulation type. |
{
"Enable_Immunity": 1
}
|
Enable_Immunity_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_Immunity_Distribution": 1,
"Immunity_Initialization_Distribution_Type": "DISTRIBUTION_SIMPLE"
}
|
Enable_Maternal_Antibodies_Transmission |
boolean |
0 |
1 |
0 |
Controls whether or not mothers pass antibodies to their infants. When enabled, you must set Maternal_Antibodies_Type,**Maternal_Antibody_Protection**, and Maternal_Antibody_Decay_Rate. Enable_Birth must be set to 1. |
{
"Enable_Maternal_Antibodies_Transmission": 1,
"Maternal_Antibodies_Type": "SIMPLE_WANING",
"Maternal_Antibody_Protection": 0.23,
"Maternal_Antibody_Decay_Rate": 0.03
}
|
Enable_Maternal_Protection |
boolean |
0 |
1 |
0 |
Controls whether or not mothers pass immunity on to their infants. Setting to 1 (true) enables maternal protection as defined by Maternal_Protection_Type. Enable_Birth must be set to 1 (true). |
{
"Enable_Maternal_Protection": 1,
"Maternal_Protection_Type": "LINEAR_FRACTIONAL"
}
|
Erythropoiesis_Anemia_Effect |
float |
0 |
1000 |
3.5 |
The exponential rate of increased red-blood-cell production from reduced red-blood-cell availability. |
{
"Erythropoiesis_Anemia_Effect": 3
}
|
Falciparum_MSP_Variants |
integer |
0 |
1000 |
100 |
The number of distinct merozoite surface protein (MSP) variants for P. falciparum malaria in the overall parasite population in the simulation, not necessarily in an individual. |
{
"Falciparum_MSP_Variants": 4
}
|
Falciparum_Nonspecific_Types |
integer |
0 |
1000 |
20 |
The number of distinct non-specific types of P. falciparum malaria. |
{
"Falciparum_Nonspecific_Types": 92
}
|
Falciparum_PfEMP1_Variants |
integer |
0 |
100000 |
1000 |
The number of distinct Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) variants for P. falciparum malaria in the overall parasite population in the simulation. |
{
"Falciparum_PfEMP1_Variants": 300
}
|
Fever_IRBC_Kill_Rate |
float |
0 |
1000 |
0.15 |
The maximum kill rate for infected red blood cells due to the inflammatory innate immune response. As fever increases above 38.5 degrees Celsius, the kill rate becomes successively higher along a sigmoidal curve approaching this rate. |
{
"Fever_IRBC_Kill_Rate": 1.4
}
|
Immune_Threshold_For_Downsampling |
float |
0 |
1 |
0 |
Threshold on acquisition immunity at which to apply immunity dependent downsampling. Individual_Sampling_Type must set to ADAPTED_SAMPLING_BY_IMMUNE_STATE. |
{
"Individual_Sampling_Type": "ADAPTED_SAMPLING_BY_IMMUNE_STATE",
"Immune_Threshold_For_Downsampling": 0.5
}
|
Immunity_Initialization_Distribution_Type |
enum |
NA |
NA |
DISTRIBUTION_OFF |
The method for initializing the immunity distribution in the simulated population. Enable_Immunity_Distribution must be set to 1 (true). Possible values are:
|
{
"Enable_Immunity": 1,
"Enable_Immunity_Distribution": 1,
"Immunity_Initialization_Distribution_Type": "DISTRIBUTION_COMPLEX"
}
|
Innate_Immune_Variation_Type |
enum |
NA |
NA |
NONE |
The type of innate immunity on which to apply individual-level variation. Possible values are:
|
{
"Innate_Immune_Variation_Type": "PYROGENIC_THRESHOLD"
}
|
Maternal_Antibodies_Type |
enum |
NA |
NA |
OFF |
The type of maternal antibody protection. Enable_Maternal_Antibodies_Transmission must be set to 1 (true). Possible values are:
|
{
"Maternal_Antibodies_Type": "CONSTANT_INITIAL_IMMUNITY"
}
|
Maternal_Antibody_Decay_Rate |
float |
0 |
3.40E+38 |
0.01 |
The decay rate per day in protection from maternal antibodies. Maternal_Antibodies_Type must be set to SIMPLE_WANING or CONSTANT_INITIAL_IMMUNITY. |
{
"Maternal_Antibodies_Type": "SIMPLE_WANING",
"Maternal_Antibody_Decay_Rate": 0.01,
"Maternal_Antibody_Protection": 0.1327
}
|
Maternal_Antibody_Protection |
float |
0 |
1 |
0.1 |
The strength of protection from maternal antibodies as a multiple of full antibody killing effect. Maternal_Antibodies_Type must be set to SIMPLE_WANING or CONSTANT_INITIAL_IMMUNITY. |
{
"Maternal_Antibodies_Type": "SIMPLE_WANING",
"Maternal_Antibody_Decay_Rate": 0.01,
"Maternal_Antibody_Protection": 0.1327
}
|
Maternal_Linear_Slope |
float |
0.0001 |
1 |
0.01 |
Slope parameter describing the rate of waning for maternal protection, must be positive. The per-day increase in susceptibility. Maternal_Protection_Type must be set to LINEAR_FRACTIONAL or LINEAR_BINARY. |
{
"Maternal_Protection_Type": "LINEAR_FRACTIONAL",
"Maternal_Linear_SusZero": 0.45,
"Maternal_Linear_Slope": 0.02
}
|
Maternal_Linear_SusZero |
float |
0 |
1 |
0.2 |
The initial level of maternal protection at age 0, given as susceptibility. A value of 0.0 implies total protection, a value of 1.0 implies no protection. Maternal_Protection_Type must be set to LINEAR_FRACTIONAL or LINEAR_BINARY. |
{
"Maternal_Protection_Type": "LINEAR_FRACTIONAL",
"Maternal_Linear_SusZero": 0.45,
"Maternal_Linear_Slope": 0.02
}
|
Maternal_Protection_Type |
enum |
NA |
NA |
NONE |
The type of maternal protection afforded to infants. Enable_Maternal_Protection must be set to 1 (true). Possible values are:
|
{
"Enable_Maternal_Protection": 1,
"Maternal_Protection_Type": "LINEAR_FRACTIONAL"
}
|
Maternal_Sigmoid_HalfMaxAge |
float |
-270 |
3650 |
180 |
The age in days that the level of maternal protection is half of its initial value. Maternal_Protection_Type must be set to SIGMOID_FRACTIONAL or SIGMOID_BINARY. |
{
"Maternal_Protection_Type": "SIGMOID_BINARY",
"Maternal_Sigmoid_SteepFac": 30,
"Maternal_Sigmoid_HalfMaxAge": 365,
"Maternal_Sigmoid_SusInit": 0.0002
}
|
Maternal_Sigmoid_SteepFac |
float |
0.1 |
1000 |
30 |
The steepness factor describing the rate of waning for maternal protection, must be positive. Small values imply rapid waning.**Maternal_Protection_Type** must be set to SIGMOID_FRACTIONAL or SIGMOID_BINARY. |
{
"Maternal_Protection_Type": "SIGMOID_BINARY",
"Maternal_Sigmoid_SteepFac": 30,
"Maternal_Sigmoid_HalfMaxAge": 365,
"Maternal_Sigmoid_SusInit": 0.0002
}
|
Maternal_Sigmoid_SusInit |
float |
0 |
1 |
0 |
The initial level of maternal protection at age -INF, given as susceptibility. A value of 0.0 implies total protection, a value of 1.0 implies no protection. Maternal_Protection_Type must be set to SIGMOID_FRACTIONAL or SIGMOID_BINARY. |
{
"Maternal_Protection_Type": "SIGMOID_BINARY",
"Maternal_Sigmoid_SteepFac": 30,
"Maternal_Sigmoid_HalfMaxAge": 365,
"Maternal_Sigmoid_SusInit": 0.0002
}
|
Max_MSP1_Antibody_Growthrate |
float |
0 |
1 |
0.02 |
The maximum increase in MSP1 antibody capacity during each asexual cycle. The higher this value, the sooner early clearances are observed and the earlier the parasite density envelope decreases. |
{
"Max_MSP1_Antibody_Growthrate": 0.045
}
|
Min_Adapted_Response |
float |
0 |
1 |
0.02 |
The minimum level of antibody stimulation to a novel antigen. The value sets the low-range asymptote for antibody capacity growth, which is calculated from Antibody_Capacity_Growth_Rate and antigen density, in the presence of any nonzero antigen level. |
{
"Min_Adapted_Response": 0.01
}
|
Mortality_Blocking_Immunity_Decay_Rate |
float |
0 |
1 |
0.001 |
The rate at which mortality-blocking immunity decays after the mortality-blocking immunity offset period. Enable_Immune_Decay must be set to 1. |
{
"Mortality_Blocking_Immunity_Decay_Rate": 0.1
}
|
Mortality_Blocking_Immunity_Duration_Before_Decay |
float |
0 |
45000 |
0 |
The number of days after infection until mortality-blocking immunity begins to decay. Enable_Immunity and Enable_Immune_Decay must be set to 1. |
{
"Mortality_Blocking_Immunity_Duration_Before_Decay": 270
}
|
MSP1_Merozoite_Kill_Fraction |
double |
NA |
NA |
0.5 |
The fraction of merozoites inhibited from invading new erythrocytes when MSP1-specific antibody level is 1. |
{
"MSP1_Merozoite_Kill_Fraction": 0.4364623975644405
}
|
Nonspecific_Antibody_Growth_Rate_Factor |
float |
0 |
1000 |
0.5 |
The factor that adjusts Antibody_Capacity_Growth_Rate for less immunogenic surface proteins, called minor epitopes. |
{
"Nonspecific_Antibody_Growth_Rate_Factor": 0.5
}
|
Nonspecific_Antigenicity_Factor |
double |
NA |
NA |
0.2 |
The nonspecific antigenicity factor that adjusts antibody IRBC kill rate to account for IRBCs caused by antibody responses to antigenically weak surface proteins. |
{
"Nonspecific_Antigenicity_Factor": 0.39192559432597257
}
|
Post_Infection_Acquisition_Multiplier |
float |
0 |
1 |
0 |
The multiplicative reduction in the probability of reacquiring disease. At the time of recovery, the immunity against acquisition is multiplied by Acquisition_Blocking_Immunity_Decay_Rate x (1 - Post_Infection_Acquisition_Multiplier). Enable_Immunity must be set to 1 (true). |
{
"Enable_Immunity": 1,
"Enable_Immune_Decay": 1,
"Immunity_Acquisition_Factor": 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,
"Immunity_Mortality_Factor": 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,
"Immunity_Transmission_Factor": 0.9
}
|
RBC_Destruction_Multiplier |
double |
NA |
NA |
9.5 |
The number of total red blood cells destroyed per infected rupturing schizont. |
{
"RBC_Destruction_Multiplier": 3.291048711
}
|
Susceptibility_Type |
enum |
NA |
NA |
FRACTIONAL |
Controls implementation of an individual’s susceptibility. Currently only relevant to Maternal_Protection_Type parameter. If set to FRACTIONAL, all agents are assigned equal values between 0 and 1 according to a governing equation. If set to BINARY, agents receive a value of either 0 or 1 with probability determined by governing equation. |
{
"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.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Base_Incubation_Period |
float |
0 |
3.40E+38 |
6 |
Average duration, in days, of the incubation period before infected individuals become infectious. Incubation_Period_Distribution must be set to either FIXED_DURATION or EXPONENTIAL_DURATION. |
{
"Incubation_Period_Distribution": "EXPONENTIAL_DURATION",
"Base_Incubation_Period": 1
}
|
Incubation_Period_Distribution |
enum |
NA |
NA |
NOT_INITIALIZED |
The distribution for the duration of the incubation period. Possible values are:
|
{
"Base_Incubation_Period": 5,
"Incubation_Period_Distribution": "EXPONENTIAL_DURATION"
}
|
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
}
|
Incubation_Period_Max |
float |
0.6 |
3.40E+38 |
0 |
The maximum length of the incubation period. Incubation_Period_Distribution must be set to UNIFORM_DURATION. |
{
"Incubation_Period_Distribution": "UNIFORM_DURATION",
"Incubation_Period_Min": 2,
"Incubation_Period_Max": 6
}
|
Incubation_Period_Mean |
float |
0 |
3.40E+38 |
6 |
The mean of the incubation period. Incubation_Period_Distribution must be set to either GAUSSIAN_DURATION or POISSON_DURATION. |
{
"Incubation_Period_Distribution": "GAUSSIAN_DURATION",
"Incubation_Period_Mean": 7,
"Infectious_Period_Std_Dev": 2
}
|
Incubation_Period_Min |
float |
0 |
3.40E+38 |
0 |
The minimum length of the incubation period. Incubation_Period_Distribution must be set to UNIFORM_DURATION. |
{
"Incubation_Period_Distribution": "UNIFORM_DURATION",
"Incubation_Period_Min": 2,
"Incubation_Period_Max": 6
}
|
Incubation_Period_Std_Dev |
float |
0 |
3.40E+38 |
1 |
The standard deviation incubation period. Incubation_Period_Distribution must be set to GAUSSIAN_DURATION. |
{
"Incubation_Period_Distribution": "GAUSSIAN_DURATION",
"Incubation_Period_Mean": 7,
"Infectious_Period_Std_Dev": 2
}
|
Infectivity and transmission¶
The following parameters determine aspects of infectivity and disease transmission. For example, how infectious individuals are and the length of time for which they remain infectious, whether the disease can be maternally transmitted, and how population density affects infectivity.
The vector transmission model does not use many of the parameters provided by the generic simulation type. Instead, gametocyte abundances and cytokine mediated infectiousness are modeled explicitly. See Vector transmission model for more information.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Acquire_Modifier |
float |
0 |
1 |
1 |
Modifier of the probability of successful infection of a mosquito by a malaria-infected individual, given the individual’s infectiousness. |
{
"Vector_Species_Params": {
"aegypti": {
"Acquire_Modifier": 1
}
}
}
|
Age_Dependent_Biting_Risk_Type |
enum |
NA |
NA |
OFF |
The type of functional form for age-dependent biting risk. Possible values are:
|
{
"Age_Dependent_Biting_Risk_Type": "SURFACE_AREA_DEPENDENT"
}
|
Base_Infectious_Period |
float |
0 |
3.40E+38 |
6 |
Average duration, in days, of the infectious period before the infection is cleared. Infectious_Period_Distribution must be set to either FIXED_DURATION or EXPONENTIAL_DURATION. This parameter is not used in this simulation type. Other mechanistic parameters control the length of the infectious period for each individual. |
{
"Base_Infectious_Period": 4
}
|
Base_Infectivity |
float |
0 |
1000 |
0.3 |
The base infectiousness of individuals before accounting for transmission-blocking effects of acquired immunity and/or campaign interventions. For vector simulations, this is the probability of infecting a mosquito during a successful blood meal (modulated by the vector parameter Acquire_Modifier). The sum infectiousness of an individual is not allowed to exceed 100%. For malaria simulations,this parameter is not used. Instead, gametocyte abundances and cytokine mediated infectiousness are modeled explicitly. |
{
"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 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_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. This parameter is not used in this simulation type. |
{
"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). This parameter is not used in this simulation type. |
{
"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 probablistically exposing individuals rather than exposing every single person. Useful in low-prevalence, high-population scenarios. Infectivity_Scale_Type must be set to CONSTANT_INFECTIVITY. This parameter is not used in this simulation type. |
{
"Exposure_Skipping": 1
}
|
Enable_Superinfection |
boolean |
0 |
1 |
0 |
Controls whether or not an individual can have multiple infections simultaneously. Set to true (1) to allow for multiple simultaneous infections; set to false (0) if multiple infections are not possible. Set the Max_Individual_Infections parameter. |
{
"Enable_Superinfection": 1,
"Max_Individual_Infections": 2
}
|
Enable_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"
}
|
Infected_Arrhenius_1 |
float |
0 |
1.00E+15 |
1.17E+11 |
The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the daily rate of fractional progression of infected mosquitoes to an infectious state. The duration of sporogony is a decreasing function of temperature. The variable a1 is a temperature-independent scale factor on the progression rate to infectiousness. |
{
"Vector_Species_Params": {
"arabiensis": {
"Acquire_Modifier": 0.2,
"Adult_Life_Expectancy": 10,
"Anthropophily": 0.95,
"Aquatic_Arrhenius_1": 84200000000,
"Aquatic_Arrhenius_2": 8328,
"Aquatic_Mortality_Rate": 0.1,
"Cycle_Arrhenius_1": 0,
"Cycle_Arrhenius_2": 0,
"Cycle_Arrhenius_Reduction_Factor": 0,
"Days_Between_Feeds": 3,
"Egg_Batch_Size": 100,
"Immature_Duration": 4,
"Indoor_Feeding_Fraction": 0.5,
"Infected_Arrhenius_1": 117000000000,
"Infected_Arrhenius_2": 8336,
"Infected_Egg_Batch_Factor": 0.8,
"Infectious_Human_Feed_Mortality_Factor": 1.5,
"Larval_Habitat_Types": {
"TEMPORARY_RAINFALL": 11250000000
},
"Nighttime_Feeding_Fraction": 1,
"Transmission_Rate": 0.5
}
}
}
|
Infected_Arrhenius_2 |
float |
0 |
1.00E+15 |
8340 |
The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the daily rate of fractional progression of infected mosquitoes to an infectious state. The duration of sporogony is a decreasing function of temperature. The variable a2 is a temperature-dependent scale factor on the progression rate to infectiousness. |
{
"Vector_Species_Params": {
"arabiensis": {
"Acquire_Modifier": 0.2,
"Adult_Life_Expectancy": 10,
"Anthropophily": 0.95,
"Aquatic_Arrhenius_1": 84200000000,
"Aquatic_Arrhenius_2": 8328,
"Aquatic_Mortality_Rate": 0.1,
"Cycle_Arrhenius_1": 0,
"Cycle_Arrhenius_2": 0,
"Cycle_Arrhenius_Reduction_Factor": 0,
"Days_Between_Feeds": 3,
"Egg_Batch_Size": 100,
"Immature_Duration": 4,
"Indoor_Feeding_Fraction": 0.5,
"Infected_Arrhenius_1": 117000000000,
"Infected_Arrhenius_2": 8336,
"Infected_Egg_Batch_Factor": 0.8,
"Infectious_Human_Feed_Mortality_Factor": 1.5,
"Larval_Habitat_Types": {
"TEMPORARY_RAINFALL": 11250000000
},
"Nighttime_Feeding_Fraction": 1,
"Transmission_Rate": 0.5
}
}
}
|
Infected_Egg_Batch_Factor |
float |
0 |
10 |
0.8 |
The dimensionless factor used to modify mosquito egg batch size in order to account for reduced fertility effects arising due to infection (e.g. when females undergo sporogony). |
{
"Vector_Species_Params": {
"arabiensis": {
"Acquire_Modifier": 0.2,
"Adult_Life_Expectancy": 10,
"Anthropophily": 0.95,
"Aquatic_Arrhenius_1": 84200000000,
"Aquatic_Arrhenius_2": 8328,
"Aquatic_Mortality_Rate": 0.1,
"Cycle_Arrhenius_1": 0,
"Cycle_Arrhenius_2": 0,
"Cycle_Arrhenius_Reduction_Factor": 0,
"Days_Between_Feeds": 3,
"Egg_Batch_Size": 100,
"Immature_Duration": 4,
"Indoor_Feeding_Fraction": 0.5,
"Infected_Arrhenius_1": 117000000000,
"Infected_Arrhenius_2": 8336,
"Infected_Egg_Batch_Factor": 0.8,
"Infectious_Human_Feed_Mortality_Factor": 1.5,
"Larval_Habitat_Types": {
"TEMPORARY_RAINFALL": 11250000000
},
"Nighttime_Feeding_Fraction": 1,
"Transmission_Rate": 0.5
}
}
}
|
Infection_Updates_Per_Timestep |
integer |
0 |
144 |
1 |
The number of infection updates executed during each timestep; note that a timestep defaults to one day. |
{
"Infection_Updates_Per_Timestep": 1
}
|
Infectious_Human_Feed_Mortality_Factor |
float |
0 |
1000 |
1.5 |
The (dimensionless) factor used to modify the death rate of mosquitoes when feeding on humans, to account for the higher mortality rate infected mosquitoes experience during human feeds versus uninfected mosquitoes. |
{
"Vector_Species_Params": {
"arabiensis": {
"Acquire_Modifier": 0.2,
"Adult_Life_Expectancy": 10,
"Anthropophily": 0.95,
"Aquatic_Arrhenius_1": 84200000000,
"Aquatic_Arrhenius_2": 8328,
"Aquatic_Mortality_Rate": 0.1,
"Cycle_Arrhenius_1": 0,
"Cycle_Arrhenius_2": 0,
"Cycle_Arrhenius_Reduction_Factor": 0,
"Days_Between_Feeds": 3,
"Egg_Batch_Size": 100,
"Immature_Duration": 4,
"Indoor_Feeding_Fraction": 0.5,
"Infected_Arrhenius_1": 117000000000,
"Infected_Arrhenius_2": 8336,
"Infected_Egg_Batch_Factor": 0.8,
"Infectious_Human_Feed_Mortality_Factor": 1.5,
"Larval_Habitat_Types": {
"TEMPORARY_RAINFALL": 11250000000
},
"Nighttime_Feeding_Fraction": 1,
"Transmission_Rate": 0.5
}
}
}
|
Infectious_Period_Distribution |
enum |
NA |
NA |
NOT_INITIALIZED |
The distribution of the duration of the infectious period. This parameter is not used in this simulation type. Other parameters that control disease mechanics explicity are used instead. Possible values are:
|
{
"Infectious_Period_Distribution": "EXPONENTIAL_DURATION"
}
|
Infectious_Period_Max |
float |
0.6 |
3.40E+38 |
0 |
The maximum length of the infectious period; used when Infectious_Period_Distribution is set to UNIFORM_DURATION. For malaria simulations, this parameter is not used. Instead, gametocyte abundances and cytokine-mediated infectiousness are modeled explicitly. |
{
"Infectious_Period_Distribution": "UNIFORM_DURATION",
"Infectious_Period_Max": 15,
"Infectious_Period_Min": 5
}
|
Infectious_Period_Mean |
float |
0 |
3.40E+38 |
6 |
The mean of the infectious period; used when Infectious_Period_Distribution is set to either GAUSSIAN_DURATION or POISSON_DURATION. For malaria simulations, this parameter is not used. Instead, gametocyte abundances and cytokine-mediated infectiousness are modeled explicitly. |
{
"Infectious_Period_Distribution": "GAUSSIAN_DURATION",
"Infectious_Period_Mean": 12,
"Infectious_Period_Std_Dev": 10
}
|
Infectious_Period_Min |
float |
0 |
3.40E+38 |
0 |
The minimum length of the infectious period; used when Infectious_Period_Distribution is set to UNIFORM_DURATION. For malaria simulations, this parameter is not used. Instead, gametocyte abundances and cytokine-mediated infectiousness are modeled explicitly. |
{
"Infectious_Period_Distribution": "UNIFORM_DURATION",
"Infectious_Period_Max": 15,
"Infectious_Period_Min": 5
}
|
Infectious_Period_Std_Dev |
float |
0 |
3.40E+38 |
1 |
The standard deviation of the infectious period; used when Infectious_Period_Distribution is set to GAUSSIAN_DURATION. For malaria simulations, this parameter is not used. Instead, gametocyte abundances and cytokine-mediated infectiousness are modeled explicitly. |
{
"Infectious_Period_Distribution": "GAUSSIAN_DURATION",
"Infectious_Period_Mean": 12,
"Infectious_Period_Std_Dev": 10
}
|
Infectivity_Boxcar_Forcing_Amplitude |
float |
0 |
3.40E+38 |
0 |
The fractional increase in R0 during the high-infectivity season when Infectivity_Scale_Type is equal to ANNUAL_BOXCAR_FUNCTION. |
{
"Infectivity_Boxcar_Forcing_Amplitude": 0.25,
"Infectivity_Boxcar_Forcing_End_Time": 270,
"Infectivity_Boxcar_Forcing_Start_Time": 90,
"Infectivity_Scale_Type": "ANNUAL_BOXCAR_FUNCTION"
}
|
Infectivity_Boxcar_Forcing_End_Time |
float |
0 |
365 |
0 |
The end of the high-infectivity season when Infectivity_Scale_Type is equal to ANNUAL_BOXCAR_FUNCTION. |
{
"Infectivity_Boxcar_Forcing_Amplitude": 0.25,
"Infectivity_Boxcar_Forcing_End_Time": 270,
"Infectivity_Boxcar_Forcing_Start_Time": 90,
"Infectivity_Scale_Type": "ANNUAL_BOXCAR_FUNCTION"
}
|
Infectivity_Boxcar_Forcing_Start_Time |
float |
0 |
365 |
0 |
The beginning of the high-infectivity season, in days, when Infectivity_Scale_Type is equal to ANNUAL_BOXCAR_FUNCTION. |
{
"Infectivity_Boxcar_Forcing_Amplitude": 0.25,
"Infectivity_Boxcar_Forcing_End_Time": 270,
"Infectivity_Boxcar_Forcing_Start_Time": 90,
"Infectivity_Scale_Type": "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. 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
}
|
Infectivity_Scale_Type |
enum |
NA |
NA |
CONSTANT_INFECTIVITY |
A scale factor that allows infectivity to be altered by time or season. Possible values are:
|
{
"Infectivity_Scale_Type": "FUNCTION_OF_CLIMATE"
}
|
Infectivity_Sinusoidal_Forcing_Amplitude |
float |
0 |
1 |
0 |
Sets the amplitude of sinusoidal variations in Base_Infectivity. Only used when Infectivity_Scale_Type is set to SINUSOIDAL_FUNCTION_OF_TIME. |
{
"Infectivity_Scale_Type": "SINUSOIDAL_FUNCTION_OF_TIME",
"Infectivity_Sinusoidal_Forcing_Amplitude": 0.1,
"Infectivity_Sinusoidal_Forcing_Phase": 0
}
|
Infectivity_Sinusoidal_Forcing_Phase |
float |
0 |
365 |
0 |
Sets the phase of sinusoidal variations in Base_Infectivity. Only used when Infectivity_Scale_Type is set to SINUSOIDAL_FUNCTION_OF_TIME. |
{
"Infectivity_Scale_Type": "SINUSOIDAL_FUNCTION_OF_TIME",
"Infectivity_Sinusoidal_Forcing_Amplitude": 0.1,
"Infectivity_Sinusoidal_Forcing_Phase": 0
}
|
Malaria_Strain_Model |
enum |
NA |
NA |
FALCIPARUM_NONRANDOM_STRAIN |
The generator that is used to construct the antigenic repertoire of a malaria infection. To create parasite diversity, various antigenic strains are created by conducting draws for merozoite surface protein (MSP) variants, Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) variants, and minor surface epitopes, out of available populations. Each possible value for this parameter utilizes different settings for the available population draws. Possible values are:
|
{
"Malaria_Strain_Model": "FALCIPARUM_STRAIN_GENERATOR"
}
|
Maternal_Infection_Transmission_Probability |
float |
0 |
1 |
0 |
The probability of transmission of infection from mother to infant at birth. Enable_Maternal_Infection_Transmission must be set to 1. Note For malaria and vector simulations, set this to 0. Instead, use the Maternal_Antibody_Protection, Maternal_Antibody_Decay_Rate, and Maternal_Antibodies_Type parameters. |
{
"Maternal_Infection_Transmission_Probability": 0.3
}
|
Max_Individual_Infections |
integer |
0 |
1000 |
1 |
The limit on the number of infections that an individual can have simultaneously. Enable_Superinfection must be set to 1. |
{
"Max_Individual_Infections": 5
}
|
Population_Density_C50 |
float |
0 |
3.40E+38 |
10 |
The population density at which R0 for a 2.5-arc minute square reaches half of its initial value. Population_Density_Infectivity_Correction must be set to SATURATING_FUNCTION_OF_DENSITY. |
{
"Population_Density_C50": 30
}
|
Population_Density_Infectivity_Correction |
enum |
NA |
NA |
CONSTANT_INFECTIVITY |
Correction to alter infectivity by population density set in the Population_Density_C50 parameter. Measured in people per square kilometer. Possible values are:
Note Sparsely populated areas have a lower infectivity, while densely populated areas have a higher infectivity, which rises to saturate at the Base_Infectivity value. |
{
"Population_Density_Infectivity_Correction": "SATURATING_FUNCTION_OF_DENSITY"
}
|
Pyrogenic_Threshold |
float |
0.1 |
20000 |
1000 |
The level of bloodstream infection, measured in IRBC per microliter, at which stimulation of the innate inflammatory immune response is half its maximum value. |
{
"Pyrogenic_Threshold": 15000
}
|
Relative_Sample_Rate_Immune |
float |
0.001 |
1 |
0.1 |
The relative sampling rate for people who have acquired immunity through recovery or vaccination. |
{
"Relative_Sample_Rate_Immune": 0.1
}
|
Susceptibility_Scaling_Rate |
float |
0 |
3.40282e+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. Possible values are:
|
{
"Susceptibility_Scaling_Type": "CONSTANT_SUSCEPTIBILITY"
}
|
Susceptibility_Type |
enum |
NA |
NA |
FRACTIONAL |
Controls implementation of an individual’s susceptibility. Currently only relevant to Maternal_Protection_Type parameter. If set to FRACTIONAL, all agents are assigned equal values between 0 and 1 according to a governing equation. If set to BINARY, agents receive a value of either 0 or 1 with probability determined by governing equation. |
{
"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
}
|
Transmission_Rate |
float |
0 |
1 |
0.5 |
The probability that the bite of an infected mosquito establishes a new infection in an immunologically naive and uninfected individual, or the modifier of the probability of success for an individual with pre-erythrocytic immunity. Note that each mosquito species will have their own Transmission_Rate parameter. |
{
"Vector_Species_Params": {
"arabiensis": {
"Cycle_Arrhenius_1" : 99.0,
"Cycle_Arrhenius_2" : 88.0,
"Cycle_Arrhenius_Reduction_Factor" : 0.77,
"Nighttime_Feeding_Fraction" : 1.0,
"Acquire_Modifier": 0.2,
"Adult_Life_Expectancy": 10,
"Anthropophily": 0.95,
"Aquatic_Arrhenius_1": 84200000000,
"Aquatic_Arrhenius_2": 8328,
"Aquatic_Mortality_Rate": 0.1,
"Days_Between_Feeds": 3,
"Egg_Batch_Size": 100,
"Immature_Duration": 4,
"Indoor_Feeding_Fraction": 0.5,
"Infected_Arrhenius_1": 117000000000,
"Infected_Arrhenius_2": 8336,
"Infected_Egg_Batch_Factor": 0.8,
"Infectious_Human_Feed_Mortality_Factor": 1.5,
"Larval_Habitat_Types": {
"TEMPORARY_RAINFALL": 11250000000,
"BRACKISH_SWAMP": 10000000000
},
"Transmission_Rate": 0.5
}
}
}
|
Zoonosis_Rate |
float |
0 |
1 |
0 |
The daily rate of zoonotic infection per individual. Animal_Reservoir_Type must be set to CONSTANT_ZOONOSIS or ZOONOSIS_FROM_DEMOGRAPHICS. If Animal_Reservoir_Type is set to ZOONOSIS_FROM_DEMOGRAPHICS, the value for the Zoonosis NodeAttribute in the demographics file will override the value set for Zoonosis_Rate. |
{
"Zoonosis_Rate": 0.005
}
|
Input data files¶
The following parameters set the paths to the the campaign file and the input data files for climate, migration, demographics, and load-balancing.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Air_Migration_Filename |
string |
NA |
NA |
The path to the input data file that defines patterns of migration by airplane. Enable_Air_Migration must be set to true (1). The file must be in .bin format. |
{
"Migration_Model": "FIXED_RATE_MIGRATION",
"Enable_Air_Migration" : 1,
"Air_Migration_Filename": "../Global_1degree_air_migration.bin"
}
|
|
Air_Temperature_Filename |
string |
NA |
NA |
air_temp.json |
The path to the input data file that defines air temperature data measured two meters above ground. Climate_Model must be set to CLIMATE_BY_DATA. The file must be in .bin format. |
{
"Climate_Model": "CLIMATE_BY_DATA",
"Air_Temperature_Filename": "Namawala_single_node_air_temperature_daily.bin"
}
|
Campaign_Filename |
string |
NA |
NA |
The path to the campaign file. It is required when interventions are part of the simulation and Enable_Interventions is set to true (1). The file must be in .json format. |
{
"Enable_Interventions": 1,
"Campaign_Filename": "campaign.json"
}
|
|
Demographics_Filenames |
array of strings |
NA |
NA |
An array of the paths to demographics files containing information on the identity and demographics of the region to simulate. The files must be in .json format. |
{
"Demographics_Filenames": [
"Namawala_single_node_demographics.json",
"Namawala_demographics_overlay.json"
]
}
|
|
Family_Migration_Filename |
string |
NA |
NA |
The name of the binary file to use to configure family migration. Enable_Family_Migration must be set to true (1). The file must be in .bin format. |
{
"Migration_Model": "FIXED_RATE_MIGRATION",
"Enable_Family_Migration" 1,
"Family_Migration_Filename": "../inputs/family_migration.bin"
}
|
|
Koppen_Filename |
string |
NA |
NA |
koppen_climate.json |
The path to the input file used to specify Koppen climate classifications. Climate_Model must be set to CLIMATE_KOPPEN. |
{
"Koppen_Filename": "Mad_2_5arcminute_koppen.dat"
}
|
Land_Temperature_Filename |
string |
NA |
NA |
land_temp.json |
The path of the input file defining temperature data measured at land surface; used only when Climate_Model is set to CLIMATE_BY_DATA. The file must be in .bin format. |
{
"Land_Temperature_Filename": "Namawala_single_node_land_temperature_daily.bin"
}
|
Load_Balance_Filename |
string |
NA |
NA |
UNINITIALIZED STRING |
The path to the input file used when a static load balancing scheme is selected. The file must be in .json format. |
{
"Load_Balance_Filename": "GitHub_426_LoadBalance.json"
}
|
Local_Migration_Filename |
string |
NA |
NA |
The path of the input file which defines patterns of migration to adjacent nodes by foot travel. The file must be in .bin format. |
{
"Local_Migration_Filename": "Local_Migration.bin"
}
|
|
Rainfall_Filename |
string |
NA |
NA |
rainfall.json |
The path of the input file which defines rainfall data. Climate_Model must be set to CLIMATE_BY_DATA. The file must be in .bin format. |
{
"Rainfall_Filename": "Namawala_single_node_rainfall_daily.bin"
}
|
Regional_Migration_Filename |
string |
NA |
NA |
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. |
{
"Regional_Migration_Filename": "Regional_Migration.bin"
}
|
|
Relative_Humidity_Filename |
string |
NA |
NA |
rel_hum.json |
The path of the input file which defines relative humidity data measured 2 meters above ground. Climate_Model must be set to CLIMATE_BY_DATA. The file must be in .bin format. |
{
"Relative_Humidity_Filename": "Namawala_single_node_relative_humidity_daily.bin"
}
|
Sea_Migration_Filename |
string |
NA |
NA |
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. |
{
"Sea_Migration_Filename": "5x5_Households_Work_Migration.bin"
}
|
|
Serialized_Population_Filenames |
array of strings |
NA |
NA |
NA |
An array of filenames with serialized population data. The number of filenames must match the number of cores used for the simulation. The files must be in .dtk format. Serialized population files are created using Serialization_Time_Steps. |
{
"Serialized_Population_Filenames": [
"state-00010.dtk"
]
}
|
Serialized_Population_Path |
string |
NA |
NA |
. |
The root path for the serialized population files. Serialized population files are created using Serialization_Time_Steps. |
{
"Serialized_Population_Path": "../00_Generic_Version_1_save/output"
}
|
Vector_Migration_Filename_Local |
string |
NA |
NA |
UNSPECIFIED_FILE |
The path to the vector migration file which defines patterns of vector migration to adjacent nodes. Enable_Vector_Migration must be set to 1. The file must be in .bin format. |
{
"Vector_Migration_Filename_Local": "5x5_Households_Local_Vector_Migration.bin"
}
|
Vector_Migration_Filename_Regional |
string |
NA |
NA |
UNSPECIFIED_FILE |
The path to the vector migration file which defines patterns of vector migration to non-adjacent nodes. Enable_Vector_Migration must be set to 1. The file must be in .bin format. |
{
"Vector_Migration_Filename_Regional": "5x5_Households_Regional_Vector_Migration.bin"
}
|
Larval habitat¶
The following parameters determine mosquito larval development related to habitat and climate. For more information, see Larval habitat. Parameters for the vector life cycle more broadly are described in Vector life cycle.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Aquatic_Arrhenius_1 |
float |
0 |
1.00E+15 |
8.42E+10 |
The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the daily rate of fractional progression of mosquito aquatic development (egg-hatching through emergence). This duration is a decreasing function of temperature. The variable a1 is a temperature-independent scale factor on development rate. |
{
"Vector_Species_Params": {
"aegypti": {
"Aquatic_Arrhenius_1": 9752291.727
}
}
}
|
Aquatic_Arrhenius_2 |
float |
0 |
1.00E+15 |
8328 |
The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the daily rate of fractional progression of mosquito aquatic development (egg-hatching through emergence). This duration is a decreasing function of temperature. The variable a2 governs how quickly the rate changes with temperature. |
{
"Vector_Species_Params": {
"arabiensis": {
"Aquatic_Arrhenius_2": 8328
}
}
}
|
Aquatic_Mortality_Rate |
float |
0 |
1 |
0.1 |
The base aquatic mortality per day for the species before adjusting for effects of overpopulation and drying out of aquatic habitat. |
{
"Vector_Species_Params": {
"arabiensis": {
"Aquatic_Mortality_Rate": 0.1
}
}
}
|
Drought_Egg_Hatch_Delay |
float |
0 |
1 |
0.33 |
Proportion of regular egg hatching that still occurs when habitat dries up. Enable_Drought_Egg_Hatch_Delay must be set to 1. |
{
"Enable_Drought_Egg_Hatch_Delay": 1,
"Drought_Egg_Hatch_Delay": 0.33
}
|
Egg_Arrhenius1 |
float |
0 |
1.00E+10 |
6.16E+07 |
The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the daily rate of mosquito egg hatching. This duration is a decreasing function of temperature. The variable a1 is a temperature-independent scale factor on hatching rate. Enable_Temperature_Dependent_Egg_Hatching must be set to 1. |
{
"Enable_Temperature_Dependent_Egg_Hatching": 1,
"Egg_Arrhenius1": 61599956,
"Egg_Arrhenius2": 5754
}
|
Egg_Arrhenius2 |
float |
0 |
1.00E+10 |
5754.03 |
The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the daily rate of mosquito egg hatching. This duration is a decreasing function of temperature. The variable a2 is a temperature-dependent scale factor on hatching rate. Enable_Temperature_Dependent_Egg_Hatching must be set to 1. |
{
"Enable_Temperature_Dependent_Egg_Hatching": 1,
"Egg_Arrhenius1": 61599956,
"Egg_Arrhenius2": 5754
}
|
Egg_Hatch_Density_Dependence |
enum |
NA |
NA |
NO_DENSITY_DEPENDENCE |
The effect of larval density on egg hatching rate. Possible values are:
|
{
"Egg_Hatch_Density_Dependence": "NO_DENSITY_DEPENDENCE"
}
|
Egg_Saturation_At_Oviposition |
enum |
NA |
NA |
NO_SATURATION |
If laying all eggs from ovipositing females would overflow the larval habitat capacity on a given day, the means by which the viable eggs become saturated. Possible values are:
|
{
"Egg_Saturation_At_Oviposition": "SATURATION_AT_OVIPOSITION"
}
|
Enable_Drought_Egg_Hatch_Delay |
boolean |
0 |
1 |
0 |
Controls whether or not eggs hatch at delayed rates, set by Drought_Egg_Hatch_Delay, when the habitat dries up completely. |
{
"Enable_Drought_Egg_Hatch_Delay": 1,
"Drought_Egg_Hatch_Delay": 0.33
}
|
Enable_Egg_Mortality |
boolean |
0 |
1 |
0 |
Controls whether or not to include a daily mortality rate on the egg population, which is independent of climatic factors. |
{
"Enable_Egg_Mortality": 1,
}
|
Enable_Temperature_Dependent_Egg_Hatching |
boolean |
0 |
1 |
0 |
Controls whether or not temperature has an effect on egg hatching, defined by Egg_Arrhenius_1 and Egg_Arrhenius_2. |
{
"Enable_Temperature_Dependent_Egg_Hatching": 1,
"Egg_Arrhenius1": 61599956.864,
"Egg_Arrhenius2": 5754.033
}
|
Larval_Density_Dependence |
enum |
NA |
NA |
UNIFORM_WHEN_OVERPOPULATION |
The functional form of mortality and growth delay for mosquito larvae based on population density. Possible values are:
|
{
"Larval_Density_Dependence": "GRADUAL_INSTAR_SPECIFIC"
}
|
Larval_Habitat_Types |
JSON object |
NA |
NA |
NA |
A measure of the habitat type and scale factors used to estimate larval population. This parameter is a dictionary that specifies habitat type with a simple numeric scale factor or, for LINEAR_SPLINE, with a more detailed configuration for scaling. The numeric scaling value represents larval density with the number of larvae in a 1x1-degree area. The factor multiplicatively scales the resulting weather or population-dependent functional form. Possible habitat values are:
The values set here can be scaled per node using LarvalHabitatMultiplier in the demographics file (see NodeAttributes) or per intervention using Larval_Habitat_Multiplier in the campaign file (see ScaleLarvalHabitat). |
The following example shows how to specify larval habitat using LINEAR_SPLINE. {
"Vector_Species_Params": {
"arabiensis": {
"Larval_Habitat_Types": {
"LINEAR_SPLINE": {
"Capacity_Distribution_Per_Year": {
"Times": [0.0, 60.833, 121.667, 182.5, 243.333, 204.167],
"Values": [0.0, 0.0, 0.2, 1.0, 0.5, 0.0]
},
"Max_Larval_Capacity": 10000000000.0
}
}
}
}
}
The following example shows how to specify the larval habitat using climatological habitat types (you may use one or more for each vector species). {
"arabiensis": {
"Larval_Habitat_Types": {
"TEMPORARY_RAINFALL": 11250000000,
"WATER_VEGETATION": 6000000000,
"BRACKISH_SWAMP": 30000000
}
}
}
|
Larval_Rainfall_Mortality_Threshold |
float |
0.01 |
1000 |
100 |
The threshold value on daily rainfall in millimeters, above which larval mortality is applied when Vector_Larval_Rainfall_Mortality is set to either SIGMOID or SIGMOID_HABITAT_SHIFTING. |
{
"Larval_Rainfall_Mortality_Threshold": 30.0
}
|
Max_Larval_Capacity |
float |
0 |
3.40E+38 |
1.00E+10 |
The maximum larval capacity. |
{
"Max_Larval_Capacity": 10000000000
}
|
Rainfall_In_mm_To_Fill_Swamp |
float |
1 |
10000 |
1000 |
Millimeters of rain to fill larval habitat to capacity. This is only used for vector species with Larval_Habitat_Types set to BRACKISH_SWAMP. |
{
"Rainfall_In_mm_To_Fill_Swamp": 1000.0
}
|
Semipermanent_Habitat_Decay_Rate |
float |
0.0001 |
100 |
0.01 |
Daily rate of larval habitat loss for semi-permanent habitats with Larval_Habitat_Types parameter value of WATER_VEGETATION or BRACKISH_SWAMP. |
{
"Semipermanent_Habitat_Decay_Rate": 0.01
}
|
Temporary_Habitat_Decay_Factor |
float |
0.001 |
100 |
0.05 |
The factor to convert raw evaporation rate (ignoring boundary layer effects) to the daily rate of larval habitat loss for temporary habitats (Larval_Habitat_Types set to TEMPORARY_RAINFALL). Units are (larval carrying capacity per day) / (kg per square meter per second). |
{
"Temporary_Habitat_Decay_Factor": 0.05
}
|
Vector_Larval_Rainfall_Mortality |
enum |
NA |
NA |
NONE |
The type of vector larval mortality function due to rainfall. Possible values are:
|
{
"Vector_Larval_Rainfall_Mortality": "SIGMOID"
}
|
Vector_Migration_Habitat_Modifier |
float |
0 |
3.40E+38 |
0 |
The preference of a vector to migrate toward a node with more habitat. Only used when Vector_Sampling_Type is set to TRACK_ALL_VECTORS. Enable_Vector_Migration must be set to 1. |
{
"Vector_Migration_Habitat_Modifier": 1.0
}
|
x_Temporary_Larval_Habitat |
double |
NA |
NA |
1 |
Scale factor for the habitat size for all mosquito populations. |
{
"x_Temporary_Larval_Habitat": 1
}
|
Migration¶
The following parameters determine aspects of population migration into and outside of a node, including daily commutes, seasonal migration, and one-way moves. Modes of transport includes travel by foot, automobile, sea, or air. Migration can also be configured to move all individuals in a family at the same time.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Air_Migration_Filename |
string |
NA |
NA |
The path to the input data file that defines patterns of migration by airplane. Enable_Air_Migration must be set to true (1). The file must be in .bin format. |
{
"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. Set to true (1) to use a migration rate distribution in the demographics file (see NodeAttributes parameters); set to false (0) to use the same migration rate applied to all individuals. 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 vehicle into and out of nodes in the road network. Migration_Model must be set to FIXED_RATE_MIGRATION. |
{
"Migration_Model": "FIXED_RATE_MIGRATION",
"Enable_Regional_Migration": 1,
"Regional_Migration_Filename": "../inputs/regional_migration.bin"
}
|
Enable_Sea_Migration |
boolean |
0 |
1 |
0 |
Controls whether or not there is migration on ships into and out of coastal cities with seaports. Migration_Model must be set to FIXED_RATE_MIGRATION. |
{
"Migration_Model": "FIXED_RATE_MIGRATION",
"Enable_Sea_Migration": 1,
"Sea_Migration_Filename": "../inputs/sea_migration.bin"
}
|
Enable_Vector_Migration |
boolean |
0 |
1 |
0 |
Controls whether or not vectors can migrate. Vector_Sampling_Type must be set to TRACK_ALL_VECTORS or SAMPLE_IND_VECTORS. Specific migration types must be enabled or disabled. |
{
"Vector_Sampling_Type": "TRACK_ALL_VECTORS",
"Enable_Vector_Migration": 1,
"Enable_Vector_Migration_Local": 1,
"Vector_Migration_Filename_Local": "../inputs/vector_local.bin"
}
|
Enable_Vector_Migration_Local |
boolean |
0 |
1 |
0 |
Controls whether or not vectors may migrate to adjacent nodes. Enable_Vector_Migration must be set to true (1). |
{
"Vector_Sampling_Type": "TRACK_ALL_VECTORS",
"Enable_Vector_Migration": 1,
"Enable_Vector_Migration_Local": 1,
"Vector_Migration_Filename_Local": "../inputs/vector_local.bin"
}
|
Enable_Vector_Migration_Regional |
boolean |
0 |
1 |
0 |
Controls whether or not vectors can migration to non-adjacent nodes. Enable_Vector_Migration must be set to true (1). |
{
"Vector_Sampling_Type": "TRACK_ALL_VECTORS",
"Enable_Vector_Migration": 1,
"Enable_Vector_Migration_Regional": 1,
"Vector_Migration_Filename_Regional": "../inputs/vector_regional.bin"
}
|
Family_Migration_Filename |
string |
NA |
NA |
The name of the binary file to use to configure family migration. Enable_Family_Migration must be set to true (1). The file must be in .bin format. |
{
"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 |
The path of the input file which defines patterns of migration to adjacent nodes by foot travel. The file must be in .bin format. |
{
"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 type of roundtrip 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"
}
|
Regional_Migration_Filename |
string |
NA |
NA |
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. |
{
"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 |
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. |
{
"Sea_Migration_Filename": "5x5_Households_Work_Migration.bin"
}
|
|
Sea_Migration_Roundtrip_Duration |
float |
0 |
10000 |
1 |
The average time spent at the destination node during a round-trip migration by ship. Used only when Migration_Pattern must be set to SINGLE_ROUND_TRIPS. |
{
"Enable_Sea_Migration": 1,
"Migration_Pattern": "SINGLE_ROUND_TRIPS",
"Sea_Migration_Roundtrip_Duration": 10000
}
|
Sea_Migration_Roundtrip_Probability |
float |
0 |
1 |
0.25 |
The likelihood that an individual who travels by ship into a neighboring node will return to the node of origin during the next migration. Used only when Enable_Sea_Migration is set to true (1). |
{
"Sea_Migration_Roundtrip_Probability": 0
}
|
Vector_Migration_Filename_Local |
string |
NA |
NA |
UNSPECIFIED_FILE |
The path to the vector migration file which defines patterns of vector migration to adjacent nodes. Enable_Vector_Migration must be set to 1. The file must be in .bin format. |
{
"Vector_Migration_Filename_Local": "5x5_Households_Local_Vector_Migration.bin"
}
|
Vector_Migration_Filename_Regional |
string |
NA |
NA |
UNSPECIFIED_FILE |
The path to the vector migration file which defines patterns of vector migration to non-adjacent nodes. Enable_Vector_Migration must be set to 1. The file must be in .bin format. |
{
"Vector_Migration_Filename_Regional": "5x5_Households_Regional_Vector_Migration.bin"
}
|
Vector_Migration_Food_Modifier |
float |
0 |
3.40E+38 |
0 |
The preference of a vector to migrate toward a node currently occupied by humans, independent of the number of humans in the node. Used only when Vector_Sampling_Type is set to TRACK_ALL_VECTORS. Enable_Vector_Migration must be set to 1. |
{
"Vector_Migration_Food_Modifier": 1.0
}
|
Vector_Migration_Habitat_Modifier |
float |
0 |
3.40E+38 |
0 |
The preference of a vector to migrate toward a node with more habitat. Only used when Vector_Sampling_Type is set to TRACK_ALL_VECTORS. Enable_Vector_Migration must be set to 1. |
{
"Vector_Migration_Habitat_Modifier": 1.0
}
|
Vector_Migration_Modifier_Equation |
enum |
NA |
NA |
LINEAR |
The functional form of vector migration modifiers. Enable_Vector_Migration must be set to 1. Possible values are: LINEAR EXPONENTIAL |
{
"Vector_Migration_Modifier_Equation": "EXPONENTIAL"
}
|
Vector_Migration_Stay_Put_Modifier |
float |
0 |
3.40E+38 |
0 |
The preference of a vector to remain in its current node rather than migrate to another node. Used only when Vector_Sampling_Type is set to TRACK_ALL_VECTORS. Enable_Vector_Migration must be set to 1. |
{
"Vector_Migration_Stay_Put_Modifier": 1.0
}
|
x_Air_Migration |
float |
0 |
3.40E+38 |
1 |
Scale factor for the rate of migration by air, as provided by the migration file. Enable_Air_Migration must be set to 1. |
{
"x_Air_Migration": 1
}
|
x_Family_Migration |
float |
0 |
3.40E+38 |
1 |
Scale factor for the rate of migration by families, as provided by the migration file. Enable_Family_Migration must be set to true (1). |
{
"x_Family_Migration": 1
}
|
x_Local_Migration |
float |
0 |
3.40E+38 |
1 |
Scale factor for rate of migration by foot travel, as provided by the migration file. Enable_Local_Migration must be set to 1. |
{
"x_Local_Migration": 1
}
|
x_Regional_Migration |
float |
0 |
3.40E+38 |
1 |
Scale factor for the rate of migration by road vehicle, as provided by the migration file. Enable_Regional_Migration must be set to 1. |
{
"x_Regional_Migration": 1
}
|
x_Sea_Migration |
float |
0 |
3.40E+38 |
1 |
Scale factor for the rate of migration by sea, as provided by the migration file. Enable_Sea_Migration must be set to 1. |
{
"x_Sea_Migration": 1
}
|
x_Vector_Migration_Local |
float |
0 |
3.40E+38 |
1 |
Scale factor for the rate of vector migration to adjacent nodes, as provided by the vector migration file. Enable_Vector_Migration must be set to 1. |
{
"x_Vector_Migration_Local": 1.0
}
|
x_Vector_Migration_Regional |
float |
0 |
3.40E+38 |
1 |
Scale factor for the rate of vector migration to non-adjacent nodes, as provided by the vector migration file. Enable_Vector_Migration must be set to 1. |
{
"x_Vector_Migration_Regional": 1.0
}
|
Mortality and survival¶
The following parameter determine mortality and survival characteristics of the disease being modeled and the population in general (non-disease mortality).
See Severe disease and mortality for more information about how fever, anemia, and parasite counts lead to severe malaria and mortality.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Anemia_Mortality_Inverse_Width |
float |
0.1 |
1000000 |
10 |
The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function: probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) ) This parameter configures the inverse width relative to threshold value of severe disease turn-on around threshold for anemia. |
{
"Anemia_Mortality_Inverse_Width": 150
}
|
Anemia_Mortality_Threshold |
double |
NA |
NA |
3 |
The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function: probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) ) This parameter configures the inverse width relative to threshold value of mortality turn-on around threshold for hemoglobin count in grams per deciliter (g/dL) at which 50% of individuals die per day. |
{
"Anemia_Mortality_Threshold": 1.5
}
|
Anemia_Severe_Inverse_Width |
float |
0.1 |
1000000 |
10 |
The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function: probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) ) This parameter configures the inverse width relative to threshold value of severe disease turn-on around threshold for anemia. |
{
"Anemia_Severe_Inverse_Width": 20
}
|
Anemia_Severe_Threshold |
float |
0 |
100 |
5 |
The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function: probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) ) This parameter configures the severe disease threshold level for anemia. Threshold units are in grams per deciliter (g/dL). |
{
"Anemia_Severe_Threshold": 3.0
}
|
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). This parameter is not used in this simulation type and any values set here will be overridden. |
{
"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. This parameter is not used with this simulation type. |
{
"Enable_Disease_Mortality": 1
}
|
Enable_Natural_Mortality |
boolean |
0 |
1 |
0 |
Controls whether or not individuals are removed from the simulation due to natural (non-disease) deaths. Enable_Vital_Dynamics must be set to 1 (true). Use Death_Rate_Dependence to set the natural death rate. |
{
"Enable_Natural_Mortality": 1,
"Enable_Vital_Dynamics": 1
}
|
Fever_Mortality_Inverse_Width |
float |
0.1 |
1000000 |
10 |
The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function: probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) ) This parameter configures the inverse width relative to threshold value of mortality turn-on around threshold for fever. |
{
"Fever_Mortality_Inverse_Width": 1000
}
|
Fever_Mortality_Threshold |
double |
NA |
NA |
3 |
The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function: probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) ) This parameter configures the fever mortality threshold, in units of degrees Celsius above normal body temperature (defined as 37 C). |
{
"Fever_Mortality_Threshold": 10
}
|
Mortality_Blocking_Immunity_Decay_Rate |
float |
0 |
1 |
0.001 |
The rate at which mortality-blocking immunity decays after the mortality-blocking immunity offset period. Enable_Immune_Decay must be set to 1. |
{
"Mortality_Blocking_Immunity_Decay_Rate": 0.1
}
|
Mortality_Time_Course |
enum |
NA |
NA |
DAILY_MORTALITY |
The method used to calculate disease deaths. Enable_Disease_Mortality must be set to 1. Possible values are:
|
{
"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, as 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.
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"
}
|
|||||||||||||||
Duration_Days |
float |
0 |
3.40E+38 |
0 |
Used by reports and custom reports. The number of days over which to report events. |
{
"Reports" :
[
{
"class" : "TestReport",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Event_Trigger_List" :
[
"Births",
"NonDiseaseDeaths"
],
"Start_Day": 123,
"Duration_Days" : 4,
"Report_Value" : 5,
"Report_Description" : "Description of report"
}
]
}
|
|||||||||||||||
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 |
NoTrigger |
Used by reports and custom reports. The list of triggers for the events included in the report. |
{
"Reports" :
[
{
"class" : "TestReport",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Event_Trigger_List" :
[
"Births",
"NonDiseaseDeaths"
],
"Start_Day": 123,
"Duration_Days" : 4,
"Report_Value" : 5,
"Report_Description" : "Description of report"
}
]
}
|
|||||||||||||||
Max_Number_Reports |
integer |
0 |
1000000 |
1 |
Used by reports and custom reports. The maximum number of report output files that will be produced for a given campaign. |
{
"Reports" :
[
{
"Pretty_Format" : 1,
"Start_Day": 0,
"Duration_Days" : 10000,
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Event_Trigger_List" :
[
"NewClinicalCase"
],
"Max_Number_Reports": 2,
"Report_Description": "Description of report",
"Reporting_Interval": 40
}
]
}
|
|||||||||||||||
Pretty_Format |
boolean |
0 |
1 |
0 |
True (1) sets pretty JSON formatting. The default, false (0), saves space. |
{
"Reports" :
[
{
"Pretty_Format" : 1,
"Start_Day": 0,
"Duration_Days" : 10000,
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Event_Trigger_List" :
[
"NewClinicalCase"
],
"Max_Number_Reports": 2,
"Report_Description": "Description of report",
"Reporting_Interval": 40
}
]
}
|
|||||||||||||||
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. |
{
"Reports" :
[
{
"Pretty_Format" : 1,
"Start_Day": 0,
"Duration_Days" : 10000,
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Event_Trigger_List" :
[
"NewClinicalCase"
],
"Max_Number_Reports": 2,
"Report_Description": "Description of report",
"Reporting_Interval": 40
}
]
}
|
|||||||||||||||
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. |
{
"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. |
{
"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 events that will be excluded from the property output report; all events NOT listed in the array will be included in the report. To report all events from the simulation, leave the events array empty. |
This example demonstrates reporting all individual property events: {
"Report_Event_Recorder_Individual_Properties": []
}
The following example demonstrates the syntax for excluding particular properties from the report: {
"Report_Event_Recorder_Individual_Properties": [
"Accessibility",
"Risk"
]
}
|
|||||||||||||||
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"
]
}
|
Parasite dynamics¶
The following parameters determine the dynamics of the Plasmodium falciparum parasite life cycle, including dynamics within the host and human population. For more information, see Malaria infection and immune model.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Base_Gametocyte_Fraction_Male |
double |
NA |
NA |
0.2 |
Fraction of gametocytes that are male. |
{
"Base_Gametocyte_Fraction_Male": 0.25
}
|
Base_Gametocyte_Mosquito_Survival_Rate |
float |
0 |
1 |
0.01 |
Average fraction of gametocytes in a blood meal that are successful in infecting the mosquito in the absence of other modulating effects, such as fever. |
{
"Base_Gametocyte_Mosquito_Survival_Rate": 0.015
}
|
Base_Gametocyte_Production_Rate |
double |
NA |
NA |
0.02 |
The fraction of infected red blood cells (IRBCs) producing gametocytes. |
{
"Base_Gametocyte_Mosquito_Survival_Rate": 0.01
}
|
Base_Sporozoite_Survival_Fraction |
float |
0 |
1 |
0.25 |
The fraction of sporozoites that survive to infect a hepatocyte in the absence of anti-CSP protection. |
{
"Base_Sporozoite_Survival_Fraction": 0.25
}
|
Cytokine_Gametocyte_Inactivation |
float |
0 |
1 |
0.02 |
The strength of inflammatory response inactivation of gametocytes. |
{
"Cytokine_Gametocyte_Inactivation": 0.0167
}
|
Enable_Sexual_Combination |
boolean |
0 |
1 |
0 |
Controls whether or not male and female gametocytes undergo sexual combination. Set to true (1) to allow for sexual combination; set to false (0) for transmission of each strain only. Note This parameter is currently not in use. |
{
"Enable_Sexual_Combination": 0
}
|
Gametocyte_Stage_Survival_Rate |
double |
0 |
1 |
1 |
The rate of gametocyte survival from one development stage to the next in the absence of drugs or inflammatory immune response. |
{
"Gametocyte_Stage_Survival_Rate": 0.8
}
|
Mean_Sporozoites_Per_Bite |
float |
0 |
1000 |
11 |
The mean number of sporozoites per infectious mosquito bite. |
{
"Mean_Sporozoites_Per_Bite": 11
}
|
Merozoites_Per_Hepatocyte |
double |
NA |
NA |
15000 |
The number of IRBCs caused by a single infected hepatocyte at the start of infection. |
{
"Merozoites_Per_Hepatocyte": 15000
}
|
Merozoites_Per_Schizont |
double |
NA |
NA |
16 |
The number of merozoites released by a single infected schizont after each asexual cycle. The number of resulting IRBC’s depends on the RBC availability and merozoite-specific immunity. |
{
"Merozoites_Per_Schizont": 16
}
|
Number_Of_Asexual_Cycles_Without_Gametocytes |
integer |
0 |
500 |
1 |
The number of asexual reproduction cycles that do not produce gametocytes. All later cycles will produce gametocytes according to Base_Gametocyte_Production_Rate. |
{
"Number_Of_Asexual_Cycles_Without_Gametocytes": 1
}
|
Parasite_Mortality_Inverse_Width |
float |
0.1 |
1000000 |
10 |
The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function: probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) ). This parameter configures the inverse width relative to threshold value of mortality turn-on around threshold for parasite density. |
{
"Parasite_Mortality_Inverse_Width": 100
}
|
Parasite_Mortality_Threshold |
double |
NA |
NA |
2000000 |
The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function: probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) ) This parameter configures the parasite-density mortality threshold for a simulation. |
{
"Parasite_Mortality_Threshold": 3000000
}
|
Parasite_Severe_Inverse_Width |
float |
0.1 |
1000000 |
10 |
The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function: probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) ) This parameter configures the inverse width relative to threshold value of severe disease turn-on around threshold for parasite density. |
{
"Parasite_Severe_Inverse_Width": 48.66825295
}
|
Parasite_Severe_Threshold |
float |
0 |
3.40E+38 |
1000000 |
The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function: probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) ) This parameter configures parasite density threshold level that results in severe disease. |
{
"Parasite_Severe_Threshold": 253871
}
|
Parasite_Smear_Sensitivity |
float |
0.0001 |
100 |
0.1 |
The number of microliters of blood tested to find single parasites in a traditional smear (corresponds to inverse parasites/microliters sensitivity). |
{
"Parasite_Smear_Sensitivity": 0.1
}
|
Parasite_Switch_Type |
enum |
NA |
NA |
CONSTANT_SWITCH_RATE_2VARS |
The parasite switch type for erythrocyte surface antigens. Possible values are:
|
{
"Parasite_Switch_Type": "RATE_PER_PARASITE_7VARS"
}
|
Population dynamics¶
The following parameters determine characteristics related to population dynamics, such as age distribution, births, deaths, and gender. The values set here generally interact closely with values in the demographics file.
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"
}
|
Base_Population_Scale_Factor |
float |
0 |
3.40E+38 |
1 |
The 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. |
{
"Base_Population_Scale_Factor": 0.0001
}
|
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 that allows the birth rate 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. |
{
"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 identical or heterogeneous characteristics. Set to false (0) to give all newborns identical characteristics; set to true (1) to allow for heterogeneity in traits such as sickle-cell status. Enable_Birth must be set to true (1). |
{
"Enable_Birth": 1,
"Enable_Demographics_Birth": 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_Gender |
boolean |
0 |
1 |
1 |
Controls whether or not gender ratios are drawn from a Gaussian distribution or a 50/50 draw. When set to 1 (true), the ratio of males to females is drawn from a Gaussian distribution with a mean of 0.5 and a standard deviation of 0.01. When set to 0 (false), the gender ratio is based on a 50/50 draw. |
{
"Enable_Demographics_Gender": 1
}
|
Enable_Demographics_Risk |
boolean |
0 |
1 |
0 |
Controls whether or not the simulation includes the impact of disease risk in demographics. This parameter is not used in this simulation type. |
{
"Enable_Demographics_Risk": 1
}
|
Enable_Vital_Dynamics |
boolean |
0 |
1 |
1 |
Controls whether or not births and deaths occur in the simulation. Births and deaths must be individually enabled and set. |
{
"Enable_Vital_Dynamics":1,
"Enable_Birth": 1,
"Death_Rate_Dependence": "NONDISEASE_MORTALITY_OFF",
"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_Birth |
float |
0 |
3.40E+38 |
1 |
Scale factor for birth rate, as provided by the demographics file (see NodeAttributes parameters). Enable_Birth must be set to 1. |
{
"x_Birth": 1
}
|
Sampling¶
The following parameters determine how a population is sampled in the simulation. While you may want every agent (individual object) to represent a single person, you can often optimize CPU time with without degrading the accuracy of the simulation but having an agent represent multiple people. The sampling rate may be adapted to have a higher or lower sampling rate for particular regions or age groups.
For vector and malaria simulations, the same concepts apply to sampling the vector population.
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 |
Threshold on acquisition immunity at which to apply immunity dependent downsampling. Individual_Sampling_Type must set to ADAPTED_SAMPLING_BY_IMMUNE_STATE. |
{
"Individual_Sampling_Type": "ADAPTED_SAMPLING_BY_IMMUNE_STATE",
"Immune_Threshold_For_Downsampling": 0.5
}
|
Individual_Sampling_Type |
enum |
NA |
NA |
TRACK_ALL |
The type of individual human sampling. 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
}
|
Mosquito_Weight |
integer |
1 |
10000 |
1 |
The value indicating how many mosquitoes are represented by a sample mosquito in the simulation. Vector_Sampling_Type must be set to SAMPLE_IND_VECTORS. |
{
"Mosquito_Weight": 10
}
|
Relative_Sample_Rate_Immune |
float |
0.001 |
1 |
0.1 |
The relative sampling rate for people who have acquired immunity through recovery or vaccination. |
{
"Relative_Sample_Rate_Immune": 0.1
}
|
Sample_Rate_0_18mo |
float |
0.001 |
1000 |
1 |
For age-adapted sampling, the relative sampling rate for infants age 0 to 18 months. Individual_Sampling_Type must be set to ADAPTED_SAMPLING_BY_AGE_GROUP or ADAPTED_SAMPLING_BY_AGE_GROUP_AND_POP_SIZE. |
{
"Sample_Rate_0_18mo": 1
}
|
Sample_Rate_10_14 |
float |
0.001 |
1000 |
1 |
For age-adapted sampling, the relative sampling rate for children age 10 to 14 years. Individual_Sampling_Type must be set to ADAPTED_SAMPLING_BY_AGE_GROUP or ADAPTED_SAMPLING_BY_AGE_GROUP_AND_POP_SIZE. |
{
"Sample_Rate_10_14": 1
}
|
Sample_Rate_15_19 |
float |
0.001 |
1000 |
1 |
For age-adapted sampling, the relative sampling rate for adolescents age 15 to 19 years. Individual_Sampling_Type must be set to ADAPTED_SAMPLING_BY_AGE_GROUP or ADAPTED_SAMPLING_BY_AGE_GROUP_AND_POP_SIZE. |
{
"Sample_Rate_15_19": 1
}
|
Sample_Rate_18mo_4yr |
float |
0.001 |
1000 |
1 |
For age-adapted sampling, the relative sampling rate for children age 18 months to 4 years. Individual_Sampling_Type must be set to ADAPTED_SAMPLING_BY_AGE_GROUP or ADAPTED_SAMPLING_BY_AGE_GROUP_AND_POP_SIZE. |
{
"Sample_Rate_18mo_4yr": 1
}
|
Sample_Rate_20_plus |
float |
0.001 |
1000 |
1 |
For age-adapted sampling, the relative sampling rate for adults age 20 and older. Individual_Sampling_Type must be set to ADAPTED_SAMPLING_BY_AGE_GROUP or ADAPTED_SAMPLING_BY_AGE_GROUP_AND_POP_SIZE. |
{
"Sample_Rate_20_plus": 1
}
|
Sample_Rate_5_9 |
float |
0.001 |
1000 |
1 |
For age-adapted sampling, the relative sampling rate for children age 5 to 9 years. Individual_Sampling_Type must be set to ADAPTED_SAMPLING_BY_AGE_GROUP or ADAPTED_SAMPLING_BY_AGE_GROUP_AND_POP_SIZE. |
{
"Sample_Rate_5_9": 1
}
|
Sample_Rate_Birth |
float |
0.001 |
1000 |
1 |
For age-adapted sampling, the relative sampling rate for births. Individual_Sampling_Type must be set to ADAPTED_SAMPLING_BY_AGE_GROUP or ADAPTED_SAMPLING_BY_AGE_GROUP_AND_POP_SIZE. |
{
"Sample_Rate_Birth": 1
}
|
Vector_Sampling_Type |
enum |
NA |
NA |
TRACK_ALL_VECTORS |
The type of vector sampling used. Possible values are:
|
{
"Vector_Sampling_Type": "TRACK_ALL_VECTORS"
}
|
Scalars and multipliers¶
The following parameters scale or multiply values set in other areas of the configuration file or input data 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.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Acquire_Modifier |
float |
0 |
1 |
1 |
Modifier of the probability of successful infection of a mosquito by a malaria-infected individual, given the individual’s infectiousness. |
{
"Vector_Species_Params": {
"aegypti": {
"Acquire_Modifier": 1
}
}
}
|
Antibody_IRBC_Kill_Rate |
double |
NA |
NA |
2 |
The scale factor multiplied by antibody level to produce the rate of clearance of the infected red blood cell (IRBC) population. |
{
"Antibody_IRBC_Kill_Rate": 1.595
}
|
Aquatic_Arrhenius_1 |
float |
0 |
1.00E+15 |
8.42E+10 |
The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the daily rate of fractional progression of mosquito aquatic development (egg-hatching through emergence). This duration is a decreasing function of temperature. The variable a1 is a temperature-independent scale factor on development rate. |
{
"Vector_Species_Params": {
"aegypti": {
"Aquatic_Arrhenius_1": 9752291.727
}
}
}
|
Aquatic_Arrhenius_2 |
float |
0 |
1.00E+15 |
8328 |
The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the daily rate of fractional progression of mosquito aquatic development (egg-hatching through emergence). This duration is a decreasing function of temperature. The variable a2 governs how quickly the rate changes with temperature. |
{
"Vector_Species_Params": {
"arabiensis": {
"Aquatic_Arrhenius_2": 8328
}
}
}
|
Base_Population_Scale_Factor |
float |
0 |
3.40E+38 |
1 |
The 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. |
{
"Base_Population_Scale_Factor": 0.0001
}
|
Birth_Rate_Time_Dependence |
enum |
NA |
NA |
NONE |
A scale factor that allows the birth rate 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"
}
|
Cycle_Arrhenius_1 |
float |
0 |
1.00E+15 |
4.09E+10 |
The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the mosquito feeding cycle rate. This duration is a decreasing function of temperature. The variable a1 is a temperature-independent scale factor on feeding rate. Temperature_Dependent_Feeding_Cycle must be set to ARRHENIUS_DEPENDENCE. |
{
"Temperature_Dependent_Feeding_Cycle": "ARRHENIUS_DEPENDENCE",
"Vector_Species_Params": {
"arabiensis": {
"Cycle_Arrhenius_1": 99,
"Cycle_Arrhenius_2": 88
}
}
}
|
Cycle_Arrhenius_2 |
float |
0 |
1.00E+15 |
7740 |
The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the mosquito feeding cycle rate. This duration is a decreasing function of temperature. The variable a2 is a temperature-independent scale factor on feeding rate. Temperature_Dependent_Feeding_Cycle must be set to ARRHENIUS_DEPENDENCE. |
{
"Temperature_Dependent_Feeding_Cycle": "ARRHENIUS_DEPENDENCE",
"Vector_Species_Params": {
"arabiensis": {
"Cycle_Arrhenius_1": 99,
"Cycle_Arrhenius_2": 88
}
}
}
|
Cycle_Arrhenius_Reduction_Factor |
float |
0 |
1 |
1 |
The scale factor applied to cycle duration (from oviposition to oviposition) to reduce the duration when primary follicles are at stage II rather than I in the case of newly emerged females. Temperature_Dependent_Feeding_Cycle must be set to ARRHENIUS_DEPENDENCE. |
{
"Temperature_Dependent_Feeding_Cycle": "ARRHENIUS_DEPENDENCE",
"Vector_Species_Params": {
"funestus": {
"Cycle_Arrhenius_Reduction_Factor": 0.44
}
}
}
|
Egg_Arrhenius1 |
float |
0 |
1.00E+10 |
6.16E+07 |
The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the daily rate of mosquito egg hatching. This duration is a decreasing function of temperature. The variable a1 is a temperature-independent scale factor on hatching rate. Enable_Temperature_Dependent_Egg_Hatching must be set to 1. |
{
"Enable_Temperature_Dependent_Egg_Hatching": 1,
"Egg_Arrhenius1": 61599956,
"Egg_Arrhenius2": 5754
}
|
Egg_Arrhenius2 |
float |
0 |
1.00E+10 |
5754.03 |
The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the daily rate of mosquito egg hatching. This duration is a decreasing function of temperature. The variable a2 is a temperature-dependent scale factor on hatching rate. Enable_Temperature_Dependent_Egg_Hatching must be set to 1. |
{
"Enable_Temperature_Dependent_Egg_Hatching": 1,
"Egg_Arrhenius1": 61599956,
"Egg_Arrhenius2": 5754
}
|
Genome_Markers |
array of strings |
NA |
NA |
NA |
A list of the names (strings) of genome marker(s) that represent the genetic components in a strain of an infection. |
{
"Genome_Markers": [
"D6",
"W2"
]
}
|
Infected_Arrhenius_1 |
float |
0 |
1.00E+15 |
1.17E+11 |
The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the daily rate of fractional progression of infected mosquitoes to an infectious state. The duration of sporogony is a decreasing function of temperature. The variable a1 is a temperature-independent scale factor on the progression rate to infectiousness. |
{
"Vector_Species_Params": {
"arabiensis": {
"Acquire_Modifier": 0.2,
"Adult_Life_Expectancy": 10,
"Anthropophily": 0.95,
"Aquatic_Arrhenius_1": 84200000000,
"Aquatic_Arrhenius_2": 8328,
"Aquatic_Mortality_Rate": 0.1,
"Cycle_Arrhenius_1": 0,
"Cycle_Arrhenius_2": 0,
"Cycle_Arrhenius_Reduction_Factor": 0,
"Days_Between_Feeds": 3,
"Egg_Batch_Size": 100,
"Immature_Duration": 4,
"Indoor_Feeding_Fraction": 0.5,
"Infected_Arrhenius_1": 117000000000,
"Infected_Arrhenius_2": 8336,
"Infected_Egg_Batch_Factor": 0.8,
"Infectious_Human_Feed_Mortality_Factor": 1.5,
"Larval_Habitat_Types": {
"TEMPORARY_RAINFALL": 11250000000
},
"Nighttime_Feeding_Fraction": 1,
"Transmission_Rate": 0.5
}
}
}
|
Infected_Arrhenius_2 |
float |
0 |
1.00E+15 |
8340 |
The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the daily rate of fractional progression of infected mosquitoes to an infectious state. The duration of sporogony is a decreasing function of temperature. The variable a2 is a temperature-dependent scale factor on the progression rate to infectiousness. |
{
"Vector_Species_Params": {
"arabiensis": {
"Acquire_Modifier": 0.2,
"Adult_Life_Expectancy": 10,
"Anthropophily": 0.95,
"Aquatic_Arrhenius_1": 84200000000,
"Aquatic_Arrhenius_2": 8328,
"Aquatic_Mortality_Rate": 0.1,
"Cycle_Arrhenius_1": 0,
"Cycle_Arrhenius_2": 0,
"Cycle_Arrhenius_Reduction_Factor": 0,
"Days_Between_Feeds": 3,
"Egg_Batch_Size": 100,
"Immature_Duration": 4,
"Indoor_Feeding_Fraction": 0.5,
"Infected_Arrhenius_1": 117000000000,
"Infected_Arrhenius_2": 8336,
"Infected_Egg_Batch_Factor": 0.8,
"Infectious_Human_Feed_Mortality_Factor": 1.5,
"Larval_Habitat_Types": {
"TEMPORARY_RAINFALL": 11250000000
},
"Nighttime_Feeding_Fraction": 1,
"Transmission_Rate": 0.5
}
}
}
|
Infected_Egg_Batch_Factor |
float |
0 |
10 |
0.8 |
The dimensionless factor used to modify mosquito egg batch size in order to account for reduced fertility effects arising due to infection (e.g. when females undergo sporogony). |
{
"Vector_Species_Params": {
"arabiensis": {
"Acquire_Modifier": 0.2,
"Adult_Life_Expectancy": 10,
"Anthropophily": 0.95,
"Aquatic_Arrhenius_1": 84200000000,
"Aquatic_Arrhenius_2": 8328,
"Aquatic_Mortality_Rate": 0.1,
"Cycle_Arrhenius_1": 0,
"Cycle_Arrhenius_2": 0,
"Cycle_Arrhenius_Reduction_Factor": 0,
"Days_Between_Feeds": 3,
"Egg_Batch_Size": 100,
"Immature_Duration": 4,
"Indoor_Feeding_Fraction": 0.5,
"Infected_Arrhenius_1": 117000000000,
"Infected_Arrhenius_2": 8336,
"Infected_Egg_Batch_Factor": 0.8,
"Infectious_Human_Feed_Mortality_Factor": 1.5,
"Larval_Habitat_Types": {
"TEMPORARY_RAINFALL": 11250000000
},
"Nighttime_Feeding_Fraction": 1,
"Transmission_Rate": 0.5
}
}
}
|
Infectious_Human_Feed_Mortality_Factor |
float |
0 |
1000 |
1.5 |
The (dimensionless) factor used to modify the death rate of mosquitoes when feeding on humans, to account for the higher mortality rate infected mosquitoes experience during human feeds versus uninfected mosquitoes. |
{
"Vector_Species_Params": {
"arabiensis": {
"Acquire_Modifier": 0.2,
"Adult_Life_Expectancy": 10,
"Anthropophily": 0.95,
"Aquatic_Arrhenius_1": 84200000000,
"Aquatic_Arrhenius_2": 8328,
"Aquatic_Mortality_Rate": 0.1,
"Cycle_Arrhenius_1": 0,
"Cycle_Arrhenius_2": 0,
"Cycle_Arrhenius_Reduction_Factor": 0,
"Days_Between_Feeds": 3,
"Egg_Batch_Size": 100,
"Immature_Duration": 4,
"Indoor_Feeding_Fraction": 0.5,
"Infected_Arrhenius_1": 117000000000,
"Infected_Arrhenius_2": 8336,
"Infected_Egg_Batch_Factor": 0.8,
"Infectious_Human_Feed_Mortality_Factor": 1.5,
"Larval_Habitat_Types": {
"TEMPORARY_RAINFALL": 11250000000
},
"Nighttime_Feeding_Fraction": 1,
"Transmission_Rate": 0.5
}
}
}
|
Infectivity_Exponential_Baseline |
float |
0 |
1 |
0 |
The scale factor applied to Base_Infectivity at the beginning of a simulation, before the infectivity begins to grow exponentially. Infectivity_Scale_Type must be set to EXPONENTIAL_FUNCTION_OF_TIME. |
{
"Infectivity_Exponential_Baseline": 0.1,
"Infectivity_Exponential_Delay": 90,
"Infectivity_Exponential_Rate": 45,
"Infectivity_Scale_Type": "EXPONENTIAL_FUNCTION_OF_TIME"
}
|
Larval_Density_Mortality_Scalar |
float |
0.01 |
1000 |
10 |
A scale factor in the formula determining the larval-age-dependent mortality for the GRADUAL_INSTAR_SPECIFIC and LARVAL_AGE_DENSITY_DEPENDENT_MORTALITY_ONLY models. |
{
"Larval_Density_Mortality_Scalar": 1.0
}
|
Newborn_Biting_Risk_Multiplier |
float |
0 |
1 |
0.2 |
The scale factor that defines the y-intercept of the linear portion of the biting risk curve when Age_Dependent_Biting_Risk_Type is set to LINEAR. |
{
"Newborn_Biting_Risk_Multiplier": 0.2
}
|
Nonspecific_Antibody_Growth_Rate_Factor |
float |
0 |
1000 |
0.5 |
The factor that adjusts Antibody_Capacity_Growth_Rate for less immunogenic surface proteins, called minor epitopes. |
{
"Nonspecific_Antibody_Growth_Rate_Factor": 0.5
}
|
Population_Scale_Type |
enum |
NA |
NA |
USE_INPUT_FILE |
The method to use for scaling the initial population specified in the demographics input file. Possible values are:
|
{
"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,
"Immunity_Acquisition_Factor": 0.9
}
|
Resistance |
JSON object |
NA |
NA |
NA |
Specifies the drug resistance multiplier. This parameter is used with the infection strain indicated in the Genome_Markers parameter. The value specified for the parameter modifier, which is part of the Resistance JSON object, is multiplied with associated parameter. If the infection strain contains different markers, then the same modifiers for each marker are multiplied together before being multiplied with the associated parameter. |
{
"parameters": {
"Genome_Markers": [
"A",
"B"
],
"Malaria_Drug_Params": {
"Artemether_Lumefantrine": {
"Bodyweight_Exponent": 0,
"Drug_Cmax": 1000,
"Drug_Decay_T1": 1,
"Drug_Decay_T2": 1,
"Drug_Dose_Interval": 1,
"Drug_Fulltreatment_Doses": 3,
"Drug_Gametocyte02_Killrate": 2.3,
"Drug_Gametocyte34_Killrate": 2.3,
"Drug_GametocyteM_Killrate": 0,
"Drug_Hepatocyte_Killrate": 0,
"Drug_PKPD_C50": 100,
"Drug_Vd": 10,
"Max_Drug_IRBC_Kill": 3.45
"Resistance": {
"A": {
"Max_IRBC_Kill_Modifier": 0.05,
"PKPD_C50_Modifier": 1.0
},
"B": {
"Max_IRBC_Kill_Modifier": 0.25,
"PKPD_C50_Modifier": 1.0
}
}
}
}
}
}
|
Susceptibility_Scaling_Rate |
float |
0 |
3.40282e+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. Possible values are:
|
{
"Susceptibility_Scaling_Type": "CONSTANT_SUSCEPTIBILITY"
}
|
Vector_Migration_Food_Modifier |
float |
0 |
3.40E+38 |
0 |
The preference of a vector to migrate toward a node currently occupied by humans, independent of the number of humans in the node. Used only when Vector_Sampling_Type is set to TRACK_ALL_VECTORS. Enable_Vector_Migration must be set to 1. |
{
"Vector_Migration_Food_Modifier": 1.0
}
|
Vector_Migration_Habitat_Modifier |
float |
0 |
3.40E+38 |
0 |
The preference of a vector to migrate toward a node with more habitat. Only used when Vector_Sampling_Type is set to TRACK_ALL_VECTORS. Enable_Vector_Migration must be set to 1. |
{
"Vector_Migration_Habitat_Modifier": 1.0
}
|
Vector_Migration_Modifier_Equation |
enum |
NA |
NA |
LINEAR |
The functional form of vector migration modifiers. Enable_Vector_Migration must be set to 1. Possible values are: LINEAR EXPONENTIAL |
{
"Vector_Migration_Modifier_Equation": "EXPONENTIAL"
}
|
Vector_Migration_Stay_Put_Modifier |
float |
0 |
3.40E+38 |
0 |
The preference of a vector to remain in its current node rather than migrate to another node. Used only when Vector_Sampling_Type is set to TRACK_ALL_VECTORS. Enable_Vector_Migration must be set to 1. |
{
"Vector_Migration_Stay_Put_Modifier": 1.0
}
|
x_Air_Migration |
float |
0 |
3.40E+38 |
1 |
Scale factor for the rate of migration by air, as provided by the migration file. Enable_Air_Migration must be set to 1. |
{
"x_Air_Migration": 1
}
|
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, as provided by the demographics file (see Complex distributions parameters). Enable_Natural_Mortality must be set to 1. |
{
"x_Other_Mortality": 1
}
|
x_Regional_Migration |
float |
0 |
3.40E+38 |
1 |
Scale factor for the rate of migration by road vehicle, as provided by the migration file. Enable_Regional_Migration must be set to 1. |
{
"x_Regional_Migration": 1
}
|
x_Sea_Migration |
float |
0 |
3.40E+38 |
1 |
Scale factor for the rate of migration by sea, as provided by the migration file. Enable_Sea_Migration must be set to 1. |
{
"x_Sea_Migration": 1
}
|
x_Temporary_Larval_Habitat |
double |
NA |
NA |
1 |
Scale factor for the habitat size for all mosquito populations. |
{
"x_Temporary_Larval_Habitat": 1
}
|
x_Vector_Migration_Local |
float |
0 |
3.40E+38 |
1 |
Scale factor for the rate of vector migration to adjacent nodes, as provided by the vector migration file. Enable_Vector_Migration must be set to 1. |
{
"x_Vector_Migration_Local": 1.0
}
|
x_Vector_Migration_Regional |
float |
0 |
3.40E+38 |
1 |
Scale factor for the rate of vector migration to non-adjacent nodes, as provided by the vector migration file. Enable_Vector_Migration must be set to 1. |
{
"x_Vector_Migration_Regional": 1.0
}
|
Simulation setup¶
These parameters determine the basic setup of a simulation including the type of simulation you are running, such as “GENERIC_SIM” or “MALARIA_SIM”, the simulation duration, and the time step duration.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Base_Year |
float |
1900 |
2200 |
2015 |
The absolute time in years when the simulation begins. This can be combined with CampaignEventByYear to trigger campaign events. This parameter is not used in this simulation type. |
{
"Base_Year": 1960
}
|
Config_Name |
string |
NA |
NA |
UNINITIALIZED STRING |
The optional, user-supplied title naming a configuration file. |
{
"Config_Name": "My_First_Config"
}
|
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_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. Infectivity_Scale_Type must be set to CONSTANT_INFECTIVITY. This parameter is not used in this simulation type. |
{
"Exposure_Skipping": 1
}
|
Listed_Events |
array of strings |
NA |
NA |
[] |
The list of valid, user-defined events that will be included in the campaign. Any event used in the campaign must either be one of the built-in events or in this list. |
{
"Listed_Events": [
"Vaccinated",
"VaccineExpired"
]
}
|
Malaria_Model |
enum |
NA |
NA |
MALARIA_MECHANISTIC_MODEL |
The type of malaria model used in a simulation. Possible values are:
Note All malaria simulations should use MALARIA_MECHANISTIC_MODEL. The others are placeholder values. |
{
"Malaria_Model": "MALARIA_MECHANISTIC_MODEL"
}
|
Memory_Usage_Halting_Threshold_Working_Set_MB |
integer |
0 |
1.00E+06 |
8000 |
The maximum size (in MB) of working set memory before the system throws an exception and halts. |
{
"Memory_Usage_Halting_Threshold_Working_Set_MB": 4000
}
|
Memory_Usage_Warning_Threshold_Working_Set_MB |
integer |
0 |
1.00E+06 |
7000 |
The maximum size (in MB) of working set memory before memory usage statistics are written to the log regardless of log level. |
{
"Memory_Usage_Warning_Threshold_Working_Set_MB": 3500
}
|
Node_Grid_Size |
float |
0.00416 |
90 |
0.004167 |
The spatial resolution indicating the node grid size for a simulation in degrees. |
{
"Node_Grid_Size": 0.042
}
|
Run_Number |
integer |
0 |
2147480000 |
1 |
Sets the random number seed through a bit manipulation process. When running a multi-core simulation, combines with processor rank to produce independent random number streams for each process. |
{
"Run_Number": 1
}
|
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. By default, no serialized state is saved. The serialized population files can then be loaded at the beginning of a simulation using Serialized_Population_Filenames and Serialized_Population_Path. |
{
"Serialization_Time_Steps": [
0,
10
]
}
|
|
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"
}
|
Sigmoid_Max |
float |
0 |
1 |
1 |
The maximum value for defining a sigmoidal curve. |
{
"Sigmoid_Min": 1
"Sigmoid_Max": 1
"Sigmoid_Mid": 2000
"Sigmoid_Rate": 1
}
|
Sigmoid_Mid |
float |
0 |
3.40E+38 |
2000 |
The mid value for defining a sigmoidal curve. |
{
"Sigmoid_Min": 1
"Sigmoid_Max": 1
"Sigmoid_Mid": 2000
"Sigmoid_Rate": 1
}
|
Sigmoid_Min |
float |
0 |
1 |
1 |
The minimum value for defining a sigmoidal curve. |
{
"Sigmoid_Min": 1
"Sigmoid_Max": 1
"Sigmoid_Mid": 2000
"Sigmoid_Rate": 1
}
|
Sigmoid_Rate |
float |
-100 |
100 |
1 |
The rate applied to the sigmoidal curve. |
{
"Sigmoid_Min": 1
"Sigmoid_Max": 1
"Sigmoid_Mid": 2000
"Sigmoid_Rate": 1
}
|
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
}
|
Symptoms and diagnosis¶
The following parameters determine the characteristics of malaria diagnosis and symptoms as the disease progresses, such as anemia and fever. See Malaria symptoms and diagnostics for more information on malaria diagnostics and symptoms.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Anemia_Mortality_Inverse_Width |
float |
0.1 |
1000000 |
10 |
The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function: probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) ) This parameter configures the inverse width relative to threshold value of severe disease turn-on around threshold for anemia. |
{
"Anemia_Mortality_Inverse_Width": 150
}
|
Anemia_Mortality_Threshold |
double |
NA |
NA |
3 |
The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function: probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) ) This parameter configures the inverse width relative to threshold value of mortality turn-on around threshold for hemoglobin count in grams per deciliter (g/dL) at which 50% of individuals die per day. |
{
"Anemia_Mortality_Threshold": 1.5
}
|
Anemia_Severe_Inverse_Width |
float |
0.1 |
1000000 |
10 |
The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function: probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) ) This parameter configures the inverse width relative to threshold value of severe disease turn-on around threshold for anemia. |
{
"Anemia_Severe_Inverse_Width": 20
}
|
Anemia_Severe_Threshold |
float |
0 |
100 |
5 |
The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function: probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) ) This parameter configures the severe disease threshold level for anemia. Threshold units are in grams per deciliter (g/dL). |
{
"Anemia_Severe_Threshold": 3.0
}
|
Clinical_Fever_Threshold_High |
float |
0 |
15 |
1 |
The fever threshold (in degrees Celsius above normal) to start a clinical incident. |
{
"Clinical_Fever_Threshold_High": 1.5
}
|
Clinical_Fever_Threshold_Low |
float |
0 |
5 |
1 |
The fever threshold (in degrees Celsius above normal) to end a clinical incident. |
{
"Clinical_Fever_Threshold_Low": 0.5
}
|
Erythropoiesis_Anemia_Effect |
float |
0 |
1000 |
3.5 |
The exponential rate of increased red-blood-cell production from reduced red-blood-cell availability. |
{
"Erythropoiesis_Anemia_Effect": 3
}
|
Fever_Detection_Threshold |
float |
0.5 |
5 |
1 |
The level of body temperature above normal (defined as 37 C), in degrees Celsius, corresponding to detectable fever. |
{
"Fever_Detection_Threshold": 1
}
|
Fever_IRBC_Kill_Rate |
float |
0 |
1000 |
0.15 |
The maximum kill rate for infected red blood cells due to the inflammatory innate immune response. As fever increases above 38.5 degrees Celsius, the kill rate becomes successively higher along a sigmoidal curve approaching this rate. |
{
"Fever_IRBC_Kill_Rate": 1.4
}
|
Fever_Mortality_Inverse_Width |
float |
0.1 |
1000000 |
10 |
The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function: probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) ) This parameter configures the inverse width relative to threshold value of mortality turn-on around threshold for fever. |
{
"Fever_Mortality_Inverse_Width": 1000
}
|
Fever_Mortality_Threshold |
double |
NA |
NA |
3 |
The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function: probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) ) This parameter configures the fever mortality threshold, in units of degrees Celsius above normal body temperature (defined as 37 C). |
{
"Fever_Mortality_Threshold": 10
}
|
Fever_Severe_Inverse_Width |
float |
0.1 |
1000000 |
10 |
The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function: probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) ) This parameter configures the inverse width relative to threshold value of mortality turn-on around threshold for fever. |
{
"Fever_Severe_Inverse_Width": 18.7
}
|
Fever_Severe_Threshold |
float |
0 |
10 |
1.5 |
The probability of having severe or fatal malaria is calculated according to three causes: anemia, fever as a pro-inflammatory correlate of cerebral malaria, and total parasite density. The probability associated with each factor is configured with two parameters of a sigmoidal function: probability = 1.0 / ( 1.0 + exp( (threshold-variable) / (threshold/invwidth) ) ) This parameter configures the threshold (in degrees Celsius above normal) for fever indicating severe disease. |
{
"Fever_Severe_Threshold": 2.5
}
|
Min_Days_Between_Clinical_Incidents |
float |
0 |
1000000 |
3 |
The number of days with fever below the low-threshold before a new incident can start, when fever again exceeds the high-threshold. |
{
"Min_Days_Between_Clinical_Incidents": 14
}
|
New_Diagnostic_Sensitivity |
float |
0.0001 |
100000 |
0.01 |
The number of microliters of blood tested to find single parasites in a new diagnostic (corresponds to inverse parasites/microliters sensitivity). |
{
"New_Diagnostic_Sensitivity": 0.025
}
|
Vector control¶
The following parameters determine the characteristics of campaign interventions aimed at vector control, such as the homing endonuclease gene (HEG), sugar feeding, and Wolbachia infection. When, to whom, and how those interventions are distributed is determined by the campaign file.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
HEG_Fecundity_Limiting |
float |
0 |
1 |
0 |
The fractional reduction in the number of eggs laid by a female mosquito who is homozygous in the homing endonuclease gene (HEG). HEG_Model must be set to GERMLINE_HOMING, EGG_HOMING, DUAL_GERMLINE_HOMING, or DRIVING_Y. |
{
"HEG_Model": "EGG_HOMING",
"HEG_Fecundity_Limiting": 0.7
}
|
HEG_Homing_Rate |
float |
0 |
1 |
0 |
The fractional redistribution from heterozygous offspring to the homozygous homing endonuclease gene (HEG) behavior. HEG_Model” must be set to GERMLINE_HOMING, EGG_HOMING, DUAL_GERMLINE_HOMING, or DRIVING_Y. |
{
"HEG_Model": "EGG_HOMING",
"HEG_Homing_Rate": 0.4
}
|
HEG_Model |
enum |
NA |
NA |
OFF |
This parameter determines the structure of homing endonuclease gene (HEG) dynamics. Possible values are:
|
{
"HEG_Model": "EGG_HOMING",
"HEG_Homing_Rate": 0.4,
"HEG_Fecundity_Limiting": 0.9
}
|
Vector_Sugar_Feeding_Frequency |
enum |
NA |
NA |
VECTOR_SUGAR_FEEDING_NONE |
The frequency of sugar feeding by a female mosquito. Used is used in conjunction with the SugarTrap and OvipositionTrap interventions. Vector_Sampling_Type must be set to TRACK_ALL_VECTORS or SAMPLE_IND_VECTORS. Possible values are:
|
{
"Vector_Sugar_Feeding_Frequency": "VECTOR_SUGAR_FEEDING_NONE"
}
|
Wolbachia_Infection_Modification |
float |
0 |
100 |
1 |
The change in vector susceptibility to infection due to a Wolbachia infection. |
{
"Wolbachia_Infection_Modification": 1.0
}
|
Wolbachia_Mortality_Modification |
float |
0 |
100 |
1 |
The change in vector mortality due to a Wolbachia infection. |
{
"Wolbachia_Infection_Modification": 0.0
}
|
Vector life cycle¶
The following parameters determine the characteristics of the vector life cycle. Set the vector species to include in the simulation and the feeding, egg-laying, migration, and larval development habits of each using these parameters. The parameters for larval development related to habitat and climate are described in Larval habitat.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Adult_Life_Expectancy |
float |
0 |
730 |
10 |
The number of days an adult mosquito survives. The daily adult mortality rate is 1 / (value of this parameter). |
{
"Vector_Species_Params": {
"arabiensis": {
"Adult_Life_Expectancy": 5.9
}
}
}
|
Anthropophily |
float |
0 |
1 |
1 |
Mosquito preference for humans over animals, measured as a fraction of blood meals from human hosts. This dimensionless propensity is important in differentiating between mosquito species. |
{
"Vector_Species_Params": {
"aegypti": {
"Anthropophily": 0.95
}
}
}
|
Cycle_Arrhenius_1 |
float |
0 |
1.00E+15 |
4.09E+10 |
The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the mosquito feeding cycle rate. This duration is a decreasing function of temperature. The variable a1 is a temperature-independent scale factor on feeding rate. Temperature_Dependent_Feeding_Cycle must be set to ARRHENIUS_DEPENDENCE. |
{
"Temperature_Dependent_Feeding_Cycle": "ARRHENIUS_DEPENDENCE",
"Vector_Species_Params": {
"arabiensis": {
"Cycle_Arrhenius_1": 99,
"Cycle_Arrhenius_2": 88
}
}
}
|
Cycle_Arrhenius_2 |
float |
0 |
1.00E+15 |
7740 |
The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the mosquito feeding cycle rate. This duration is a decreasing function of temperature. The variable a2 is a temperature-independent scale factor on feeding rate. Temperature_Dependent_Feeding_Cycle must be set to ARRHENIUS_DEPENDENCE. |
{
"Temperature_Dependent_Feeding_Cycle": "ARRHENIUS_DEPENDENCE",
"Vector_Species_Params": {
"arabiensis": {
"Cycle_Arrhenius_1": 99,
"Cycle_Arrhenius_2": 88
}
}
}
|
Cycle_Arrhenius_Reduction_Factor |
float |
0 |
1 |
1 |
The scale factor applied to cycle duration (from oviposition to oviposition) to reduce the duration when primary follicles are at stage II rather than I in the case of newly emerged females. Temperature_Dependent_Feeding_Cycle must be set to ARRHENIUS_DEPENDENCE. |
{
"Temperature_Dependent_Feeding_Cycle": "ARRHENIUS_DEPENDENCE",
"Vector_Species_Params": {
"funestus": {
"Cycle_Arrhenius_Reduction_Factor": 0.44
}
}
}
|
Days_Between_Feeds |
float |
0 |
730 |
2 |
The number of days between female mosquito feeding attempts. In the cohort model, this value is converted to a feeding rate, equal to 1/(value of this parameter), and is used to determine the probability that one of each of the various feeding modes (animal host, human host, indoor, outdoor, etc) occurs. In the individual model, this parameter determines the number of days a vector waits between feeds. |
{
"Vector_Species_Params": {
"gambiae": {
"Days_Between_Feeds": 3
}
}
}
|
Drought_Egg_Hatch_Delay |
float |
0 |
1 |
0.33 |
Proportion of regular egg hatching that still occurs when habitat dries up. Enable_Drought_Egg_Hatch_Delay must be set to 1. |
{
"Enable_Drought_Egg_Hatch_Delay": 1,
"Drought_Egg_Hatch_Delay": 0.33
}
|
Egg_Arrhenius1 |
float |
0 |
1.00E+10 |
6.16E+07 |
The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the daily rate of mosquito egg hatching. This duration is a decreasing function of temperature. The variable a1 is a temperature-independent scale factor on hatching rate. Enable_Temperature_Dependent_Egg_Hatching must be set to 1. |
{
"Enable_Temperature_Dependent_Egg_Hatching": 1,
"Egg_Arrhenius1": 61599956,
"Egg_Arrhenius2": 5754
}
|
Egg_Arrhenius2 |
float |
0 |
1.00E+10 |
5754.03 |
The Arrhenius equation, \(a_1^{-a_2/T}\), with T in degrees Kelvin, parameterizes the daily rate of mosquito egg hatching. This duration is a decreasing function of temperature. The variable a2 is a temperature-dependent scale factor on hatching rate. Enable_Temperature_Dependent_Egg_Hatching must be set to 1. |
{
"Enable_Temperature_Dependent_Egg_Hatching": 1,
"Egg_Arrhenius1": 61599956,
"Egg_Arrhenius2": 5754
}
|
Egg_Batch_Size |
float |
0 |
10000 |
100 |
Number of eggs laid by one female mosquito that has fed successfully. The value of Egg_Batch_Size is used for both the number of female eggs and for the number of male eggs. For example, the default value of 100 means that there are 100 female eggs and 100 male eggs. |
{
"Vector_Species_Params": {
"arabiensis": {
"Egg_Batch_Size": 100
}
}
}
|
Egg_Hatch_Delay_Distribution |
enum |
NA |
NA |
NO_DELAY |
The distribution of the delay from oviposition to egg hatching. Possible values are:
|
{
"Egg_Hatch_Delay_Distribution": "EXPONENTIAL_DURATION",
"Mean_Egg_Hatch_Delay": 2
}
|
Egg_Hatch_Density_Dependence |
enum |
NA |
NA |
NO_DENSITY_DEPENDENCE |
The effect of larval density on egg hatching rate. Possible values are:
|
{
"Egg_Hatch_Density_Dependence": "NO_DENSITY_DEPENDENCE"
}
|
Egg_Saturation_At_Oviposition |
enum |
NA |
NA |
NO_SATURATION |
If laying all eggs from ovipositing females would overflow the larval habitat capacity on a given day, the means by which the viable eggs become saturated. Possible values are:
|
{
"Egg_Saturation_At_Oviposition": "SATURATION_AT_OVIPOSITION"
}
|
Enable_Egg_Mortality |
boolean |
0 |
1 |
0 |
Controls whether or not to include a daily mortality rate on the egg population, which is independent of climatic factors. |
{
"Enable_Egg_Mortality": 1,
}
|
Enable_Temperature_Dependent_Egg_Hatching |
boolean |
0 |
1 |
0 |
Controls whether or not temperature has an effect on egg hatching, defined by Egg_Arrhenius_1 and Egg_Arrhenius_2. |
{
"Enable_Temperature_Dependent_Egg_Hatching": 1,
"Egg_Arrhenius1": 61599956.864,
"Egg_Arrhenius2": 5754.033
}
|
Enable_Vector_Aging |
boolean |
0 |
1 |
0 |
Controls whether or not vectors undergo senescence as they age. |
{
"Enable_Vector_Aging": 1
}
|
Enable_Vector_Mortality |
boolean |
0 |
1 |
1 |
Controls whether or not vectors can die. Vector_Sampling_Type must be set to TRACK_ALL_VECTORS. |
{
"Vector_Sampling_Type": "TRACK_ALL_VECTORS",
"Enable_Vector_Mortality": 1
}
|
Human_Feeding_Mortality |
float |
0 |
1 |
0.1 |
The fraction of mosquitoes, for all species, that die while feeding on a human. |
{
"Human_Feeding_Mortality": 0.15
}
|
Immature_Duration |
float |
0 |
730 |
2 |
The number of days for larvae to develop into adult mosquitoes. The value is used to calculate mosquito development rate, which equals 1 / (value of this parameter). Development from immature to adult is not dependent on temperature. |
{
"Vector_Species_Params": {
"funestus": {
"Immature_Duration": 4
}
}
}
|
Indoor_Feeding_Fraction |
float |
0 |
1 |
1 |
The fraction (dimensionless) of feeds in which mosquitoes feed on humans indoors; the fraction of feeds on humans that occur outdoors equals 1 - (value of this parameter). |
{
"Vector_Species_Params": {
"arabiensis": {
"Acquire_Modifier": 0.2,
"Adult_Life_Expectancy": 10,
"Anthropophily": 0.95,
"Aquatic_Arrhenius_1": 84200000000,
"Aquatic_Arrhenius_2": 8328,
"Aquatic_Mortality_Rate": 0.1,
"Cycle_Arrhenius_1": 0,
"Cycle_Arrhenius_2": 0,
"Cycle_Arrhenius_Reduction_Factor": 0,
"Days_Between_Feeds": 3,
"Egg_Batch_Size": 100,
"Immature_Duration": 4,
"Indoor_Feeding_Fraction": 0.5,
"Infected_Arrhenius_1": 117000000000,
"Infected_Arrhenius_2": 8336,
"Infected_Egg_Batch_Factor": 0.8,
"Infectious_Human_Feed_Mortality_Factor": 1.5,
"Larval_Habitat_Types": {
"TEMPORARY_RAINFALL": 11250000000
},
"Nighttime_Feeding_Fraction": 1,
"Transmission_Rate": 0.5
}
}
}
|
Infected_Egg_Batch_Factor |
float |
0 |
10 |
0.8 |
The dimensionless factor used to modify mosquito egg batch size in order to account for reduced fertility effects arising due to infection (e.g. when females undergo sporogony). |
{
"Vector_Species_Params": {
"arabiensis": {
"Acquire_Modifier": 0.2,
"Adult_Life_Expectancy": 10,
"Anthropophily": 0.95,
"Aquatic_Arrhenius_1": 84200000000,
"Aquatic_Arrhenius_2": 8328,
"Aquatic_Mortality_Rate": 0.1,
"Cycle_Arrhenius_1": 0,
"Cycle_Arrhenius_2": 0,
"Cycle_Arrhenius_Reduction_Factor": 0,
"Days_Between_Feeds": 3,
"Egg_Batch_Size": 100,
"Immature_Duration": 4,
"Indoor_Feeding_Fraction": 0.5,
"Infected_Arrhenius_1": 117000000000,
"Infected_Arrhenius_2": 8336,
"Infected_Egg_Batch_Factor": 0.8,
"Infectious_Human_Feed_Mortality_Factor": 1.5,
"Larval_Habitat_Types": {
"TEMPORARY_RAINFALL": 11250000000
},
"Nighttime_Feeding_Fraction": 1,
"Transmission_Rate": 0.5
}
}
}
|
Larval_Density_Dependence |
enum |
NA |
NA |
UNIFORM_WHEN_OVERPOPULATION |
The functional form of mortality and growth delay for mosquito larvae based on population density. Possible values are:
|
{
"Larval_Density_Dependence": "GRADUAL_INSTAR_SPECIFIC"
}
|
Larval_Density_Mortality_Offset |
float |
0.0001 |
1000 |
0.1 |
An offset factor in the formula determining the larval-age-dependent mortality for the GRADUAL_INSTAR_SPECIFIC and LARVAL_AGE_DENSITY_DEPENDENT_MORTALITY_ONLY models. |
{
"Larval_Density_Mortality_Offset": 0.001
}
|
Larval_Density_Mortality_Scalar |
float |
0.01 |
1000 |
10 |
A scale factor in the formula determining the larval-age-dependent mortality for the GRADUAL_INSTAR_SPECIFIC and LARVAL_AGE_DENSITY_DEPENDENT_MORTALITY_ONLY models. |
{
"Larval_Density_Mortality_Scalar": 1.0
}
|
Max_Larval_Capacity |
float |
0 |
3.40E+38 |
1.00E+10 |
The maximum larval capacity. |
{
"Max_Larval_Capacity": 10000000000
}
|
Mean_Egg_Hatch_Delay |
float |
0 |
120 |
0 |
The mean delay in egg hatch time, in days, from the time of oviposition. Set the delay distribution with Egg_Hatch_Delay_Distribution. |
{
"Egg_Hatch_Delay_Distribution": "EXPONENTIAL_DURATION",
"Mean_Egg_Hatch_Delay": 2
}
|
Nighttime_Feeding_Fraction |
float |
0 |
1 |
1 |
The fraction of feeds on humans (for a specific mosquito species) that occur during the nighttime. Thus the fraction of feeds on humans that occur during the day equals 1 - (value of this parameter). |
{
"Vector_Species_Params": {
"arabiensis": {
"Acquire_Modifier": 0.2,
"Adult_Life_Expectancy": 10,
"Anthropophily": 0.95,
"Aquatic_Arrhenius_1": 84200000000,
"Aquatic_Arrhenius_2": 8328,
"Aquatic_Mortality_Rate": 0.1,
"Cycle_Arrhenius_1": 0,
"Cycle_Arrhenius_2": 0,
"Cycle_Arrhenius_Reduction_Factor": 0,
"Days_Between_Feeds": 3,
"Egg_Batch_Size": 100,
"Immature_Duration": 4,
"Indoor_Feeding_Fraction": 0.5,
"Infected_Arrhenius_1": 117000000000,
"Infected_Arrhenius_2": 8336,
"Infected_Egg_Batch_Factor": 0.8,
"Infectious_Human_Feed_Mortality_Factor": 1.5,
"Larval_Habitat_Types": {
"TEMPORARY_RAINFALL": 11250000000
},
"Nighttime_Feeding_Fraction": 1,
"Transmission_Rate": 0.5
}
}
}
|
Temperature_Dependent_Feeding_Cycle |
enum |
NA |
NA |
NO_TEMPERATURE_DEPENDENCE |
The effect of temperature on the duration between blood feeds. Possible values are:
|
{
"Temperature_Dependent_Feeding_Cycle": "BOUNDED_DEPENDENCE"
}
|
Vector_Migration_Food_Modifier |
float |
0 |
3.40E+38 |
0 |
The preference of a vector to migrate toward a node currently occupied by humans, independent of the number of humans in the node. Used only when Vector_Sampling_Type is set to TRACK_ALL_VECTORS. Enable_Vector_Migration must be set to 1. |
{
"Vector_Migration_Food_Modifier": 1.0
}
|
Vector_Migration_Modifier_Equation |
enum |
NA |
NA |
LINEAR |
The functional form of vector migration modifiers. Enable_Vector_Migration must be set to 1. Possible values are: LINEAR EXPONENTIAL |
{
"Vector_Migration_Modifier_Equation": "EXPONENTIAL"
}
|
Vector_Migration_Stay_Put_Modifier |
float |
0 |
3.40E+38 |
0 |
The preference of a vector to remain in its current node rather than migrate to another node. Used only when Vector_Sampling_Type is set to TRACK_ALL_VECTORS. Enable_Vector_Migration must be set to 1. |
{
"Vector_Migration_Stay_Put_Modifier": 1.0
}
|
Vector_Species_Names |
array of strings |
NA |
NA |
[] |
An array of vector species names for all species that are present in the simulation. You may provide any species names you desire, but the same names must be used in Vector_Species_Params. |
{
"Vector_Species_Names": [
"arabiensis",
"funestus",
"gambiae"
]
}
|
Vector_Species_Params |
JSON object |
NA |
NA |
NA |
A JSON object that lists each vector species, as defined in Vector_Species_Names, and the parameters that define them. |
{
"Vector_Species_Params": {
"arabiensis": {
"Acquire_Modifier": 1,
"Adult_Life_Expectancy": 10,
"Anthropophily"
"Aquatic_Arrhenius_1": 84200000000,
"Aquatic_Arrhenius_2": 8328,
"Aquatic_Mortality_Rate": 0.1,
"Days_Between_Feeds": 3,
"Egg_Batch_Size": 100,
"Immature_Duration": 4,
"Indoor_Feeding_Fraction": 0.5,
"Infected_Arrhenius_1": 117000000000,
"Infected_Arrhenius_2": 8336,
"Infected_Egg_Batch_Factor": 0.8,
"Infectious_Human_Feed_Mortality_Factor": 1.5,
"Larval_Habitat_Types": {
"TEMPORARY_RAINFALL": 1e10,
"WATER_VEGETATION": 1e6
},
"Transmission_Rate": 0.5
}
}
|
Vector_Sugar_Feeding_Frequency |
enum |
NA |
NA |
VECTOR_SUGAR_FEEDING_NONE |
The frequency of sugar feeding by a female mosquito. Used is used in conjunction with the SugarTrap and OvipositionTrap interventions. Vector_Sampling_Type must be set to TRACK_ALL_VECTORS or SAMPLE_IND_VECTORS. Possible values are:
|
{
"Vector_Sugar_Feeding_Frequency": "VECTOR_SUGAR_FEEDING_NONE"
}
|
Campaign parameters¶
The parameters described in this reference section can be added to the JSON (JavaScript Object Notation) formatted campaign file to determine the interventions that are used to control the spread of disease and where, when, how, and to whom the interventions are distributed. Additionally, the campaign file specifies the details of the outbreak of the disease itself. For more conceptual background on how to create a campaign file, see Creating campaigns.
In the configuration file, you must set the Enable_Interventions parameter to 1 and set the Campaign_Filename parameter to the name of the campaign file for the interventions to take place. The campaign file must be in the same directory as the configuration file.
The campaign file is hierarchically organized into logical groups of parameters that can have arbitrary levels of nesting. It contains an Events array of campaign events, each of which contains a JSON object describing the event coordinator, which in turn contains a nested JSON object describing the intervention. At the same level as the Events array is the boolean Use_Defaults to indicate whether or not to use the default values for required parameters that are not included in the file. It is common to include JSON keys for campaign name, event name, or intervention name; these are informational only and not used by EMOD.
The skeletal JSON example below illustrates the basic file structure (this does not include all required parameters for each object). Note that the nested JSON elements have been organized to best illustrate their hierarchy, but that many files in the Regression directory of the EMOD GitHub repository list the parameters and nested objects differently.
{
"Campaign_Name": "Campaign example",
"Use_Defaults": 1,
"Events": [{
"Event_Name": "The first event",
"class": "CampaignEvent",
"Event_Coordinator_Config": {
"class": "StandardInterventionDistributionEventCoordinator",
"Intervention_Config": {
"Intervention_Name": "The vaccine",
"class": "SimpleVaccine"
}
}
}, {
"Event_Name": "The second event",
"class": "CampaignEvent",
"Event_Coordinator_Config": {
"class": "StandardInterventionDistributionEventCoordinator",
"Intervention_Config": {
"Intervention_Name": "The disease outbreak",
"class": "OutbreakIndividual"
}
}
}]
}
The topics below contain only parameters available when using the malaria simulation type.
Note
Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
Events and event coordinators¶
Campaign events determine when and where an intervention is distributed during a campaign. Event coordinators are nested within the campaign event JSON object and determine who receives the intervention.
The following events and event coordinators are available to use with the malaria simulation type.
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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. |
{
"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 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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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. 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 |
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_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 |
NA |
NA |
0 |
When set to true (1), the intervention is only distributed to individuals that began the simulation in the node (i.e. those that claim the node as their residence). |
{
"Target_Residents_Only": 1
}
|
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 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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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 |
float |
0 |
3.40E+3 |
6 |
The initial amount of stock 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. |
{
"Initial_Amount": 10
}
|
Initial_Amount_Distribution_Type |
enum |
NA |
NA |
NOT_INITIALIZED |
The distribution type to set when initializing Initial_Amount. Possible values are:
|
{
"Initial_Amount_Distribution_Type": "FIXED_DURATION"
}
|
Initial_Amount_Max |
float |
0 |
3.40E+3 |
0 |
The maximum amount of initial stock when Initial_Amount_Distribution_Type is set to UNIFORM_DISTRIBUTION. |
{
"Initial_Amount_Distribution_Type": "UNIFORM_DURATION",
"Initial_Amount_Min": 5,
"Initial_Amount_Max": 10
}
|
Initial_Amount_Mean |
float |
0 |
3.40E+3 |
6 |
The average amount of initial stock when Initial_Amount_Distribution_Type is set to GAUSSIAN_DISTRIBUTION. |
{
"Initial_Amount_Distribution_Type": "GAUSSIAN_DISTRIBUTION",
"Initial_Amount_Std_Dev": 1,
"Initial_Amount_Mean": 5
}
|
Initial_Amount_Min |
float |
0 |
3.40E+3 |
0 |
The minimum amount of initial stock when Initial_Amount_Distribution_Type is set to UNIFORM_DISTRIBUTION. |
{
"Initial_Amount_Distribution_Type": "UNIFORM_DURATION",
"Initial_Amount_Min": 5,
"Initial_Amount_Max": 10
}
|
Initial_Amount_Std_Dev |
float |
0 |
3.40E+3 |
1 |
The standard deviation for the amount of initial stock when Initial_Amount_Distribution_Type is set to GAUSSIAN_DISTRIBUTION. |
{
"Initial_Amount_Distribution_Type": "GAUSSIAN_DISTRIBUTION",
"Initial_Amount_Std_Dev": 1,
"Initial_Amount_Mean": 5
}
|
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. 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"
}
]
}
|
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. 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 |
NA |
NA |
0 |
When set to true (1), the intervention is only distributed to individuals that began the simulation in the node (i.e. those that claim the node as their residence). |
{
"Target_Residents_Only": 1
}
|
Trigger_Condition_List |
array of strings |
NA |
NA |
NoTrigger |
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_Type": "FIXED_DURATION",
"Initial_Amount": 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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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. 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 |
3.40E+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 |
NA |
NA |
0 |
When set to true (1), the intervention is only distributed to individuals that began the simulation in the node (i.e. those that claim the node as their residence). |
{
"Target_Residents_Only": 1
}
|
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"
}
}
}]
}
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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_To_Broadcast. |
{
"Threshold_Type" : "COUNT",
"Action_List" :
[
{
"Threshold" : 20,
"Event_To_Broadcast" : "Action_Event_1"
},
{
"Threshold" : 30,
"Event_To_Broadcast" : "Action_Event_2"
}
]
}
|
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"
}
]
}
|
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 the threshold type and values and the actions to take when the specified conditions and parameters have been met, as configured in the Incidence_Counter parameter. Some of the values are specified in the following parameters: ** Threshold_Type ** ** Action_List ** ** Threshold ** ** Event_To_Broadcast ** |
{
"Responder" :
{
"Threshold_Type" : "COUNT",
"Action_List" :
[
{
"Threshold" : 5,
"Event_To_Broadcast" : "Action_Event_1"
},
{
"Threshold" : 10,
"Event_To_Broadcast" : "Action_Event_2"
}
]
}
}
|
Threshold |
float |
0 |
3.40E+03 |
0 |
The threshold value that triggers the specified action for the Event_To_Broadcast parameter. When you use the Threshold parameter you must also use the Event_To_Broadcast parameter. |
{
"Threshold_Type" : "COUNT",
"Action_List" :
[
{
"Threshold" : 20,
"Event_To_Broadcast" : "Action_Event_1"
},
{
"Threshold" : 30,
"Event_To_Broadcast" : "Action_Event_2"
}
]
}
|
Threshold_Type |
enum |
NA |
NA |
COUNT |
Threshold type. Possible values are: * COUNT, * PERCENTAGE. |
{
"Threshold_Type" : "COUNT",
"Action_List" :
[
{
"Threshold" : 20,
"Event_To_Broadcast" : "Action_Event_1"
},
{
"Threshold" : 30,
"Event_To_Broadcast" : "Action_Event_2"
}
]
}
|
Timesteps_Between_Repetitions |
integer |
-1 |
1000 |
-1 |
The repetition interval. |
{
"class": "IncidenceEventCoordinator",
"Number_Repetitions" : 3,
"Timesteps_Between_Repetitions" : 6
}
|
Trigger_Condition_List |
array of strings |
NA |
NA |
NA |
A list of events that will trigger an intervention. |
{
"Trigger_Condition_List": [
"NewClinicalCase",
"NewSevereCase"
]
}
|
{
"class": "IncidenceEventCoordinator",
"Number_Repetitions" : 3,
"Timesteps_Between_Repetitions" : 6
}
The MultiInterventionEventCoordinator coordinator class distributes multiple individual-level or node- level interventions at the same time to the same set of covered individuals or nodes. You cannot specify a mix of individual-level and node-level interventions within the same event coordinator. This enables correlated distributions, such as dosing of multiple drugs with different regimens and pharmacokinetic pharmacodynamic models. 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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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_Configs |
array of JSON objects |
NA |
NA |
NA |
The array of nested JSON parameters for the interventions to be distributed by this event coordinator. |
{
"Intervention_Configs": [{
"Cost_To_Consumer": 1,
"Dosing_Type": "FullTreatmentCourse",
"Drug_Type": "DHA",
"class": "AntimalarialDrug"
},
{
"Cost_To_Consumer": 1,
"Dosing_Type": "FullTreatmentCourse",
"Drug_Type": "Piperaquine",
"class": "AntimalarialDrug"
}
]
}
|
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. 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 |
NA |
NA |
0 |
When set to true (1), the intervention is only distributed to individuals that began the simulation in the node (i.e. those that claim the node as their residence). |
{
"Target_Residents_Only": 1
}
|
{
"Events": [{
"class": "CampaignEvent",
"Start_Day": 10,
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Event_Coordinator_Config": {
"class": "MultiInterventionEventCoordinator",
"Intervention_Configs": [{
"class": "PropertyValueChanger",
"Target_Property_Key": "Risk",
"Target_Property_Value": "HIGH",
"Daily_Probability": 1.0,
"Maximum_Duration": 0,
"Revert": 0
},
{
"class": "BroadcastEvent",
"Broadcast_Event": "PVC_Distributed"
}
]
}
}]
}
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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 NodeEventCoordinator coordinator class distributes node-level interventions. After distribution, the coordinator expires. 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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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 |
|---|---|---|---|---|---|---|
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"
}
}
|
{
"Events": [{
"class": "CampaignEvent",
"Event_Name": "Scale habitat",
"Start_Day": 1,
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Event_Coordinator_Config": {
"Intervention_Config": {
"class": "ScaleLarvalHabitat",
"Larval_Habitat_Multiplier": {
"TEMPORARY_RAINFALL": 0.05
}
},
"class": "NodeEventCoordinator"
}
}]
}
The SimpleInterventionDistributionEventCoordinator coordinator class distributes individual- level or node-level interventions and is a simplified version of the StandardInterventionDistributionEventCoordinator. There is reduced complexity, such as there is no targeting restrictions on demographics, properties, age, gender, or other target values. 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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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 |
float |
0 |
1 |
1 |
The fraction of individuals that will receive this intervention. |
{
"Coverage": 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"
}
}
|
{
"Events": [{
"class": "CampaignEvent",
"Event_Name": "Male circumcision for initial population",
"Start_Day": 1,
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Event_Coordinator_Config": {
"class": "SimpleInterventionDistributionEventCoordinator",
"Coverage": 0.5,
"Intervention_Config": {
"class": "MaleCircumcision",
"Circumcision_Reduced_Acquire": 0.6
}
}
}]
}
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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. 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 |
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_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 |
NA |
NA |
0 |
When set to true (1), the intervention is only distributed to individuals that began the simulation in the node (i.e. those that claim the node as their residence). |
{
"Target_Residents_Only": 1
}
|
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"
}
}
}]
}
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”.
The following node-level interventions are available for use with the malaria simulation type.
The following node-level interventions are commonly used for vector control in malaria simulations.
Intervention
Target life stage
Target biting preference
Target biting location
Effect
node
feeding cycle
animal
killing
feeding cycle
human
all
blocking
feeding cycle
all
all
killing
larva
all
all
killing, reduction
feeding cycle
human
outdoor
killing
feeding cycle
all
all
killing
larva
all
all
reduction
feeding cycle
human
outdoor
killing
feeding cycle
all
outdoor
blocking
adults
all
all
killing
The AnimalFeedKill intervention class imposes node-targeted mortality to a vector that feeds on animals.
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Cost_To_Consumer |
float |
0 |
999999 |
10 |
The unit cost per vector control (unamortized). |
{
"Cost_To_Consumer": 8
}
|
Disqualifying_Properties |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
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"
}
|
Killing_Config |
JSON object |
NA |
NA |
NA |
The configuration of killing efficacy of the targeted stage. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Killing_Config": {
"Box_Duration": 3650,
"Initial_Effect": 0.53429,
"class": "WaningEffectBox"
}
}
|
New_Property_Value |
string |
NA |
NA |
NA |
An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one. |
{
"New_Property_Value": "InterventionStatus:None"
}
|
{
"Events": [{
"class": "CampaignEvent",
"Start_Day": 120,
"Nodeset_Config": {
"class": "NodeSetAlld"
},
"Event_Coordinator_Config": {
"class": "NodeEventCoordinator",
"Intervention_Config": {
"class": "AnimalFeedKill",
"Cost_To_Consumer": 10.0,
"Killing_Config": {
"Box_Duration": 100,
"Decay_Time_Constant": 150,
"Initial_Effect": 0.2,
"class": "WaningEffectBoxExponential"
}
}
}
}],
"Use_Defaults": 1
}
The ArtificialDiet intervention class is used to include feeding stations for vectors within a node in a simulation. This is a node-targeted intervention and takes effect at the broad village level rather than at the individual level. For individual-level effect, use ArtificialDietHousingModification instead. An artificial diet diverts the vector from feeding on the human population, resulting in a two-fold benefit:
The uninfected mosquitoes avoid biting infected humans some of the time, therefore decreasing the amount of human-to-vector transmission.
Infectious vectors are diverted to feed on the artificial diet instead of the humans, therefore decreasing the amount of vector-to-human transmission.
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Artificial_Diet_Target |
enum |
NA |
NA |
AD_WithinVillage |
The targeted deployment of artificial diet. This parameter is required. Possible values are:
|
{
"Artificial_Diet_Target": "AD_WithinVillage"
}
|
Attraction_Config |
JSON object |
NA |
NA |
NA |
The fraction of vector feeds attracted to the artificial diet. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Attraction_Config:" {
"Initial_Effect": 0.5,
"Box_Duration": 100,
"Decay_Time_Constant": 150,
"class": "WaningEffectBoxExponential"
}
}
|
Cost_To_Consumer |
float |
0 |
999999 |
10 |
The unit cost per vector control (unamortized). |
{
"Cost_To_Consumer": 8
}
|
Disqualifying_Properties |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
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"
}
|
Primary_Decay_Time_Constant |
float |
0 |
1.00E+0 |
0 |
The primary decay time constant (in days) of the decay profile. |
{
"Primary_Decay_Time_Constant": 180
}
|
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
}
|
{
"Events": [{
"class": "CampaignEvent",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Start_Day": 120,
"Event_Coordinator_Config": {
"class": "StandardInterventionDistributionEventCoordinator",
"Intervention_Config": {
"class": "ArtificialDiet",
"Artificial_Diet_Target": "AD_WithinVillage",
"Attraction_Config": {
"Initial_Effect": 0.5,
"Box_Duration": 100,
"Decay_Time_Constant": 150,
"class": "WaningEffectBoxExponential"
},
"Cost_To_Consumer": 10.0
}
}
}],
"Use_Defaults": 1
}
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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 |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
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_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"
}
|
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 |
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 |
NA |
NA |
0 |
When set to true (1), the intervention is only distributed to individuals that began the simulation in the node (i.e. those that claim the node as their residence). |
{
"Target_Residents_Only": 1
}
|
{
"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"
}
}
}
}]
}
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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 |
|---|---|---|---|---|---|---|
Antigen |
integer |
0 |
10 |
0 |
The antigenic ID of the outbreak infection. |
{
"Intervention_Config": {
"Antigen": 0,
"Genome": 0,
"Outbreak_Source": "PrevalenceIncrease",
"class": "OutbreakIndividual"
}
}
|
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 |
-2.15E+0 |
2.15E+0 |
0 |
The genetic ID of the outbreak infection. |
{
"Intervention_Config": {
"Antigen": 0,
"Genome": 0,
"Outbreak_Source": "PrevalenceIncrease",
"class": "OutbreakIndividual",
"Incubation_Period_Override": 0
}
}
|
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 InputEIR intervention class enables the Entomological Inoculation Rate (EIR) to be configured for each month of the year in a particular node. The EIR is the number of mosquito bites per night multiplied by the proportion of those bites that are positive for sporozoites.
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Age_Dependence |
enum |
NA |
NA |
OFF |
Determines how InputEIR depends on the age of the target. Possible values are:
|
{
"Age_Dependence": "SURFACE_AREA_DEPENDENT"
}
|
Disqualifying_Properties |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
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"
}
|
Monthly_EIR |
array of floats |
0 |
1000 |
NA |
An array of 12 elements that contain an entomological inoculation rate (EIR) for each month. Each value should be between 0 and 1000. |
{
"Monthly_EIR": [0.39, 0.19, 0.77, 0, 0, 0, 6.4, 2.2, 4.7, 3.9, 0.87, 0.58]
}
|
New_Property_Value |
string |
NA |
NA |
NA |
An optional 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": "Constant EIR challenge",
"Events": [{
"class": "CampaignEvent",
"Event_Name": "Input EIR intervention",
"Start_Day": 0,
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Event_Coordinator_Config": {
"class": "StandardInterventionDistributionEventCoordinator",
"Number_Repetitions": 1,
"Intervention_Config": {
"class": "InputEIR",
"Age_Dependence": "SURFACE_AREA_DEPENDENT",
"Monthly_EIR": [0.39, 0.19, 0.77, 0, 0, 0, 6.4, 2.2, 4.7, 3.9, 0.87, 0.58]
}
}
}]
}
The InsectKillingFence intervention class is a vector control intervention uses a photonic fence, a type of insect-killing fence in which the system directs lethal doses of focused laser energy onto flying insects.
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Cost_To_Consumer |
float |
0 |
999999 |
10 |
The unit cost per vector control (unamortized). |
{
"Cost_To_Consumer": 8
}
|
Disqualifying_Properties |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
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"
}
|
Killing_Config |
JSON object |
NA |
NA |
NA |
The configuration for the effects of killing of the targeted stage. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Killing_Config": {
"Box_Duration": 3650,
"Initial_Effect": 0.5,
"class": "WaningEffectBox"
}
}
|
New_Property_Value |
string |
NA |
NA |
NA |
An optional 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+0 |
0 |
The primary decay time constant (in days) of the decay profile. |
{
"Primary_Decay_Time_Constant": 180
}
|
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
}
|
{
"Events": [{
"class": "CampaignEvent",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Start_Day": 140,
"Event_Coordinator_Config": {
"class": "StandardInterventionDistributionEventCoordinator",
"Intervention_Config": {
"class": "InsectKillingFence",
"Cost_To_Consumer": 3.75,
"Killing_Config": {
"Decay_Time_Constant": 2190,
"Initial_Effect": 0.28,
"class": "WaningEffectExponential"
}
}
}
}],
"Use_Defaults": 1
}
The Larvicides intervention class configures habitat reduction for mosquito breeding sites that are poisoned by vector-transported larvicides. This intervention accommodates vector-transported larvicides where resting surfaces may be treated not just with IRSHousingModification to kill mosquitoes, but also with particles that can be dispersed by mosquitoes to poison larval development sites. In particular, a relatively small fraction of resting events may result in a relatively large fraction of larval habitat.
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Blocking_Config |
JSON object |
NA |
NA |
NA |
The configuration of larval habitat reduction and waning for targeted stage. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Blocking_Config": {
"Box_Duration": 3650,
"Initial_Effect": 0.0,
"class": "WaningEffectBox"
}
}
|
Cost_To_Consumer |
float |
0 |
999999 |
10 |
The unit cost per vector control (unamortized). |
{
"Cost_To_Consumer": 8
}
|
Disqualifying_Properties |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Habitat_Target |
enum |
NA |
NA |
TEMPORARY_RAINFALL |
The larval habitat type targeted for larvicide. This is a required parameter. See Larval habitat parameters for more information. Possible values are:
|
{
"Habitat_Target": "WATER_VEGETATION"
}
|
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"
}
|
Killing_Config |
JSON object |
NA |
NA |
NA |
The configuration of larval killing efficacy and waning for targeted stage. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Killing_Config": {
"Box_Duration": 100,
"Decay_Time_Constant": 150,
"Initial_Effect": 0.2,
"class": "WaningEffectBoxExponential"
}
}
|
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+0 |
0 |
The primary decay time constant (in days) of the decay profile. |
{
"Primary_Decay_Time_Constant": 180
}
|
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
}
|
{
"Events": [{
"class": "CampaignEvent",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Start_Day": 140,
"Event_Coordinator_Config": {
"class": "StandardInterventionDistributionEventCoordinator",
"Intervention_Config": {
"Cost_To_Consumer": 3.75,
"Habitat_Target": "ALL_HABITATS",
"Killing_Config": {
"Box_Duration": 3650,
"Initial_Effect": 0.1,
"class": "WaningEffectBox"
},
"Blocking_Config": {
"Box_Duration": 0,
"Initial_Effect": 0.0,
"class": "WaningEffectBox"
},
"Reduction": 0,
"class": "Larvicides"
}
}
}],
"Use_Defaults": 1
}
The MalariaChallenge intervention class is similar to Outbreak. However, instead of distributing infections, it distributes malaria challenges by either tracking numbers of sporozoites or infectious mosquito bites.
Note
Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Challenge_Type |
enum |
NA |
NA |
NA |
The type of malaria challenge. Possible values are:
|
{
"Challenge_Type": "InfectiousBites"
}
|
Coverage |
float |
0 |
1 |
1 |
The fraction of individuals receiving an intervention. |
{
"Coverage": 1.0
}
|
Disqualifying_Properties |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Infectious_Bite_Count |
integer |
1 |
1000 |
1 |
The number of infectious bites. Use when Challenge_Type is set to InfectiousBites. |
{
"Infectious_Bite_Count": 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_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"
}
|
Sporozoite_Count |
integer |
0 |
1.00E+0 |
1.00E+00 |
The number of sporozoites. Use when Challenge_Type is set to Sporozoites. |
{
"Sporozoite_Count": 3
}
|
{
"Use_Defaults": 1,
"Events": [{
"class": "CampaignEvent",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Start_Day": 40,
"Event_Coordinator_Config": {
"class": "StandardInterventionDistributionEventCoordinator",
"Intervention_Config": {
"class": "MalariaChallenge",
"Challenge_Type": "InfectiousBites",
"Coverage": 1.0,
"Infectious_Bite_Count": 2,
"Sporozoite_Count": 3
}
}
}]
}
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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 |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Duration_At_Node_Distribution_Type |
enum |
NA |
NA |
NOT_INITIALIZED |
The shape of the distribution for the amount of time spent at the destination node for intervention-based migration. Possible values are:
|
{
"Duration_At_Node_Distribution_Type": "GAUSSIAN_DURATION"
}
|
Duration_At_Node_Exponential_Period |
float |
0 |
3.40E+3 |
6 |
The period (1/rate) to use for an exponential distribution for the amount of time spent at the destination node when Duration_At_Node_Distribution_Type is set to EXPONENTIAL_DURATION. |
{
"Duration_At_Node_Distribution_Type": "EXPONENTIAL_DURATION",
"Duration_At_Node_Exponential_Period": 4
}
|
Duration_At_Node_Fixed |
float |
0 |
3.40E+3 |
6 |
The value to use for a fixed distribution for the amount of time spent at the destination node when Duration_At_Node_Distribution_Type is set to FIXED_DURATION. |
{
"Duration_At_Node_Distribution_Type" : "FIXED_DURATION",
"Duration_At_Node_Fixed": 10
}
|
Duration_At_Node_Gausian_Mean |
float |
0 |
3.40E+3 |
6 |
The mean value to use for a Gaussian distribution for the amount of time spent at the destination node when Duration_At_Node_Distribution_Type is set to GAUSSIAN_DURATION. |
{
"Duration_At_Node_Distribution_Type": "GAUSSIAN_DURATION",
"Duration_At_Node_Gausian_Mean": 9.0,
"Duration_At_Node_Gausian_StdDev": 2.0
}
|
Duration_At_Node_Gausian_StdDev |
float |
0 |
3.40E+3 |
1 |
The standard deviation to use for a Gaussian distribution for the amount of time spent at the destination node when Duration_At_Node_Distribution_Type is set to GAUSSIAN_DURATION. |
{
"Duration_At_Node_Distribution_Type": "GAUSSIAN_DURATION",
"Duration_At_Node_Gausian_Mean": 9.0,
"Duration_At_Node_Gausian_StdDev": 2.0
}
|
Duration_At_Node_Poisson_Mean |
float |
0 |
3.40E+3 |
6 |
The mean to use for a Poisson distribution for the amount of time spent at the destination node when Duration_At_Node_Distribution_Type is set to POISSON_DURATION. |
{
"Duration_At_Node_Distribution_Type": "POISSON_DURATION",
"Duration_At_Node_Poisson_Mean": 4
}
|
Duration_At_Node_Uniform_Max |
float |
0 |
3.40E+3 |
0 |
The maximum value to use for a uniform distribution for the amount of time spent at the destination node when Duration_At_Node_Distribution_Type is set to UNIFORM_DURATION. |
{
"Duration_At_Node_Distribution_Type": "UNIFORM_DURATION",
"Duration_At_Node_Uniform_Max": 75,
"Duration_At_Node_Uniform_Min": 45
}
|
Duration_At_Node_Uniform_Min |
float |
0 |
3.40E+3 |
0 |
The minimum value to use for a uniform distribution for the amount of time spent at the destination node when Duration_At_Node_Distribution_Type is set to UNIFORM_DURATION. |
{
"Duration_At_Node_Distribution_Type": "UNIFORM_DURATION",
"Duration_At_Node_Uniform_Max": 75,
"Duration_At_Node_Uniform_Min": 45
}
|
Duration_Before_Leaving_Distribution_Type |
enum |
NA |
NA |
NOT_INITIALIZED |
The shape of the distribution of the number of days to wait before migrating to the destination node for intervention-based migration. Possible values are:
|
{
"Duration_Before_Leaving_Distribution_Type": "FIXED_DURATION"
}
|
Duration_Before_Leaving_Exponential_Period |
float |
0 |
3.40E+3 |
6 |
The period (1/rate) to use for an exponential distribution of the number of days to wait before migrating to the destination node when Duration_Before_Leaving_Distribution_Type is set to EXPONENTIAL_DURATION. |
{
"Duration_Before_Leaving_Distribution_Type": "EXPONENTIAL_DURATION",
"Duration_Before_Leaving_Exponential_Period": 4
}
|
Duration_Before_Leaving_Fixed |
float |
0 |
3.40E+3 |
6 |
The value to use for a fixed distribution of the number of days to wait before migrating to the destination node when Duration_Before_Leaving_Distribution_Type is set to FIXED_DURATION. |
{
"Duration_Before_Leaving_Distribution_Type": "FIXED_DURATION",
"Duration_Before_Leaving_Fixed": 4
}
|
Duration_Before_Leaving_Gausian_Mean |
float |
0 |
3.40E+3 |
6 |
The mean value to use for a Gaussian distribution of the number of days to wait before migrating to the destination node when Duration_Before_Leaving_Distribution_Type is set to GAUSSIAN_DURATION. |
{
"Duration_Before_Leaving_Distribution_Type": "GAUSSIAN_DURATION",
"Duration_Before_Leaving_Gausian_Mean": 9.0,
"Duration_Before_Leaving_Gausian_StdDev": 2.0
}
|
Duration_Before_Leaving_Gausian_StdDev |
float |
0 |
3.40E+3 |
1 |
The standard deviation to use for a Gaussian distribution of the number of days to wait before migrating to the destination node when Duration_Before_Leaving_Distribution_Type is set to GAUSSIAN_DURATION. |
{
"Duration_Before_Leaving_Distribution_Type": "GAUSSIAN_DURATION",
"Duration_Before_Leaving_Gausian_Mean": 9.0,
"Duration_Before_Leaving_Gausian_StdDev": 2.0
}
|
Duration_Before_Leaving_Poisson_Mean |
float |
0 |
3.40E+3 |
6 |
The mean to use for a Poisson distribution of the number of days to wait before migrating to the destination node when Duration_Before_Leaving_Distribution_Type is set to POISSON_DURATION. |
{
"Duration_Before_Leaving_Distribution_Type": "POISSON_DURATION",
"Duration_Before_Leaving_Poisson_Mean": 3.0,
}
|
Duration_Before_Leaving_Uniform_Max |
float |
0 |
3.40E+3 |
0 |
The maximum value to use for a uniform distribution of the number of days to wait before migrating to the destination node when Duration_Before_Leaving_Distribution_Type is set to UNIFORM_DURATION. |
{
"Duration_Before_Leaving_Distribution_Type": "UNIFORM_DURATION",
"Duration_Before_Leaving_Uniform_Max": 14,
"Duration_Before_Leaving_Uniform_Min": 0,
}
|
Duration_Before_Leaving_Uniform_Min |
float |
0 |
3.40E+3 |
0 |
The minimum value to use for a uniform distribution of the number of days to wait before migrating to the destination node when Duration_Before_Leaving_Distribution_Type is set to UNIFORM_DURATION. |
{
"Duration_Before_Leaving_Distribution_Type": "UNIFORM_DURATION",
"Duration_Before_Leaving_Uniform_Max": 14,
"Duration_Before_Leaving_Uniform_Min": 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_Name":"Diagnostic_Sample"
}
|
Is_Moving |
boolean |
NA |
NA |
0 |
Set to true (1) to indicate the individual is permanently moving to a new home node for intervention-based migration. |
{
"Is_Moving": 1
}
|
New_Property_Value |
string |
NA |
NA |
NA |
An optional 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": 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_Type": "FIXED_DURATION",
"Duration_Before_Leaving_Fixed": 0,
"Duration_At_Node_Distribution_Type": "FIXED_DURATION",
"Duration_At_Node_Fixed": 10,
"Is_Moving": 0
}
}
}
}]
}
The MosquitoRelease intervention class adds mosquito release vector control programs to the simulation. Mosquito release is a key vector control mechanism that allows the release of sterile males, genetically modified mosquitoes, or even Wolbachia-infected mosquitoes. See Vector control configuration parameters for more information.
Note
Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Cost_To_Consumer |
float |
0 |
999999 |
0 |
The cost of each mosquito release. |
{
"Cost_To_Consumer": 1.5
}
|
Disqualifying_Properties |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
HEG |
enum |
NA |
NA |
WILD |
HEG characteristics of released mosquitoes. Possible values are:
|
{
"Intervention_Config":{
"Mated_Genetics": {
"HEG": "NotMated",
"Pesticide_Resistance": "NotMated"
},
"Released_Gender": "VECTOR_FEMALE",
"Released_Genetics": {
"HEG": "WILD",
"Pesticide_Resistance": "WILD"
},
"Released_Number": 10000,
"Released_Species": "gambiae",
"Released_Sterility": "VECTOR_FERTILE",
"Released_Wolbachia": "VECTOR_WOLBACHIA_A",
"class": "MosquitoRelease"
}
}
|
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"
}
|
Mated_Genetics |
JSON object |
NA |
NA |
NA |
The genetic properties of the mate if released mosquitoes have mated, e.g. HEG and pesticide resistance. The characteristics are set under ResistanceHegGenetics. Used when Released_Gender is set to VECTOR_FEMALE. |
{
"Mated_Genetics": {
"HEG": "NotMated",
"Pesticide_Resistance": "NotMated"
}
}
|
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"
}
|
Pesticide_Resistance |
enum |
NA |
NA |
WILD |
The pesticide resistance characteristics of released mosquitoes. Possible values are:
|
{
"Released_Genetics": {
"HEG": "WILD",
"Pesticide_Resistance": "WILD"
}
}
|
Released_Gender |
enum |
NA |
NA |
NA |
The gender of released mosquitoes. Possible values are:
|
{
"Released_Gender": "VECTOR_FEMALE"
}
|
Released_Genetics |
JSON object |
NA |
NA |
NA |
The genetic properties of the released mosquito, e.g. HEG and pesticide resistance. The characteristics are set under the ResistanceHegGenetics parameter. |
{
"Released_Genetics": {
"HEG": "WILD",
"Pesticide_Resistance": "WILD"
}
}
|
Released_Number |
integer |
1 |
1.00E+0 |
10000 |
The number of mosquitoes released in the intervention. |
{
"Released_Number": 10000
}
|
Released_Species |
string |
NA |
NA |
NA |
The name of the released mosquito species, such as “arabiensis”. The simulation configuration parameter, Vector_Species_Params, needs to contain that specific mosquito species as does the Vector_Species_Names array. |
{
"Released_Species": "gambiae"
}
|
Released_Sterility |
enum |
NA |
NA |
VECTOR_FERTILE |
The sterility of released mosquitoes. Possible values are:
|
{
"Released_Sterility": "VECTOR_FERTILE"
}
|
Released_Wolbachia |
enum |
NA |
NA |
WOLBACHIA_FREE |
The Wolbachia type of released mosquitoes. Possible values are:
|
{
"Released_Wolbachia": "VECTOR_WOLBACHIA_A"
}
|
{
"Events": [{
"class": "CampaignEvent",
"Event_Name": "MosquitoRelease",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Start_Day": 210,
"Event_Coordinator_Config": {
"class": "StandardInterventionDistributionEventCoordinator",
"Number_Repetitions": 5,
"Timesteps_Between_Repetitions": 30,
"Intervention_Config": {
"class": "MosquitoRelease",
"Cost_To_Consumer": 200,
"Mated_Genetics": {
"HEG": "NotMated",
"Pesticide_Resistance": "NotMated"
},
"Released_Gender": "VECTOR_MALE",
"Released_Genetics": {
"HEG": "FULL",
"Pesticide_Resistance": "WILD"
},
"Released_Number": 10000,
"Released_Species": "arabiensis",
"Released_Sterility": "VECTOR_FERTILE"
}
}
}],
"Use_Defaults": 1
}
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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 |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
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"
}
|
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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 |
NoTrigger |
The event to broadcast if an intervention cannot be distributed due to the Blackout_Period. See Event list for possible values. |
{
"Blackout_Event_Trigger" : "Blackout"
}
|
Blackout_On_First_Occurrence |
boolean |
NA |
NA |
0 |
If set to true (1), individuals will enter the blackout period after the first occurrence of an event in the Trigger_Condition_List. |
{
"Blackout_On_First_Occurrence": 0
}
|
Blackout_Period |
float |
0 |
3.40E+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 |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Distribute_On_Return_Home |
boolean |
NA |
NA |
0 |
When set to true (1), individuals will receive an intervention upon returning home if that intervention was originally distributed while the individual was away. |
{
"Distribute_On_Return_Home" : 1
}
|
Duration |
float |
-1 |
3.40E+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_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"
}
|
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 |
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 |
NA |
NA |
0 |
When set to true (1), the intervention is only distributed to individuals that began the simulation in the node (i.e. those that claim the node as their residence). |
{
"Target_Residents_Only": 1
}
|
Trigger_Condition_List |
array of strings |
NA |
NA |
NA |
An array of events that will trigger an intervention. The events contained in the list can be built-in events (see Event list for possible events) or the custom events defined in 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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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 |
NoTrigger |
The event to broadcast if an intervention cannot be distributed due to the Blackout_Period. See Event list for possible values. |
{
"Blackout_Event_Trigger" : "Blackout"
}
|
Blackout_On_First_Occurrence |
boolean |
NA |
NA |
0 |
If set to true (1), individuals will enter the blackout period after the first occurrence of an event in the Trigger_Condition_List. |
{
"Blackout_On_First_Occurrence": 0
}
|
Blackout_Period |
float |
0 |
3.40E+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 |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Distribute_On_Return_Home |
boolean |
NA |
NA |
0 |
When set to true (1), individuals will receive an intervention upon returning home if that intervention was originally distributed while the individual was away. |
{
"Distribute_On_Return_Home" : 1
}
|
Duration |
float |
-1 |
3.40E+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_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"
}
|
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 |
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 |
NA |
NA |
0 |
When set to true (1), the intervention is only distributed to individuals that began the simulation in the node (i.e. those that claim the node as their residence). |
{
"Target_Residents_Only": 1
}
|
Trigger_Condition_List |
array of strings |
NA |
NA |
NA |
An array of events that will trigger an intervention. The events contained in the list can be built-in events (see Event list for possible events) or the custom events defined in 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_Distribution": "FIXED_DURATION",
"Delay_Period": 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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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 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 |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
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+3 |
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_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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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 |
|---|---|---|---|---|---|---|
Antigen |
integer |
0 |
10 |
0 |
The antigenic ID of the outbreak infection. |
{
"Intervention_Config": {
"Antigen": 0,
"Genome": 0,
"Outbreak_Source": "PrevalenceIncrease",
"class": "OutbreakIndividual"
}
}
|
Genome |
integer |
-2.15E+0 |
2.15E+0 |
0 |
The genetic ID of the outbreak infection. |
{
"Intervention_Config": {
"Antigen": 0,
"Genome": 0,
"Outbreak_Source": "PrevalenceIncrease",
"class": "OutbreakIndividual",
"Incubation_Period_Override": 0
}
}
|
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,
"Events": [{
"class": "CampaignEvent",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Start_Day": 40,
"Event_Coordinator_Config": {
"class": "StandardInterventionDistributionEventCoordinator",
"Intervention_Config": {
"class": "Outbreak",
"Antigen": 0,
"Genome": 0,
"Number_Cases_Per_Node": 10
}
}
}]
}
The OutdoorRestKill intervention class imposes node-targeted mortality to a vector that is at rest in an outdoor environment after an outdoor feed on a human.
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Cost_To_Consumer |
float |
0 |
999999 |
10 |
The unit cost per vector control (unamortized). |
{
"Cost_To_Consumer": 8
}
|
Disqualifying_Properties |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
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"
}
|
Killing_Config |
JSON object |
NA |
NA |
NA |
The configuration for the effects of killing of the targeted stage. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Killing_Config": {
"Box_Duration": 3650,
"Initial_Effect": 0.5,
"class": "WaningEffectBox"
}
}
|
New_Property_Value |
string |
NA |
NA |
NA |
An optional 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+0 |
0 |
The primary decay time constant (in days) of the decay profile. |
{
"Primary_Decay_Time_Constant": 180
}
|
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
}
|
{
"Events": [{
"class": "CampaignEvent",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Start_Day": 120,
"Event_Coordinator_Config": {
"class": "StandardInterventionDistributionEventCoordinator",
"Intervention_Config": {
"class": "OutdoorRestKill",
"Cost_To_Consumer": 1.0,
"Killing_Config": {
"Box_Duration": 100,
"Decay_Time_Constant": 150,
"Initial_Effect": 0.75,
"class": "WaningEffectBoxExponential"
}
}
}
}],
"Use_Defaults": 1
}
The OvipositionTrap intervention class utilizes an oviposition trap to collect host-seeking mosquitoes, and is based upon imposing a mortality to egg hatching from oviposition. This is a node-targeted intervention and affects all mosquitoes living and feeding at a given node. This trap requires the use of individual mosquitoes in the simulation configuration file (Vector_Sampling_Type must be set to TRACK_ALL_VECTORS or SAMPLE_IND_VECTORS), rather than the cohort model. See Sampling configuration parameters for more information.
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Cost_To_Consumer |
float |
0 |
999999 |
10 |
The unit cost per vector control (unamortized). |
{
"Cost_To_Consumer": 8
}
|
Disqualifying_Properties |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Habitat_Target |
enum |
NA |
NA |
TEMPORARY_RAINFALL |
The oviposition habitat type targeted by oviposition traps. This is a required parameter. See Larval habitat parameters for more information. Possible values are:
|
{
"Habitat_Target": "WATER_VEGETATION"
}
|
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"
}
|
Killing_Config |
JSON object |
NA |
NA |
NA |
The configuration of the killing effects for the fraction of oviposition cycles that end in the female mosquito’s death. If there is skip oviposition, this does not configure the mortality per skip, but instead configures the effective net mortality per gonotrophic cycle over all skips. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Killing_Config": {
"Box_Duration": 3650,
"Initial_Effect": 0.53429,
"class": "WaningEffectBox"
}
}
|
New_Property_Value |
string |
NA |
NA |
NA |
An optional 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+0 |
0 |
The primary decay time constant (in days) of the decay profile. |
{
"Primary_Decay_Time_Constant": 180
}
|
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
}
|
{
"Events": [{
"class": "CampaignEvent",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Start_Day": 140,
"Event_Coordinator_Config": {
"Target_Demographic": "Everyone",
"class": "StandardInterventionDistributionEventCoordinator",
"Intervention_Config": {
"class": "OvipositionTrap",
"Cost_To_Consumer": 3.75,
"Habitat_Target": "WATER_VEGETATION",
"Killing_Config": {
"class": "WaningEffectExponential",
"Decay_Time_Constant": 2190,
"Initial_Effect": 0.95
},
"Reduction": 0
}
}
}],
"Use_Defaults": 1
}
The ScaleLarvalHabitat intervention class enables species-specific habitat modification within shared habitat types. This intervention has a similar function to the demographic parameter ScaleLarvalMultiplier, but enables habitat availability to be modified at any time or at any location during the simulation, as specified in the campaign event.
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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 |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
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"
}
|
Larval_Habitat_Multiplier |
JSON object |
NA |
NA |
NA |
The value by which to scale the larval habitat availability (specified in Larval_Habitat_Types in the configuration file) per intervention, across all habitat types, for specific habitat types, or for specific mosquito species within each habitat type. See Larval habitat parameters for more information. |
{
"Larval_Habitat_Multiplier": {
"TEMPORARY_RAINFALL": 0.05
}
}
|
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,
"Events": [{
"class": "CampaignEvent",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Start_Day": 730,
"Event_Coordinator_Config": {
"class": "NodeEventCoordinator",
"Intervention_Config": {
"class": "ScaleLarvalHabitat",
"Larval_Habitat_Multiplier": {
"TEMPORARY_RAINFALL": 0.05
}
}
}
}]
}
The SpaceSpraying intervention class implements node-level vector control by spraying pesticides outdoors. This intervention targets specific habitat types, and imposes a repellent effect or mortality rate on all targeted mosquitoes.
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Cost_To_Consumer |
float |
0 |
999999 |
10 |
The unit cost per vector control (unamortized). |
{
"Cost_To_Consumer": 8
}
|
Disqualifying_Properties |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Habitat_Target |
enum |
NA |
NA |
TEMPORARY_RAINFALL |
The larval habitat type targeted for habitat reduction. This is a required parameter. See Larval habitat parameters for more information. Possible values are:
|
{
"Habitat_Target": "WATER_VEGETATION"
}
|
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"
}
|
Killing_Config |
JSON object |
NA |
NA |
NA |
The configuration of killing efficacy and waning for space spaying. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Killing_Config": {
"Box_Duration": 3650,
"Initial_Effect": 0.5,
"class": "WaningEffectBox"
}
}
|
New_Property_Value |
string |
NA |
NA |
NA |
An optional 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+0 |
0 |
The primary decay time constant (in days) of the decay profile. |
{
"Primary_Decay_Time_Constant": 180
}
|
Reduction_Config |
JSON object |
NA |
NA |
NA |
The configuration of larval habitat reduction and waning for space spraying. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Reduction_Config": {
"Box_Duration": 100,
"Decay_Time_Constant": 150,
"Initial_Effect": 0.1,
"class": "WaningEffectBoxExponential"
}
}
|
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
}
|
Spray_Kill_Target |
enum |
NA |
NA |
SpaceSpray_FemalesOnly |
The gender kill-target of vector control interventions. Possible values are:
|
{
"Spray_Kill_Target": "SpaceSpray_FemalesAndMales"
}
|
{
"Use_Defaults": 1,
"Campaign_Name": "SpaceSpraying",
"Events": [{
"class": "CampaignEvent",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Start_Day": 4,
"Event_Coordinator_Config": {
"class": "NodeEventCoordinator",
"Intervention_Config": {
"class": "SpaceSpraying",
"Cost_To_Consumer": 0,
"Killing_Config": {
"class": "WaningEffectBoxExponential",
"Box_Duration": 100,
"Decay_Time_Constant": 150,
"Initial_Effect": 1
},
"Reduction_Config": {
"class": "WaningEffectBoxExponential",
"Box_Duration": 100,
"Decay_Time_Constant": 150,
"Initial_Effect": 0
},
"Durability_Time_Profile": "BOXDECAYDURABILITY",
"Habitat_Target": "ALL_HABITATS",
"Spray_Kill_Target": "SpaceSpray_FemalesAndMales"
}
}
}]
}
The SpatialRepellent intervention class implements node-level spatial repellents exclusively against outdoor-biting mosquitoes.
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Cost_To_Consumer |
float |
0 |
999999 |
10 |
The unit cost per vector control (unamortized). |
{
"Cost_To_Consumer": 8
}
|
Disqualifying_Properties |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
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"
}
|
Primary_Decay_Time_Constant |
float |
0 |
1.00E+0 |
0 |
The primary decay time constant (in days) of the decay profile. |
{
"Primary_Decay_Time_Constant": 180
}
|
Repellency_Config |
JSON object |
NA |
NA |
NA |
The configuration of efficacy and waning for spatial repellent. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Repellency_Config": {
"Box_Duration": 100,
"Decay_Time_Constant": 150,
"Initial_Effect": 0.4,
"class": "WaningEffectBoxExponential"
}
}
|
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
}
|
{
"Events": [{
"class": "CampaignEvent",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Start_Day": 120,
"Event_Coordinator_Config": {
"class": "StandardInterventionDistributionEventCoordinator",
"Intervention_Config": {
"class": "SpatialRepellent",
"Repellency_Config": {
"Box_Duration": 100,
"Decay_Time_Constant": 150,
"Initial_Effect": 0.4,
"class": "WaningEffectBoxExponential"
},
"Cost_To_Consumer": 1.0
}
}
}],
"Use_Defaults": 1
}
The SugarTrap intervention class implements a vector sugar-baited trap to collect host-seeking mosquitoes. This intervention affects all mosquitoes living and feeding at a given node, and requires the use of individual mosquitoes in the simulation configuration file (Vector_Sampling_Type must be set to TRACK_ALL_VECTORS or SAMPLE_IND-VECTORS), rather than using the cohort model. See Sampling configuration parameters for more information.
The impact of sugar-baited traps will depend on the sugar-feeding behavior specified in the configuration file, whether there is no sugar feeding, sugar feeding occurs once at emergence, sugar feeding occurs once per blood meal, or sugar feeding occurs every day. See Vector control configuration parameters for more information.
Note
Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Cost_To_Consumer |
float |
0 |
999999 |
10 |
The unit cost per vector control (unamortized). |
{
"Cost_To_Consumer": 9.0
}
|
Disqualifying_Properties |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
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"
}
|
Killing_Config |
JSON object |
NA |
NA |
NA |
The configuration for the effects of killing of the targeted stage. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Killing_Config": {
"Box_Duration": 3650,
"Initial_Effect": 0.5,
"class": "WaningEffectBox"
}
}
|
New_Property_Value |
string |
NA |
NA |
NA |
An optional 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+0 |
0 |
The primary decay time constant (in days) of the decay profile. |
{
"Primary_Decay_Time_Constant": 180
}
|
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
}
|
{
"Events": [{
"class": "CampaignEvent",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Start_Day": 1460,
"Event_Coordinator_Config": {
"class": "StandardInterventionDistributionEventCoordinator",
"Target_Demographic": "Everyone",
"Intervention_Config": {
"class": "SugarTrap",
"Cost_To_Consumer": 3.75,
"Killing_Config": {
"class": "WaningEffectExponential",
"Decay_Time_Constant": 2190,
"Initial_Effect": 0.1
}
}
}
}],
"Use_Defaults": 1
}
Individual-level interventions¶
Individual-level interventions determine what will be distributed to individuals to reduce the spread of a disease. For example, distributing vaccines or drugs are individual-level interventions. Sometimes this can be an intermediate intervention that schedules another intervention.
The following individual-level interventions are available for use with the malaria simulation type.
The following individual-level interventions are commonly used for vector control in malaria simulations.
Intervention
Target life stage
Target biting preference
Target biting location
Effect
feeding cycle
human
killing
killing
feeding cycle
human
indoor
killing, blocking
feeding cycle
human
all
killing
feeding cycle
human
indoor
killing, blocking
feeding cycle
human
indoor
killing, blocking
feeding cycle
human
all
blocking
feeding cycle
human
indoor
killing, blocking
The AdherentDrug class extends AntiMalarialDrug class and allows for incorporating different patterns of adherence to taking the drug.
Note
Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Adherence_Config |
array of JSON objects |
NA |
NA |
NA |
A list of nested JSON objects for the interventions to be distributed by this event coordinator. |
{
"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 |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
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 |
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"
}
|
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 |
NoTrigger |
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"
}
|
{
"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"
}
The AntimalarialDrug intervention is used to apply drug-based control efforts to malaria simulations. When configuring this intervention, note that the Malaria_Drug_Params need to be configured, as they govern how particular anti-malarial drugs will behave.
Note
Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Cost_To_Consumer |
float |
0 |
999999 |
10 |
The unit cost per drug (unamortized). |
{
"Intervention_Config": {
"Cost_To_Consumer": 3.75,
"Dosing_Type": "FullTreatmentWhenSymptom",
"Drug_Type": "Chloroquine",
"class": "AntimalarialDrug"
}
}
|
Disqualifying_Properties |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
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"
}
}
|
Drug_Type |
enum |
NA |
NA |
UNINITIALIZED STRING |
The type of drug to distribute in a drugs intervention. Possible values for Malaria are:
The above are the actual values you use in a configuration file. For a list showing the full name for each drug, please see Malaria Drug Efficacy. |
{
"Intervention_Config": {
"Cost_To_Consumer": 3.75,
"Dosing_Type": "FullTreatmentWhenSymptom",
"Drug_Type": "Chloroquine",
"class": "AntimalarialDrug"
}
}
|
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"
}
|
{
"Use_Defaults": 1,
"Events": [{
"class": "CampaignEvent",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Start_Day": 270,
"Event_Coordinator_Config": {
"class": "StandardInterventionDistributionEventCoordinator",
"Target_Demographic": "Everyone",
"Demographic_Coverage": 0.8,
"Intervention_Config": {
"class": "AntimalarialDrug",
"Cost_To_Consumer": 3.75,
"Dosing_Type": "FullTreatmentWhenSymptom",
"Drug_Type": "Chloroquine"
}
}
},
{
"class": "CampaignEvent",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Start_Day": 300,
"Event_Coordinator_Config": {
"class": "StandardInterventionDistributionEventCoordinator",
"Target_Demographic": "Everyone",
"Demographic_Coverage": 0.8,
"Intervention_Config": {
"class": "AntimalarialDrug",
"Cost_To_Consumer": 3.75,
"Dosing_Type": "FullTreatmentParasiteDetect",
"Drug_Type": "Chloroquine"
}
}
}
]
}
The ArtificialDietHousingModification intervention class is used to include feeding stations for vectors at an individual level in a simulation. For a node-level effect that takes place at the village level, use ArtificialDiet instead. An artificial diet diverts the vector from feeding on the human population, resulting in a two-fold benefit:
The uninfected mosquitoes avoid biting infected humans some of the time, therefore decreasing the amount of human-to-vector transmission.
Infectious vectors are diverted to feed on the artificial diet instead of the humans, therefore decreasing the amount of vector-to-human transmission.
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Blocking_Config |
JSON object |
NA |
NA |
NA |
The configuration of pre-feed mosquito repellency and waning for housing modification. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Blocking_Config": {
"Box_Duration": 3650,
"Initial_Effect": 0.0,
"class": "WaningEffectBox"
}
}
|
Cost_To_Consumer |
float |
0 |
999999 |
8 |
The unit cost per housing modification (unamortized). |
{
"Cost_To_Consumer": 10.0
}
|
Disqualifying_Properties |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
Intervention_Name |
string |
NA |
NA |
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"
}
|
Killing_Config |
JSON object |
NA |
NA |
NA |
The configuration of killing efficacy and waning for housing modification. Killing is conditional on NOT blocking the mosquito before feeding. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Killing_Config": {
"Box_Duration": 3650,
"Initial_Effect": 0.53429,
"class": "WaningEffectBox"
}
}
|
New_Property_Value |
string |
NA |
NA |
NA |
An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one. |
{
"New_Property_Value": "InterventionStatus:None"
}
|
{
"Events": [
{
"class": "CampaignEvent",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Start_Day": 120,
"Event_Coordinator_Config": {
"class": "StandardInterventionDistributionEventCoordinator",
"Target_Demographic": "Everyone",
"Demographic_Coverage": 0.5,
"Intervention_Config": {
"class": "ArtificialDietHousingModification",
"Blocking_Config": {
"Box_Duration": 100,
"Decay_Time_Constant": 150,
"Initial_Effect": 0.5,
"class": "WaningEffectBoxExponential"
},
"Cost_To_Consumer": 10.0,
"Killing_Config": {
"Box_Duration": 100,
"Decay_Time_Constant": 150,
"Initial_Effect": 0.5,
"class": "WaningEffectBoxExponential"
}
}
}
}
],
"Use_Defaults": 1
}
The BitingRisk class is used with individual-level interventions and allows for adjusting the relative risk of being bitten by a vector.
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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 |
|---|---|---|---|---|---|---|
Constant |
float |
0 |
3.40E+03 |
6 |
When Risk_Distribution_Type is set to FIXED_DURATION, this will specify the time delay (in number of days). |
{
"Intervention_Config": {
"class": "BitingRisk",
"Risk_Distribution_Type" : "FIXED_DURATION",
"Constant" : 0.1
}
}
|
Disqualifying_Properties |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
Exponential_Mean |
float |
0 |
3.40E+03 |
6 |
When Risk_Distribution_Type is set to EXPONENTIAL_DURATION, the Exponential_Mean parameter represents the exponential rate that describes the distribution of the time delay (in units of 1/days). |
{
"Risk_Distribution_Type": "EXPONENTIAL_DURATION",
"Exponential_Mean": 10
}
|
Gaussian_Mean |
float |
0 |
3.40E+03 |
6 |
When Risk_Distribution_Type is set to GAUSSIAN_DURATION, the Gaussian_Mean parameter defines the mean deviation of the Gaussian distribution for the active period. Negative values are truncated at zero. |
{
"Risk_Distribution_Type": "GAUSSIAN_DURATION",
"Gaussian_Mean": 25,
"Gaussian_Std_Dev": 5
}
|
Gaussian_Std_Dev |
float |
0 |
3.40E+03 |
1 |
When Risk_Distribution_Type is set to GAUSSIAN_DURATION, the Gaussian_Std_Dev parameter defines the standard deviation of the Gaussian distribution for the active period. Negative values are truncated at zero. |
{
"Risk_Distribution_Type": "GAUSSIAN_DURATION",
"Gaussian_Mean": 25,
"Gaussian_Std_Dev": 5
}
|
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"
}
|
Risk_Distribution_Type |
enum |
NA |
NA |
NOT_INITIALIZED |
The distribution of the time delay from when the biting risk intervention is distributed to when the individual actually receives the intervention. Possible values are:
|
{
"Risk_Distribution_Type": "GAUSSIAN_DURATION",
"Gaussian_Mean": 25,
"Gaussian_Std_Dev": 5
}
|
Uniform_Max |
float |
0 |
3.40E+03 |
1 |
The maximum time delay (in number of days) when Risk_Distribution_Type is set to UNIFORM_DURATION. |
{
"BR_Risk_Distribution_Type": "UNIFORM_DURATION",
"BR_Uniform_Min": 1,
"BR_Uniform_Max": 30
}
|
Uniform_Min |
float |
0 |
3.40E+03 |
0 |
The minimum time delay (in number of days) when Risk_Distribution_Type is set to UNIFORM_DURATION. |
{
"BR_Risk_Distribution_Type": "UNIFORM_DURATION",
"BR_Uniform_Min": 1,
"BR_Uniform_Max": 30
}
|
{
"Intervention_Config": {
"class": "BitingRisk",
"Risk_Distribution_Type" : "FIXED_DURATION",
"Constant" : 0.1
}
}
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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 |
NoTrigger |
The name of the event that will trigger the intervention. See Event list for possible values. |
{
"Broadcast_Event": "HIVNeedsHIVTest"
}
|
Disqualifying_Properties |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
Intervention_Name |
string |
NA |
NA |
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"
}
|
{
"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_Distribution": "FIXED_DURATION",
"Delay_Period": 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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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 |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
Event_Trigger |
enum |
NA |
NA |
NoTrigger |
The name of the event to broadcast to selected nodes. See Event list for possible values. |
{
"Event_Trigger": "VaccinateNeighbors"
}
|
Include_My_Node |
boolean |
NA |
NA |
0 |
Set to true (1) to broadcast the event to the current node. |
{
"Actual_IndividualIntervention_Config": {
"class": "BroadcastEventToOtherNodes",
"Event_Trigger": "VaccinateNeighbors",
"Include_My_Node" : 1,
"Node_Selection_Type" : "DISTANCE_AND_MIGRATION",
"Max_Distance_To_Other_Nodes_Km" : 1
}
}
|
Intervention_Name |
string |
NA |
NA |
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"
}
|
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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 |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Distributed_Event_Trigger |
enum |
NA |
NA |
NoTrigger |
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 |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
Duration_To_Wait_Before_Revaccination |
float |
0 |
3.40E+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 |
NA |
NA |
1 |
The overall vaccine efficacy when individuals receive more than one vaccine. When set to true (1), the vaccine efficacies are multiplied together; when set to false (0), the efficacies are additive. |
{
"Intervention_Config": {
"class": "SimpleVaccine",
"Cost_To_Consumer": 10,
"Vaccine_Type": "AcquisitionBlocking",
"Vaccine_Take": 1,
"Efficacy_Is_Multiplicative": 0,
"Waning_Config": {
"class": "WaningEffectConstant",
"Initial_Effect": 0.3
}
}
}
|
Expired_Event_Trigger |
enum |
NA |
NA |
NoTrigger |
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_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"
}
|
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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": [{
"class": "HIVSimpleDiagnostic",
"New_Property_Value": "InterventionStatus:None",
"Base_Specificity": 1,
"Base_Sensitivity": 1,
"Event_Or_Config": "Event",
"Positive_Diagnosis_Event": "NoTrigger",
"Negative_Diagnosis_Event": "HIVNeedsMaleCircumcision"
}]
}
|
Coverage |
float |
0 |
1 |
1 |
The proportion of individuals who receive the DelayedIntervention that actually receive the configured interventions. |
{
"Coverage": 1.0
}
|
Delay_Distribution |
enum |
NA |
NA |
NOT_INITIALIZED |
The distribution of the time delay from when the intervention is distributed to when the individual actually receives the intervention. Possible values are:
|
{
"Delay_Distribution": "UNIFORM_DURATION"
}
|
Delay_Period |
float |
0 |
3.40E+3 |
6 |
When Delay_Distribution is set to FIXED_DURATION, this will specify the time delay (in number of days). When Delay_Distribution is set to EXPONENTIAL_DURATION, this represents the exponential rate that describes the distribution of the time delay (in units of 1/days). |
{
"Delay_Distribution": "FIXED_DURATION",
"Delay_Period": 8
}
|
Delay_Period_Max |
float |
0 |
3.40E+3 |
6 |
The maximum time delay (in number of days) when Delay_Distribution is set to UNIFORM_DURATION. |
{
"Delay_Distribution": "UNIFORM_DURATION",
"Delay_Period_Min": 1,
"Delay_Period_Max": 30
}
|
Delay_Period_Mean |
float |
0 |
3.40E+3 |
6 |
The mean time delay (in number of days), when Delay_Distribution is set to GAUSSIAN_DURATION. |
{
"Delay_Distribution": "GAUSSIAN_DURATION",
"Delay_Period_Mean": 25,
"Delay_Period_Std_Dev": 5
}
|
Delay_Period_Min |
float |
0 |
3.40E+3 |
6 |
The minimum time delay (in number of days) when Delay_Distribution is set to UNIFORM_DURATION. |
{
"Delay_Distribution": "UNIFORM_DURATION",
"Delay_Period_Min": 1,
"Delay_Period_Max": 30
}
|
Delay_Period_Scale |
float |
0 |
3.40E+3 |
16 |
The scale parameter (lambda > 0) of the distribution (in days) when Delay_Distribution is set to WEIBULL_DURATION. |
{
"Delay_Distribution": "WEIBULL_DURATION",
"Delay_Period_Scale": 10,
"Delay_Period_Shape": 15
}
|
Delay_Period_Shape |
float |
0 |
20 |
The shape parameter (kappa > 0) of the distribution when Delay_Distribution is set to WEIBULL_DURATION. |
{
"Delay_Distribution": "WEIBULL_DURATION",
"Delay_Period_Scale": 10,
"Delay_Period_Shape": 15
}
|
|
Delay_Period_Std_Dev |
float |
0 |
3.40E+3 |
6 |
The standard deviation of the Gaussian distribution (in number of days) when Delay_Distribution is set to GAUSSIAN_DURATION. |
{
"Delay_Distribution": "GAUSSIAN_DURATION",
"Delay_Period_Mean": 25,
"Delay_Period_Std_Dev": 5
}
|
Disqualifying_Properties |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
Intervention_Name |
string |
NA |
NA |
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"
}
|
{
"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_Distribution": "FIXED_DURATION",
"Delay_Period": 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_Distribution": "UNIFORM_DURATION",
"Delay_Period_Min": 15,
"Delay_Period_Max": 30,
"Actual_IndividualIntervention_Configs": [{
"Outbreak_Source": "PrevalenceIncrease",
"class": "OutbreakIndividual"
}]
}
}
}
],
"Use_Defaults": 1
}
The HumanHostSeekingTrap intervention class applies a trap that attracts and kills host-seeking mosquitoes in the simulation. Human-host-seeking traps are individually-distributed interventions that have attraction and killing rates that decay in an analogous fashion to the blocking and killing rates of bednets.
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Attract_Config |
JSON object |
NA |
NA |
NA |
The configuration of attraction efficacy and waning for human host-seeking trap. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Attract_Config": {
"Box_Duration": 3650,
"Initial_Effect": 0.6,
"class": "WaningEffectBox"
}
}
|
Cost_To_Consumer |
float |
0 |
999999 |
3.75 |
The unit cost per trap (unamortized) |
{
"Cost_To_Consumer" : 4.5
}
|
Disqualifying_Properties |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
Intervention_Name |
string |
NA |
NA |
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"
}
|
Killing_Config |
JSON object |
NA |
NA |
NA |
The configuration of killing efficacy and waning for human host-seeking trap. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Killing_Config": {
"Box_Duration": 3650,
"Initial_Effect": 0.9,
"class": "WaningEffectBox"
}
}
|
New_Property_Value |
string |
NA |
NA |
NA |
An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one. |
{
"New_Property_Value": "InterventionStatus:None"
}
|
Primary_Decay_Time_Constant |
float |
0 |
1.00E+0 |
0 |
The primary decay time constant (in days) of the decay profile. |
{
"Primary_Decay_Time_Constant": 180
}
|
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
}
|
{
"Events": [{
"class": "CampaignEvent",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Start_Day": 140,
"Event_Coordinator_Config": {
"class": "StandardInterventionDistributionEventCoordinator",
"Target_Demographic": "Everyone",
"Demographic_Coverage": 0.7,
"Intervention_Config": {
"class": "HumanHostSeekingTrap",
"Cost_To_Consumer": 3.75,
"Attract_Config": {
"Box_Duration": 3650,
"Initial_Effect": 0.6,
"class": "WaningEffectBox"
},
"Killing_Config": {
"Box_Duration": 3650,
"Initial_Effect": 0.9,
"class": "WaningEffectBox"
}
}
}
}],
"Use_Defaults": 1
}
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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 InsectKillingFenceHousingModification intervention class is similar to InsectKillingFence, but is individual-based instead of node-based. Vector control surrounds a designated house with a fence that kills mosquitoes that fly across the path of the fence.
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Blocking_Config |
JSON object |
NA |
NA |
NA |
The configuration of pre-feed mosquito repellency and waning for housing modification. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Blocking_Config": {
"Box_Duration": 3650,
"Initial_Effect": 0.0,
"class": "WaningEffectBox"
}
}
|
Cost_To_Consumer |
float |
0 |
999999 |
8 |
The unit cost per housing modification (unamortized). |
{
"Cost_To_Consumer": 10.0
}
|
Disqualifying_Properties |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
Intervention_Name |
string |
NA |
NA |
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"
}
|
Killing_Config |
JSON object |
NA |
NA |
NA |
The configuration of killing efficacy and waning for housing modification. Killing is conditional on NOT blocking the mosquito before feeding. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Killing_Config": {
"Box_Duration": 3650,
"Initial_Effect": 0.53429,
"class": "WaningEffectBox"
}
}
|
New_Property_Value |
string |
NA |
NA |
NA |
An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one. |
{
"New_Property_Value": "InterventionStatus:None"
}
|
Primary_Decay_Time_Constant |
float |
0 |
1.00E+0 |
0 |
The primary decay time constant (in days) of the decay profile. |
{
"Primary_Decay_Time_Constant": 180
}
|
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
}
|
{
"Events": [
{
"class": "CampaignEvent",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Start_Day": 120,
"Event_Coordinator_Config": {
"class": "StandardInterventionDistributionEventCoordinator",
"Target_Demographic": "Everyone",
"Demographic_Coverage": 0.5,
"Intervention_Config": {
"class": "InsectKillingFenceHousingModification",
"Blocking_Config": {
"class": "WaningEffectBoxExponential",
"Box_Duration": 100,
"Decay_Time_Constant": 150,
"Initial_Effect": 0.5
},
"Cost_To_Consumer": 10.0,
"Killing_Config": {
"class": "WaningEffectBoxExponential",
"Box_Duration": 100,
"Decay_Time_Constant": 150,
"Initial_Effect": 0.5
}
}
}
}
],
"Use_Defaults": 1
}
The IRSHousingModification intervention class includes Indoor Residual Spraying (IRS) in the simulation. IRS is another key vector control tool in which insecticide is sprayed on the interior walls of a house so that mosquitoes resting on the walls after consuming a blood meal will die. IRS can also have a repellency effect.
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Blocking_Config |
JSON object |
NA |
NA |
NA |
The configuration of pre-feed mosquito repellency and waning for housing modification. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Blocking_Config": {
"Box_Duration": 3650,
"Initial_Effect": 0.0,
"class": "WaningEffectBox"
}
}
|
Cost_To_Consumer |
float |
0 |
999999 |
8 |
The unit cost per housing modification (unamortized). |
{
"Cost_To_Consumer": 10.0
}
|
Disqualifying_Properties |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
Intervention_Name |
string |
NA |
NA |
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"
}
|
Killing_Config |
JSON object |
NA |
NA |
NA |
The configuration of killing efficacy and waning for housing modification. Killing is conditional on NOT blocking the mosquito before feeding. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Killing_Config": {
"Box_Duration": 3650,
"Initial_Effect": 0.53429,
"class": "WaningEffectBox"
}
}
|
New_Property_Value |
string |
NA |
NA |
NA |
An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one. |
{
"New_Property_Value": "InterventionStatus:None"
}
|
Primary_Decay_Time_Constant |
float |
0 |
1.00E+0 |
0 |
The primary decay time constant (in days) of the decay profile. |
{
"Primary_Decay_Time_Constant": 180
}
|
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
}
|
{
"Events": [
{
"class": "CampaignEvent",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Start_Day": 540,
"Event_Coordinator_Config": {
"class": "StandardInterventionDistributionEventCoordinator",
"Target_Demographic": "Everyone",
"Demographic_Coverage": 0.8,
"Intervention_Config": {
"class": "IRSHousingModification",
"Blocking_Config": {
"Box_Duration": 3650,
"Initial_Effect": 0,
"class": "WaningEffectBox"
},
"Cost_To_Consumer": 8,
"Killing_Config": {
"Box_Duration": 3650,
"Initial_Effect": 0.5,
"class": "WaningEffectBox"
}
}
}
}
],
"Use_Defaults": 1
}
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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 |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
Dropout |
boolean |
NA |
NA |
0 |
If set to true (1), when an intervention distribution is missed, all subsequent interventions are also missed. If set to false (0), all calendar dates/doses are applied independently of each other. |
{
"Actual_IndividualIntervention_Config": {
"class": "IVCalendar",
"Dont_Allow_Duplicates": 0,
"Dropout": 0,
"Calendar": [{
"Age": 12045,
"Probability": 1
}]
}
}
|
Intervention_Name |
string |
NA |
NA |
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"
}
|
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 Ivermectin intervention class modifies the feeding outcome probabilities for both indoor- feeding and outdoor-feeding mosquitoes. It is an individually-distributed intervention that configures the waning of the drug’s killing effect on the adult mosquito population. This intervention enables exploration of the impact of giving humans an insecticidal drug, and how the effectiveness and duration of the drug’s killing-effect interacts with other interventions. For example, you can look at the impact of controlling and eliminating malaria transmission using both anti-parasite drugs that clear existing infections and insecticidal drugs.
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Cost_To_Consumer |
float |
0 |
999999 |
8 |
The unit cost per Ivermectin dosing (unamortized). |
{
"Cost_To_Consumer": 6.5
}
|
Disqualifying_Properties |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
Intervention_Name |
string |
NA |
NA |
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"
}
|
Killing_Config |
JSON object |
NA |
NA |
NA |
The configuration of Ivermectin killing efficacy and waning over time. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Killing_Config": {
"Box_Duration": 3650,
"Initial_Effect": 0.53429,
"class": "WaningEffectBox"
}
}
|
New_Property_Value |
string |
NA |
NA |
NA |
An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one. |
{
"New_Property_Value": "InterventionStatus:None"
}
|
{
"Events": [
{
"class": "CampaignEvent",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Start_Day": 120,
"Event_Coordinator_Config": {
"class": "StandardInterventionDistributionEventCoordinator",
"Number_Repetitions": 5,
"Target_Demographic": "Everyone",
"Timesteps_Between_Repetitions": 3,
"Demographic_Coverage": 0.8,
"Intervention_Config": {
"class": "Ivermectin",
"Cost_To_Consumer": 1,
"Killing_Config": {
"Box_Duration": 3,
"Initial_Effect": 1,
"class": "WaningEffectBox"
}
}
}
}
],
"Use_Defaults": 1
}
The MalariaDiagnostic intervention class is similar to SimpleDiagnostic, but distributes a test for malaria. There are three types of configurable diagnostics, each with their own sensitivity parameters.
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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 1, the diagnostic always accurately reflects the condition. 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
}
|
Detection_Threshold |
float |
0 |
1.00E+0 |
100 |
The diagnostic detection threshold for parasites, in units of microliters of blood. Used when Diagnostic_Type is set to Other. |
{
"Detection_Threshold": 40
}
|
Diagnostic_Type |
enum |
NA |
NA |
Microscopy |
The type of malaria diagnostic used. Possible values are:
|
{
"Diagnostic_Type": "Other"
}
|
Disqualifying_Properties |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
Event_Or_Config |
enum |
NA |
NA |
Config |
Specifies whether the current intervention (or a positive diagnosis, depending on the intervention class) distributes a nested intervention (the Config option) or an event will be broadcast which may trigger other interventions in the campaign file (the Event option). Possible values are:
|
{
"Event_Or_Config": "Config"
}
|
Intervention_Name |
string |
NA |
NA |
NA |
The optional name used to refer to this intervention as a means to differentiate it from others that use the same class. |
{
"Intervention_Name":"Diagnostic_Sample"
}
|
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 |
No_Trigger |
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",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Start_Day": 18050,
"Event_Coordinator_Config": {
"class": "StandardInterventionDistributionEventCoordinator",
"Target_Demographic": "Everyone",
"Demographic_Coverage": 0.5,
"Intervention_Config": {
"class": "NodeLevelHealthTriggeredIV",
"Trigger_Condition": "TriggerString",
"Trigger_Condition_String": "Diagnostic_Survey_2",
"Actual_IndividualIntervention_Config": {
"class": "MalariaDiagnostic",
"Detection_Threshold": 40,
"Diagnostic_Type": "Other",
"Event_Or_Config": "Config",
"Positive_Diagnosis_Config": {
"class": "MultiInterventionDistributor",
"Intervention_List": [{
"class": "BroadcastEventToOtherNodes",
"Event_Trigger": "Diagnostic_Survey_3",
"Include_My_Node": 1,
"Max_Distance_To_Other_Nodes_Km": 0.1,
"Node_Selection_Type": "DISTANCE_ONLY"
},
{
"class": "BroadcastEventToOtherNodes",
"Event_Trigger": "Give_Drugs",
"Include_My_Node": 1,
"Max_Distance_To_Other_Nodes_Km": 0.1,
"Node_Selection_Type": "DISTANCE_ONLY"
}
]
}
}
}
}
}],
"Use_Defaults": 1
}
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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 |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
Duration_At_Node_Distribution_Type |
enum |
NA |
NA |
NOT_INITIALIZED |
The shape of the distribution for the amount of time spent at the destination node for intervention-based migration. Possible values are:
|
{
"Duration_At_Node_Distribution_Type": "GAUSSIAN_DURATION"
}
|
Duration_At_Node_Exponential_Period |
float |
0 |
3.40E+3 |
6 |
The period (1/rate) to use for an exponential distribution for the amount of time spent at the destination node when Duration_At_Node_Distribution_Type is set to EXPONENTIAL_DURATION. |
{
"Duration_At_Node_Distribution_Type": "EXPONENTIAL_DURATION",
"Duration_At_Node_Exponential_Period": 4
}
|
Duration_At_Node_Fixed |
float |
0 |
3.40E+3 |
6 |
The value to use for a fixed distribution for the amount of time spent at the destination node when Duration_At_Node_Distribution_Type is set to FIXED_DURATION. |
{
"Duration_At_Node_Distribution_Type" : "FIXED_DURATION",
"Duration_At_Node_Fixed": 10
}
|
Duration_At_Node_Gausian_Mean |
float |
0 |
3.40E+3 |
6 |
The mean value to use for a Gaussian distribution for the amount of time spent at the destination node when Duration_At_Node_Distribution_Type is set to GAUSSIAN_DURATION. |
{
"Duration_At_Node_Distribution_Type": "GAUSSIAN_DURATION",
"Duration_At_Node_Gausian_Mean": 9.0,
"Duration_At_Node_Gausian_StdDev": 2.0
}
|
Duration_At_Node_Gausian_StdDev |
float |
0 |
3.40E+3 |
1 |
The standard deviation to use for a Gaussian distribution for the amount of time spent at the destination node when Duration_At_Node_Distribution_Type is set to GAUSSIAN_DURATION. |
{
"Duration_At_Node_Distribution_Type": "GAUSSIAN_DURATION",
"Duration_At_Node_Gausian_Mean": 9.0,
"Duration_At_Node_Gausian_StdDev": 2.0
}
|
Duration_At_Node_Poisson_Mean |
float |
0 |
3.40E+3 |
6 |
The mean to use for a Poisson distribution for the amount of time spent at the destination node when Duration_At_Node_Distribution_Type is set to POISSON_DURATION. |
{
"Duration_At_Node_Distribution_Type": "POISSON_DURATION",
"Duration_At_Node_Poisson_Mean": 4
}
|
Duration_At_Node_Uniform_Max |
float |
0 |
3.40E+3 |
0 |
The maximum value to use for a uniform distribution for the amount of time spent at the destination node when Duration_At_Node_Distribution_Type is set to UNIFORM_DURATION. |
{
"Duration_At_Node_Distribution_Type": "UNIFORM_DURATION",
"Duration_At_Node_Uniform_Max": 75,
"Duration_At_Node_Uniform_Min": 45
}
|
Duration_At_Node_Uniform_Min |
float |
0 |
3.40E+3 |
0 |
The minimum value to use for a uniform distribution for the amount of time spent at the destination node when Duration_At_Node_Distribution_Type is set to UNIFORM_DURATION. |
{
"Duration_At_Node_Distribution_Type": "UNIFORM_DURATION",
"Duration_At_Node_Uniform_Max": 75,
"Duration_At_Node_Uniform_Min": 45
}
|
Duration_Before_Leaving_Distribution_Type |
enum |
NA |
NA |
NOT_INITIALIZED |
The shape of the distribution of the number of days to wait before migrating to the destination node for intervention-based migration. Possible values are:
|
{
"Duration_Before_Leaving_Distribution_Type": "FIXED_DURATION"
}
|
Duration_Before_Leaving_Exponential_Period |
float |
0 |
3.40E+3 |
6 |
The period (1/rate) to use for an exponential distribution of the number of days to wait before migrating to the destination node when Duration_Before_Leaving_Distribution_Type is set to EXPONENTIAL_DURATION. |
{
"Duration_Before_Leaving_Distribution_Type": "EXPONENTIAL_DURATION",
"Duration_Before_Leaving_Exponential_Period": 4
}
|
Duration_Before_Leaving_Fixed |
float |
0 |
3.40E+3 |
6 |
The value to use for a fixed distribution of the number of days to wait before migrating to the destination node when Duration_Before_Leaving_Distribution_Type is set to FIXED_DURATION. |
{
"Duration_Before_Leaving_Distribution_Type": "FIXED_DURATION",
"Duration_Before_Leaving_Fixed": 4
}
|
Duration_Before_Leaving_Gausian_Mean |
float |
0 |
3.40E+3 |
6 |
The mean value to use for a Gaussian distribution of the number of days to wait before migrating to the destination node when Duration_Before_Leaving_Distribution_Type is set to GAUSSIAN_DURATION. |
{
"Duration_Before_Leaving_Distribution_Type": "GAUSSIAN_DURATION",
"Duration_Before_Leaving_Gausian_Mean": 9.0,
"Duration_Before_Leaving_Gausian_StdDev": 2.0
}
|
Duration_Before_Leaving_Gausian_StdDev |
float |
0 |
3.40E+3 |
1 |
The standard deviation to use for a Gaussian distribution of the number of days to wait before migrating to the destination node when Duration_Before_Leaving_Distribution_Type is set to GAUSSIAN_DURATION. |
{
"Duration_Before_Leaving_Distribution_Type": "GAUSSIAN_DURATION",
"Duration_Before_Leaving_Gausian_Mean": 9.0,
"Duration_Before_Leaving_Gausian_StdDev": 2.0
}
|
Duration_Before_Leaving_Poisson_Mean |
float |
0 |
3.40E+3 |
6 |
The mean to use for a Poisson distribution of the number of days to wait before migrating to the destination node when Duration_Before_Leaving_Distribution_Type is set to POISSON_DURATION. |
{
"Duration_Before_Leaving_Distribution_Type": "POISSON_DURATION",
"Duration_Before_Leaving_Poisson_Mean": 3.0,
}
|
Duration_Before_Leaving_Uniform_Max |
float |
0 |
3.40E+3 |
0 |
The maximum value to use for a uniform distribution of the number of days to wait before migrating to the destination node when Duration_Before_Leaving_Distribution_Type is set to UNIFORM_DURATION. |
{
"Duration_Before_Leaving_Distribution_Type": "UNIFORM_DURATION",
"Duration_Before_Leaving_Uniform_Max": 14,
"Duration_Before_Leaving_Uniform_Min": 0,
}
|
Duration_Before_Leaving_Uniform_Min |
float |
0 |
3.40E+3 |
0 |
The minimum value to use for a uniform distribution of the number of days to wait before migrating to the destination node when Duration_Before_Leaving_Distribution_Type is set to UNIFORM_DURATION. |
{
"Duration_Before_Leaving_Distribution_Type": "UNIFORM_DURATION",
"Duration_Before_Leaving_Uniform_Max": 14,
"Duration_Before_Leaving_Uniform_Min": 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_Name":"Diagnostic_Sample"
}
|
Is_Moving |
boolean |
NA |
NA |
0 |
Set to true (1) to indicate the individual is permanently moving to a new home node for intervention-based migration. |
{
"Is_Moving": 1
}
|
New_Property_Value |
string |
NA |
NA |
NA |
An optional 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_Type": "FIXED_DURATION",
"Duration_Before_Leaving_Fixed": 0,
"Duration_At_Node_Distribution_Type": "FIXED_DURATION",
"Duration_At_Node_Fixed": 999,
"Is_Moving": 0
}
}
}
]
}
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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 |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
Intervention_Name |
string |
NA |
NA |
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"
}
|
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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 |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
Intervention_Name |
string |
NA |
NA |
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"
}
|
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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 |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
Intervention_List |
array of JSON objects |
NA |
NA |
NA |
The list of individual interventions that is distributed by MultiInterventionDistributor. |
{
"Actual_IndividualIntervention_Config": {
"Intervention_List": [{
"Cost_To_Consumer": 1,
"Dosing_Type": "FullTreatmentNewDetectionTech",
"Drug_Type": "Artemether",
"class": "AntimalarialDrug"
},
{
"Cost_To_Consumer": 1,
"Dosing_Type": "FullTreatmentNewDetectionTech",
"Drug_Type": "Lumefantrine",
"class": "AntimalarialDrug"
}
],
"class": "MultiInterventionDistributor"
}
}
|
Intervention_Name |
string |
NA |
NA |
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"
}
|
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": "FIXED_DURATION",
"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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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 |
|---|---|---|---|---|---|---|
Antigen |
integer |
0 |
10 |
0 |
The antigenic ID of the outbreak infection. |
{
"Intervention_Config": {
"Antigen": 0,
"Genome": 0,
"Outbreak_Source": "PrevalenceIncrease",
"class": "OutbreakIndividual"
}
}
|
Genome |
integer |
-2.15E+0 |
2.15E+0 |
0 |
The genetic ID of the outbreak infection. |
{
"Intervention_Config": {
"Antigen": 0,
"Genome": 0,
"Outbreak_Source": "PrevalenceIncrease",
"class": "OutbreakIndividual",
"Incubation_Period_Override": 0
}
}
|
Ignore_Immunity |
boolean |
NA |
NA |
1 |
Individuals will be force-infected (with a specific strain) regardless of actual immunity level when set to true (1). |
{
"Intervention_Config": {
"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": 0.005,
"Intervention_Config": {
"Outbreak_Source": "PrevalenceIncrease",
"class": "OutbreakIndividual"
},
"class": "StandardInterventionDistributionEventCoordinator"
},
"Event_Name": "Outbreak",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Start_Day": 1,
"class": "CampaignEvent"
}],
"Use_Defaults": 1
}
The OutbreakIndividualMalaria class extends OutbreakIndividual class and allows for specifying a specific strain of infection.
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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 |
|---|---|---|---|---|---|---|
Antigen |
integer |
0 |
10 |
0 |
The antigenic ID of the outbreak infection. |
{
"Intervention_Config": {
"Antigen": 0,
"Genome": 0,
"Outbreak_Source": "PrevalenceIncrease",
"class": "OutbreakIndividual"
}
}
|
Create_Random_Genome |
boolean |
NA |
NA |
0 |
If set to true (1), then a random genome is created for the infection and the Genome_Markers parameter is not used. If set to false (0), then you must define the Genome_Markers parameter which allows you to then specify genetic components in a strain of infection. |
{
"OIM_Create_Random_Genome": 1
}
|
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 |
NA |
NA |
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
}
|
{
"Intervention_Config": {
"class": "OutbreakIndividualMalaria",
"Antigen": 0,
"Genome_Markers": [ "D6", "W2" ]
}
}
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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 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 |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
Intervention_Name |
string |
NA |
NA |
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+3 |
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 RTSSVaccine intervention class protects individuals against infection acquisition by directly boosting the circumsporozoite protein (CSP) antibody concentration. This contrasts with the SimpleVaccine intervention, which is used to modify the probability of acquisition or transmission.
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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 |
|---|---|---|---|---|---|---|
Antibody_Type |
enum |
NA |
NA |
CSP |
The antibody type used to determine immune response targets. Possible values are:
|
{
"Intervention_Config": {
"Antibody_Type": "CSP",
"Antibody_Variant": 0,
"Boosted_Antibody_Concentration": 1200,
"Cost_To_Consumer": 1.0,
"class": "RTSSVaccine"
}
}
|
Antibody_Variant |
integer |
0 |
2.15E+0 |
0 |
The antibody variant index. |
{
"Intervention_Config": {
"Antibody_Type": "CSP",
"Antibody_Variant": 0,
"Boosted_Antibody_Concentration": 1200,
"Cost_To_Consumer": 1.0,
"class": "RTSSVaccine"
}
}
|
Boosted_Antibody_Concentration |
float |
0 |
1.00E+0 |
1 |
The boosted antibody concentration, where unity equals maximum from natural exposure. |
{
"Intervention_Config": {
"Antibody_Type": "CSP",
"Antibody_Variant": 0,
"Boosted_Antibody_Concentration": 1200,
"Cost_To_Consumer": 1.0,
"class": "RTSSVaccine"
}
}
|
Cost_To_Consumer |
float |
0 |
999999 |
3.75 |
The unit cost of RTS,S vaccination (unamortized). |
{
"Intervention_Config": {
"Antibody_Type": "CSP",
"Antibody_Variant": 0,
"Boosted_Antibody_Concentration": 1200,
"Cost_To_Consumer": 1.0,
"class": "RTSSVaccine"
}
}
|
Disqualifying_Properties |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
Intervention_Name |
string |
NA |
NA |
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"
}
|
{
"Events": [{
"class": "CampaignEvent",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Start_Day": 20,
"Event_Coordinator_Config": {
"class": "StandardInterventionDistributionEventCoordinator",
"Target_Demographic": "Everyone",
"Demographic_Coverage": 0.8,
"Intervention_Config": {
"class": "RTSSVaccine",
"Antibody_Type": "CSP",
"Antibody_Variant": 0,
"Boosted_Antibody_Concentration": 1200,
"Cost_To_Consumer": 1.0
}
}
}],
"Use_Defaults": 1
}
The ScreeningHousingModification intervention class implements housing screens as a vector control effort. Housing screens are used to decrease the number of mosquitoes that can enter indoors and therefore reduce indoor biting.
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Blocking_Config |
JSON object |
NA |
NA |
NA |
The configuration of pre-feed mosquito repellency and waning for housing modification. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Blocking_Config": {
"Box_Duration": 3650,
"Initial_Effect": 0.0,
"class": "WaningEffectBox"
}
}
|
Cost_To_Consumer |
float |
0 |
999999 |
8 |
The unit cost per housing modification (unamortized). |
{
"Cost_To_Consumer": 10.0
}
|
Disqualifying_Properties |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
Intervention_Name |
string |
NA |
NA |
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"
}
|
Killing_Config |
JSON object |
NA |
NA |
NA |
The configuration of killing efficacy and waning for housing modification. Killing is conditional on NOT blocking the mosquito before feeding. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Killing_Config": {
"Box_Duration": 3650,
"Initial_Effect": 0.53429,
"class": "WaningEffectBox"
}
}
|
New_Property_Value |
string |
NA |
NA |
NA |
An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one. |
{
"New_Property_Value": "InterventionStatus:None"
}
|
Primary_Decay_Time_Constant |
float |
0 |
1.00E+0 |
0 |
The primary decay time constant (in days) of the decay profile. |
{
"Primary_Decay_Time_Constant": 180
}
|
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
}
|
{
"Events": [{
"class": "CampaignEvent",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Start_Day": 120,
"Event_Coordinator_Config": {
"class": "StandardInterventionDistributionEventCoordinator",
"Target_Demographic": "Everyone",
"Demographic_Coverage": 0.8,
"Intervention_Config": {
"class": "ScreeningHousingModification",
"Blocking_Config": {
"Box_Duration": 100,
"Decay_Time_Constant": 150,
"Initial_Effect": 0.8,
"class": "WaningEffectBoxExponential"
},
"Cost_To_Consumer": 1.0,
"Killing_Config": {
"Box_Duration": 100,
"Decay_Time_Constant": 150,
"Initial_Effect": 0.0,
"class": "WaningEffectBoxExponential"
}
}
}
}],
"Use_Defaults": 1
}
The SimpleBednet intervention class implements Insecticide-Treated Nets (ITNs) in the simulation. ITNs are a key component of modern malaria control efforts, and have recently been scaled up towards universal coverage in sub-Saharan Africa. Modern bednets are made of a polyethylene or polyester mesh, which is impregnated with a slowly releasing pyrethroid insecticide. Mosquitoes that are seeking a blood meal will encounter a net and, if the net retains its physical integrity and has been correctly installed, the feeding attempt is blocked. Blocked feeding attempts also carry the possibility of killing the mosquito. Net ownership is configured through the demographic coverage, and the blocking and killing rates of mosquitoes are time- dependent.
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Blocking_Config |
JSON object |
NA |
NA |
NA |
Configures the rate of blocking for indoor mosquito feeds on individuals with an ITN. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Blocking_Config": {
"Box_Duration": 3650,
"Initial_Effect": 0,
"class": "WaningEffectBox"
}
}
|
Cost_To_Consumer |
float |
0 |
999999 |
3.75 |
The unit cost per bednet (unamortized) |
{
"Cost_To_Consumer": 4.5
}
|
Disqualifying_Properties |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
Intervention_Name |
string |
NA |
NA |
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"
}
|
Killing_Config |
JSON object |
NA |
NA |
NA |
The configuration of the rate at which mosquitoes die, conditional on a successfully blocked feed. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Killing_Config": {
"Box_Duration": 3650,
"Initial_Effect": 0.53429,
"class": "WaningEffectBox"
}
}
|
New_Property_Value |
string |
NA |
NA |
NA |
An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one. |
{
"New_Property_Value": "InterventionStatus:None"
}
|
Primary_Decay_Time_Constant |
float |
0 |
1.00E+0 |
0 |
The primary decay time constant (in days) of the decay profile. |
{
"Primary_Decay_Time_Constant": 180
}
|
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
}
|
Usage_Config |
JSON object |
NA |
NA |
NA |
The user-defined WaningEffects to determine when and if an individual is using a bed net. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Usage_Config": {
"class": "WaningEffectConstant",
"Initial_Effect": 1.0
}
}
|
{
"Events": [{
"class": "CampaignEvent",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Start_Day": 1460,
"Event_Coordinator_Config": {
"class": "StandardInterventionDistributionEventCoordinator",
"Target_Demographic": "Everyone",
"Demographic_Coverage": 0.7,
"Intervention_Config": {
"class": "SimpleBednet",
"Cost_To_Consumer": 3.75,
"Killing_Config": {
"Initial_Effect": 0.6,
"Decay_Time_Constant": 1460,
"class": "WaningEffectExponential"
},
"Blocking_Config": {
"Initial_Effect": 0.9,
"Decay_Time_Constant": 730,
"class": "WaningEffectExponential"
},
"Usage_Config": {
"class": "WaningEffectConstant",
"Initial_Effect": 1.0
}
}
}
}],
"Use_Defaults": 1
}
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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 |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
Efficacy_Is_Multiplicative |
boolean |
NA |
NA |
1 |
The overall vaccine efficacy when individuals receive more than one vaccine. When set to true (1), the vaccine efficacies are multiplied together; when set to false (0), the efficacies are additive. |
{
"Intervention_Config": {
"class": "SimpleVaccine",
"Cost_To_Consumer": 10,
"Vaccine_Type": "AcquisitionBlocking",
"Vaccine_Take": 1,
"Efficacy_Is_Multiplicative": 0,
"Waning_Config": {
"class": "WaningEffectConstant",
"Initial_Effect": 0.3
}
}
}
|
Intervention_Name |
string |
NA |
NA |
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"
}
|
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Cost_To_Consumer |
float |
0 |
3.40E+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 |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
Intervention_Name |
string |
NA |
NA |
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 |
No_Trigger |
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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": 0.000000001,
"class": "AntiTBDrug"
}
}
|
Actual_IndividualIntervention_Event |
enum |
NA |
NA |
NoTrigger |
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 |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
Event_Or_Config |
enum |
NA |
NA |
Config |
Specifies whether the current intervention (or a positive diagnosis, depending on the intervention class) distributes a nested intervention (the Config option) or an event will be broadcast which may trigger other interventions in the campaign file (the Event option). Possible values are:
|
{
"Event_Or_Config": "Config"
}
|
Intervention_Name |
string |
NA |
NA |
NA |
The optional name used to refer to this intervention as a means to differentiate it from others that use the same class. |
{
"Intervention_Name":"Diagnostic_Sample"
}
|
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 |
NA |
NA |
1 |
If set to true (1), the health-seeking behavior gets used once and discarded. If set to false (0), it remains indefinitely. |
{
"Single_Use": 1
}
|
Tendency |
float |
0 |
1 |
1 |
The probability of seeking healthcare. |
{
"Tendency": 0.01
}
|
{
"Use_Defaults": 1,
"Events": [{
"class": "CampaignEvent",
"Event_Name": "Drugs after TB activation",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Start_Day": 9125,
"Event_Coordinator_Config": {
"class": "StandardInterventionDistributionEventCoordinator",
"Number_Repetitions": 1,
"Target_Demographic": "Everyone",
"Demographic_Coverage": 1,
"Intervention_Config": {
"class": "NodeLevelHealthTriggeredIV",
"Trigger_Condition_List": ["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 SimpleHousingModification intervention class implements a housing modification for vector control. It is the base class from which other housing modifications are derived.
Note
Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Blocking_Config |
JSON object |
NA |
NA |
NA |
The configuration of pre-feed mosquito repellency and waning for housing modification. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Blocking_Config": {
"Box_Duration": 3650,
"Initial_Effect": 0.0,
"class": "WaningEffectBox"
}
}
|
Cost_To_Consumer |
float |
0 |
999999 |
8 |
The unit cost per housing modification (unamortized). |
{
"Cost_To_Consumer": 10.0
}
|
Disqualifying_Properties |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
Intervention_Name |
string |
NA |
NA |
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"
}
|
Killing_Config |
JSON object |
NA |
NA |
NA |
The configuration of killing efficacy and waning for housing modification. Killing is conditional on NOT blocking the mosquito before feeding. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Killing_Config": {
"Box_Duration": 3650,
"Initial_Effect": 0.53429,
"class": "WaningEffectBox"
}
}
|
New_Property_Value |
string |
NA |
NA |
NA |
An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one. |
{
"New_Property_Value": "InterventionStatus:None"
}
|
{
"Events": [{
"class": "CampaignEvent",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Start_Day": 120,
"Event_Coordinator_Config": {
"class": "StandardInterventionDistributionEventCoordinator",
"Target_Demographic": "Everyone",
"Demographic_Coverage": 0.8,
"Intervention_Config": {
"class": "SimpleHousingModification",
"Blocking_Config": {
"Box_Duration": 100,
"Decay_Time_Constant": 150,
"Initial_Effect": 0.6,
"class": "WaningEffectBoxExponential"
},
"Cost_To_Consumer": 1.0,
"Killing_Config": {
"Box_Duration": 100,
"Decay_Time_Constant": 150,
"Initial_Effect": 0.4,
"class": "WaningEffectBoxExponential"
}
}
}
}],
"Use_Defaults": 1
}
The SimpleIndividualRepellent intervention class provides protection to individuals against both indoor-feeding and outdoor-feeding mosquito bites.
Note
Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Blocking_Config |
JSON object |
NA |
NA |
NA |
The configuration of efficacy and waning for individual repellent. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Intervention_Config": {
"Blocking_Config": {
"Box_Duration": 100,
"Decay_Time_Constant": 150,
"Initial_Effect": 0.8,
"class": "WaningEffectBoxExponential"
},
"Cost_To_Consumer": 10.0,
"class": "SimpleIndividualRepellent"
}
}
|
Cost_To_Consumer |
float |
0 |
999999 |
8 |
The unit cost per repellent (unamortized). |
{
"Intervention_Config": {
"Blocking_Config": {
"Box_Duration": 100,
"Decay_Time_Constant": 150,
"Initial_Effect": 0.8,
"class": "WaningEffectBoxExponential"
},
"Cost_To_Consumer": 10.0,
"class": "SimpleIndividualRepellent"
}
}
|
Disqualifying_Properties |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
Intervention_Name |
string |
NA |
NA |
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"
}
|
Primary_Decay_Time_Constant |
float |
0 |
1.00E+0 |
0 |
The primary decay time constant (in days) of the decay profile. |
{
"Primary_Decay_Time_Constant": 180
}
|
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
}
|
{
"Events": [{
"class": "CampaignEvent",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Start_Day": 120,
"Event_Coordinator_Config": {
"class": "StandardInterventionDistributionEventCoordinator",
"Target_Demographic": "Everyone",
"Demographic_Coverage": 0.5,
"Intervention_Config": {
"class": "SimpleIndividualRepellent",
"Blocking_Config": {
"Box_Duration": 100,
"Decay_Time_Constant": 150,
"Initial_Effect": 0.8,
"class": "WaningEffectBoxExponential"
},
"Cost_To_Consumer": 10.0
}
}
}],
"Use_Defaults": 1
}
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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 |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
Efficacy_Is_Multiplicative |
boolean |
NA |
NA |
1 |
The overall vaccine efficacy when individuals receive more than one vaccine. When set to true (1), the vaccine efficacies are multiplied together; when set to false (0), the efficacies are additive. |
{
"Intervention_Config": {
"class": "SimpleVaccine",
"Cost_To_Consumer": 10,
"Vaccine_Type": "AcquisitionBlocking",
"Vaccine_Take": 1,
"Efficacy_Is_Multiplicative": 0,
"Waning_Config": {
"class": "WaningEffectConstant",
"Initial_Effect": 0.3
}
}
}
|
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_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"
}
|
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
}
The SpatialRepellentHousingModification intervention class is a housing modification utilizing spatial repellents. The protection provided by this intervention is exclusively against indoor-biting mosquitoes.
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Blocking_Config |
JSON object |
NA |
NA |
NA |
The configuration of pre-feed mosquito repellency and waning for housing modification. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Blocking_Config": {
"Box_Duration": 3650,
"Initial_Effect": 0.0,
"class": "WaningEffectBox"
}
}
|
Cost_To_Consumer |
float |
0 |
999999 |
8 |
The unit cost per housing modification (unamortized). |
{
"Cost_To_Consumer": 10.0
}
|
Disqualifying_Properties |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
Intervention_Name |
string |
NA |
NA |
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"
}
|
Killing_Config |
JSON object |
NA |
NA |
NA |
The configuration of killing efficacy and waning for housing modification. Killing is conditional on NOT blocking the mosquito before feeding. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Killing_Config": {
"Box_Duration": 3650,
"Initial_Effect": 0.53429,
"class": "WaningEffectBox"
}
}
|
New_Property_Value |
string |
NA |
NA |
NA |
An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one. |
{
"New_Property_Value": "InterventionStatus:None"
}
|
Primary_Decay_Time_Constant |
float |
0 |
1.00E+0 |
0 |
The primary decay time constant (in days) of the decay profile. |
{
"Primary_Decay_Time_Constant": 180
}
|
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
}
|
{
"Events": [
{
"class": "CampaignEvent",
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Start_Day": 120,
"Event_Coordinator_Config": {
"class": "StandardInterventionDistributionEventCoordinator",
"Target_Demographic": "Everyone",
"Demographic_Coverage": 0.8,
"Intervention_Config": {
"class": "SpatialRepellentHousingModification",
"Blocking_Config": {
"Box_Duration": 100,
"Decay_Time_Constant": 150,
"Initial_Effect": 0.8,
"class": "WaningEffectBoxExponential"
},
"Cost_To_Consumer": 1.0,
"Killing_Config": {
"Box_Duration": 100,
"Decay_Time_Constant": 150,
"Initial_Effect": 0.1,
"class": "WaningEffectBoxExponential"
}
}
}
}
],
"Use_Defaults": 1
}
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.
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.
Parameter |
Data type |
Minimum |
Maximum |
Default |
Description |
Example |
|---|---|---|---|---|---|---|
Blocking_Config |
JSON object |
NA |
NA |
NA |
Configures the rate of blocking for indoor mosquito feeds on individuals with an ITN. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Blocking_Config": {
"Box_Duration": 3650,
"Initial_Effect": 0,
"class": "WaningEffectBox"
}
}
|
Cost_To_Consumer |
float |
0 |
999999 |
3.75 |
The unit cost per bednet (unamortized) |
{
"Cost_To_Consumer": 4.5
}
|
Discard_Event |
enum |
NA |
NA |
NoTrigger |
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. |
{
"Received_Event": "Bednet_Got_New_One",
"Using_Event": "Bednet_Using",
"Discard_Event": "Bednet_Discarded",
"Expiration_Distribution_Type": "FIXED_DURATION",
"Expiration_Period": 50
}
|
Disqualifying_Properties |
string |
NA |
NA |
NA |
A list of IndividualProperty key:value pairs that cause an intervention to be aborted (persistent interventions will stop being distributed to individuals with these values). See NodeProperties and IndividualProperties parameters for more information. Generally used to control the flow of health care access. For example, to prevent the same individual from accessing health care via two different routes at the same time. |
{
"Disqualifying_Properties": [
"InterventionStatus:LostForever"
]
}
|
Dont_Allow_Duplicates |
boolean |
NA |
NA |
0 |
If an individual’s container has an intervention, set to true (1) to prevent them from receiving another copy of the intervention. Supported by all intervention classes. |
{
"Dont_Allow_Duplicates": 0
}
|
Expiration_Distribution_Type |
enum |
NA |
NA |
NOT_INITIALIZED |
The type of distribution to use when calculating the time to discard a bed net. Possible values are:
|
{
"Received_Event": "Bednet_Got_New_One",
"Using_Event": "Bednet_Using",
"Discard_Event": "Bednet_Discarded",
"Expiration_Distribution_Type": "FIXED_DURATION",
"Expiration_Period": 50
}
|
Expiration_Percentage_Period_1 |
float |
0 |
1 |
0.5 |
The percentage of draws where the first time-scale is used. Expiration_Distribution_Type must be set to DUAL_TIMESCALE_DURATION. |
{
"Expiration_Distribution_Type": "DUAL_TIMESCALE_DURATION",
"Expiration_Period_1": 150,
"Expiration_Period_2": 50,
"Expiration_Percentage_Period_1": 0.9
}
|
Expiration_Period |
float |
0 |
3.40E+3 |
6.00E+00 |
The distribution period for when a bed net expires. Expiration_Distribution_Type must be set to FIXED_DURATION or EXPONENTIAL_DURATION. |
{
"Expiration_Distribution_Type": "FIXED_DURATION",
"Expiration_Period": 50
}
|
Expiration_Period_1 |
float |
0 |
3.40E+3 |
6 |
The decay length of the first time-scale, when Expiration_Distribution_Type is set to DUAL_TIMESCALE_DURATION. |
{
"Expiration_Distribution_Type": "DUAL_TIMESCALE_DURATION",
"Expiration_Period_1": 150,
"Expiration_Period_2": 50,
"Expiration_Percentage_Period_1": 0.9
}
|
Expiration_Period_2 |
float |
0 |
3.40E+3 |
6 |
The decay length of the second time-scale, when Expiration_Distribution_Type is set to DUAL_TIMESCALE_DURATION. |
{
"Expiration_Distribution_Type": "DUAL_TIMESCALE_DURATION",
"Expiration_Period_1": 150,
"Expiration_Period_2": 50,
"Expiration_Percentage_Period_1": 0.9
}
|
Expiration_Period_Max |
float |
0 |
3.40E+3 |
0 |
The maximum duration of use for a bed net when Expiration_Distribution_Type is set to UNIFORM_DURATION. |
{
"Expiration_Distribution_Type": "UNIFORM_DURATION",
"Expiration_Period_Max": 50,
"Expiration_Period_Min": 20
}
|
Expiration_Period_Mean |
float |
0 |
3.40E+3 |
6 |
The mean of the duration of use for a bed net when Expiration_Distribution_Type is set to GAUSSIAN_DURATION. |
{
"Expiration_Distribution_Type": "GAUSSIAN_DURATION",
"Expiration_Period_Mean": 10,
"Expiration_Period_Std_Dev": 1
}
|
Expiration_Period_Min |
float |
0 |
3.40E+3 |
0 |
The minimum duration of use for a bed net when Expiration_Distribution_Type is set to UNIFORM_DURATION. |
{
"Expiration_Distribution_Type": "UNIFORM_DURATION",
"Expiration_Period_Max": 50,
"Expiration_Period_Min": 20
}
|
Expiration_Period_Std_Dev |
float |
0 |
3.40E+3 |
1 |
The standard deviation for the duration of use for a bed net when Expiration_Distribution_Type is set to GAUSSIAN_DURATION. |
{
"Expiration_Distribution_Type": "GAUSSIAN_DURATION",
"Expiration_Period_Mean": 10,
"Expiration_Period_Std_Dev": 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_Name":"Diagnostic_Sample"
}
|
Killing_Config |
JSON object |
NA |
NA |
NA |
The configuration of the rate at which mosquitoes die, conditional on a successfully blocked feed. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Killing_Config": {
"Box_Duration": 3650,
"Initial_Effect": 0.53429,
"class": "WaningEffectBox"
}
}
|
New_Property_Value |
string |
NA |
NA |
NA |
An optional IndividualProperty key:value pair that will be assigned when the intervention is distributed. See NodeProperties and IndividualProperties parameters for more information. Generally used to indicate the broad category of health care cascade to which an intervention belongs to prevent individuals from accessing care through multiple pathways. For example, if an individual must already be taking a particular medication to be prescribed a new one. |
{
"New_Property_Value": "InterventionStatus:None"
}
|
Received_Event |
enum |
NA |
NA |
NoTrigger |
This parameter broadcasts when a new net is received, either the first net or a replacement net. See Event list for possible values. |
{
"Received_Event": "Bednet_Got_New_One",
"Using_Event": "Bednet_Using",
"Discard_Event": "Bednet_Discarded"
}
|
Usage_Config_List |
array of JSON objects |
NA |
NA |
NA |
The list of WaningEffects whose effects are multiplied together to get the usage effect. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Usage_Config_List": [{
"class": "WaningEffectConstant",
"Initial_Effect": 1.0
}]
}
|
Using_Event |
enum |
NA |
NA |
NoTrigger |
This parameter broadcasts each time step in which a bed net is used. See Event list for possible values. |
{
"Received_Event": "Bednet_Got_New_One",
"Using_Event": "Bednet_Using",
"Discard_Event": "Bednet_Discarded"
}
|
{
"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": 0,
"Intervention_Config": {
"class": "UsageDependentBednet",
"Cost_To_Consumer": 5,
"Usage_Config_List":
[
{
"class": "WaningEffectConstant",
"Initial_Effect": 1.0
}
],
"Blocking_Config": {
"class": "WaningEffectConstant",
"Initial_Effect": 1.0
},
"Killing_Config": {
"class": "WaningEffectConstant",
"Initial_Effect": 0.5
},
"Received_Event" : "Bednet_Got_New_One",
"Using_Event" : "Bednet_Using",
"Discard_Event" : "Bednet_Discarded",
"Expiration_Distribution_Type" : "FIXED_DURATION",
"Expiration_Period" : 50
}
}
}
],
"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
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. JSON does not permit comments, but you can add “dummy” parameters to add contextual information to your files.
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",
"Box_Duration": 100,
"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. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Box_Duration": 40
}
|
Initial_Effect |
float |
0 |
1 |
-1 |
Initial strength of the effect. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Initial_Effect": 1.0
}
|
The initial efficacy is held for a specified duration, then the efficacy decays at an exponential rate.
{
"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. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Box_Duration": 3650
}
|
Decay_Time_Constant |
float |
0 |
100000 |
100 |
The exponential decay length, in days. |
{
"Decay_Time_Constant": 30
}
|
Initial_Effect |
float |
0 |
1 |
-1 |
Initial strength of the effect. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Initial_Effect": 1.0
}
|
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 |
Initial strength of the effect. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Initial_Effect": 1.0
}
|
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 |
Initial strength of the effect. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Initial_Effect": 1.0
}
|
The efficacy decays at an exponential rate.
{
"Intervention_Config": {
"class": "SimpleBednet",
"Blocking_Config": {
"class": "WaningEffectExponential",
"Box_Duration": 100,
"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. |
{
"Decay_Time_Constant": 30
}
|
Initial_Effect |
float |
0 |
1 |
-1 |
Initial strength of the effect. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Initial_Effect": 1.0
}
|
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 |
Initial strength of the effect. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Initial_Effect": 1.0
}
|
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,
0.260000,
0.080000,
0.140000,
0.540000
]
}
|
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.
{
"Intervention_Config": {
"class": "UsageDependentBednet",
"Usage_Config_List": [{
"class": "WaningEffectMapLinearAge",
"Initial_Effect": 1.0,
"Durability_Map": {
"Times": [0.0, 12.99999, 13.0, 125.0],
"Values": [0.0, 0.0, 1.0, 1.0]
}
}]
}
}
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 |
Initial strength of the effect. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Initial_Effect": 1.0
}
|
Times |
array of floats |
0 |
125 |
NA |
An array of years. |
{
"Durability_Map": {
"Times": [0.0, 12.99999, 13.0, 125.0],
"Values": [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,
0.260000,
0.080000,
0.140000,
0.540000
]
}
|
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 |
Initial strength of the effect. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Initial_Effect": 1.0
}
|
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,
0.260000,
0.080000,
0.140000,
0.540000
]
}
|
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 |
Initial strength of the effect. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Initial_Effect": 1.0
}
|
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,
0.260000,
0.080000,
0.140000,
0.540000
]
}
|
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 |
Initial strength of the effect. Specify how this effect decays over time using one of the Waning effect classes. |
{
"Initial_Effect": 1.0
}
|
Event list¶
The following list provides possible values for built-in events. These events can be broadcast to nodes to trigger interventions.
Note
Not all events are appropriate for every simulation type.
Births
DiseaseDeaths
EighteenMonthsOld
Emigrating
EnteredRelationship
EveryTimeStep
EveryUpdate
ExitedRelationship
FirstCoitalAct
FourteenWeeksPregnant
GaveBirth
HappyBirthday
HIVNewlyDiagnosed
HIVNonPreARTToART
HIVPreARTToART
HIVSymptomatic
HIVTestedNegative
HIVTestedPositive
Immigrating
InterventionDisqualified
NewClinicalCase
NewInfectionEvent
NewSevereCase
NodePropertyChange
NonDiseaseDeaths
NoTrigger
Pregnant
PropertyChange
ProviderOrdersTBTest
SixWeeksOld
StartedART
STIDebut
STINewInfection
STIPostImmigrating
STIPreEmigrating
StoppedART
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 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
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.
Contents
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 data 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 data 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 on Azure environment¶
The following problems are specific to running simulations using the Eradication binary on CentOS 7.1 on Azure.
Regression test graphs differ when run on CentOS on Azure¶
After you run regression simulations on CentOS on Azure 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 on Azure.
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 cannot locate the input data files¶
If you chose not to have the PrepareLinuxEnvironment.sh script download the EMOD source code and input data files, you need to set up the environment variable, path and symlink that are needed to run simulations on CentOS on Azure. See Install EMOD on CentOS on Azure.
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
EMOD software terms¶
The following terms are used to describe both general computing processes and concepts and the files, features, and functionality related to running simulations with Epidemiological MODeling software (EMOD).
- 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 considered part of the input data files and 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 on Azure, 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.
- input data files
Fixed information used as input to a simulation. Examples of such data are population, altitude, demographics, and transportation migrations. The configurable config.json and campaign.json files do not fall into this category of static fixed input.
- inset chart
The default output report for EMOD that includes the number of individuals in each of the disease model compartments (susceptible, exposed, infectious, and recovered) at each time step of the simulation.
- 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 (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.
- JSON
See JavaSCript Object Notation.
- 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.
- 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.
- 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.
- 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-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.
- 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, typically used to refer to one of a set of scenarios included in the EMOD bundle and located in the Regression subdirectory of the bundle. 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, configuration, and campaign files that describes a real-world example of a disease outbreak and interventions. Many scenarios are included with EMOD source installations or are available in the Regression directory of the EMOD GitHub repository. The tutorials describe how to run simulations using these 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 EMOD software execution performed by the kernel. Each simulation has an associated set of files that control the inputs, configuration, and campaign.
- 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)
Vector-borne diseases (VECTOR_SIM)
Malaria (MALARIA_SIM)
Tuberculosis with HIV coinfection (TBHIV_SIM)
Sexually transmitted infections (STI_SIM)
HIV (HIV_SIM)
- 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.
- 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.
COMPS terms¶
The following terms are used to describe processes, concepts, and the files, features, and functionality related to using COMPS.
- asset collection
Collection of user created input files, such as demographics, temperature, weather, and overlay files. These files are stored in COMPS and can be available for use by other users.
- dashboard
The COMPS dashboard provides an overview of computing cluster usage, including current and queued jobs. Resource management is simple due to the job-priority system used by the platform.
- experiments
Logical grouping of simulations. This allows for managing numerous simulations as a single unit or grouping.
- multi-chart
COMPS provides powerful charting functionality to visualize the output channels for simulations. A chart can include output for a single simulation or for multiple simulations. Viewing multiple simulations in a single chart (multi-chart) provides a fast, flexible way to filter simulations to view only data of interest.
- suites
Logical grouping of experiments. This allows for managing multiple experiments as a single unit or grouping.
- work item
Work item is used to build experiments and suites. It builds a set of simulations or groups of simulations, such as creating parameter sweeps. Work item defines how many simulations run at the start of the experiment to determine if the configuration settings are functional.
- work order
JSON formatted file used for the creation of a work item, in combination with a configuration file, and (optional) campaign and additional files.
Epidemiology terms¶
The following terms are used to describe general concepts and processes in the field of epidemiology and disease modeling.
- 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.
- 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.
- 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.
- diffusive migration
The diffusion of people in and out of nearby nodes by foot travel.
- 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 data 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.
- stochastic
Characterized by having a random probability distribution that may be analyzed statistically but not predicted precisely.
- subpatent
When an individual is infected but asymptomatic, so the infection is not readily detectable.
- 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.
- 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.
Malaria and vector terms¶
The following terms are used to describe the biological processes involved in malaria and other vector-borne diseases.
- circumsporozoite protein (CSP)
One of two proteins (the other being thrombospondin-related adhesive protein) involved in sporozoite recognition of host cells in malaria.
- cohort model
A vector model that tracks properties of mosquito “cohorts”, for example, population, age, gender, fertility, etc.
- cytokine
Cytokines are small secreted proteins that are released by cells, and have specific effects on interactions and communications between cells. They are especially important in the immune system, and can function in inflammatory (and anti-inflammatory) response.
- CRISPR
A genome-editing technique that utilizes the CRISPR/Cas9 system (a prokaryotic immune system) that can be used to splice DNA sequences in very specific target locations, enabling the precise insertion or removal of genes of interest.
- entomological inoculation rate (EIR)
The commonly-used measure of the intensity of malaria transmission. EIR is equal to the number of mosquito bites per night times the proportion of those bites that test positive for sporozoites.
- gametocyte
The male and female sexual forms of the blood-stage malaria parasite. During blood-feeding, gametocytes are taken up into the mosquito mid-gut where they commence the mosquito-phase of the parasite life cycle.
- gene drive mosquito
Gene drive is a genetic technique that promotes the inheritance of particular genes or specific genetic elements. In mosquitoes, approaches are being tested that work to suppress the spread of malaria by creating mosquito lines through fertility suppression, driving-Y chromosomes, or express genes that limit malaria transmission.
- gonotrophic
A female mosquito’s feeding and egg-laying cycle, where the duration is defined as the average number of days that gravid mosquitoes take to oviposit after taking a blood meal.
- hepatocyte
The cell type that comprises liver tissue.
- homing endonuclease gene (HEG)
A “selfish” genetic element that can spread through the mosquito population even at a cost to the host. The HEG allele status is a tracked property of mosquitoes. Large numbers of HEG- positive mosquitoes may be introduced into the simulation by a MosquitoRelease intervention.
- individual mosquito model
A vector model simulation where every individual mosquito in the population is modeled, as well as a simulation of a sampled subset of mosquitoes to represent the population as the whole.
- indoor residual spraying (IRS)
The process of spraying insecticide on the interior surfaces of dwellings in order to repel or kill mosquitoes.
- insecticide-treated nets (ITN)
Bednets hung over sleeping areas that are treated with insecticide to repel mosquitoes and kill those that land on them.
- larval habitat
Mosquito larval ecology includes several habitat types including brackish swamp, constant, human population, temporary rainfall, and semi-permanent water vegetation.
- larvicide
A node-targeted intervention that may be configured to have a direct killing effect on the larval population and a temporary reduction on available larval habitat.
- mass drug administration (MDA)
The treatment of an entire population in a geographic area with a curative dose of an antimalarial drug without first testing for infection, and regardless of the presence of symptoms.
- merozoite
The malaria parasite stage that invades red blood cells. After each cycle of asexual reproduction within the infected red blood cell, merozoites are released into the bloodstream to invade new red blood cells.
- merozoites
see “merozoite”
- merozoite surface protein (MSP)
A type of protein integral to red blood cell invasion by malaria parasites that is an important target of the human immune response.
- oviposition
to deposit or lay eggs.
- pre-erythrocytic
Stages of a malaria parasite that occur before the red blood cells are invaded. This includes the period starting from inoculation with sporozoites, through the liver-stage multiplication, up to infected hepatocyte rupture, and the first invasion of erythrocytes by merozoites.
- pre-erythrocytic vaccine (PEV)
A vaccine targeted at the earliest stage of malaria parasite development, namely before the blood-stage infection. Other vaccines target the asexual and sexual stages of the parasite.
- Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1)
A protein encoded by the malaria parasite and expressed on the surface of infected red blood cells. PfEMP1 adheres to various human receptors, allowing sequestration of parasitized red blood cells and avoidance of clearance in the spleen. PfEMP1 is an important antigenic target of the human adapted immune response. It exhibits a high degree of antigenic variation via a genetic switching mechanism evolved to escape immune pressure.
- schizont
Cells formed during the asexual stage of the life cycle of sporozoan protozoans, such as the malaria parasite.
- spatial repellent
An intervention that may be individually-distributed (protective indoors and outdoors), or node-distributed at the community-level, that repels mosquitoes from biting.
- sporogony
The asexual process of spore formation in parasitic sporozoans.
- sporozoite
a motile, spore-like stage of the malaria parasite that is the infectious agent introduced into the host.
- sugar-baited trap
Bait stations, often placed inside houses, that have an attractive and toxic sugar used to attract and kill sugar-feeding male and female mosquitoes.
- superinfection
The simultaneous infection with multiple strains of the same pathogen.
- transmission-blocking vaccine (TBV)
A vaccine that blocks gametocytes from infected humans by producing viable sporozoites in mosquitoes. Also referred to as a “sexual-stage” vaccine, since vaccines in this class block gametocytes from infected humans by producing viable sporozoites in mosquitoes.
- vaccine intervention types
In EMOD, vaccine intervention types are either “AcquisitionBlocking”, “TransmissionBlocking” or “MortalityBlocking”. For example, “mode-of-action”, “targeted parasite stages”, (such as pre-erythrocytic, asexual, or sexual), etc.
- vector
Insect or other living carrier that transmits an infectious agent. The vector life cycle and feeding cycle in the model are described in the article Eckhoff, Malaria Journal 2011, 10:303.
- vectorial capacity
The daily rate of all mosquitoes that would be infected after biting a single infectious host. This measure can be used to describe the transmission intensity of malaria, and serves as a rate at which future infections arise from a currently infected host.
- vector model
The vector model includes both a cohort model and an individual mosquito model. The model is a closed-loop feeding cycle where successful animal feeds (both indoors and outdoors) produce eggs, larva, vector mating, and then, adult mosquitoes. The vector model inherits the same human- infection model structure from the generic simulation type: uninfected, latent incubation, infectious, multiple immune variables, and superinfection. However, the transmission of infections is not between individual humans, but rather via the human-to-vector and vector- to-human pathways.
- Wolbachia
This is a genus of bacteria that infects many arthopod species (especially insects). It has been discovered that insects infected with Wolbachia have increased resistance to viral or other parasitic infections. Work is underway to determine if strains of modified Wolbachia can be used to control malaria, by effectively preventing mosquitoes from becoming infected with Plasmodium parasites.
EMOD source code installation¶
This section describes how to install the software needed to build the EMOD executable (Eradication.exe) or Eradication binary 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 on computers running CentOS 7.1 on Azure.
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 input data 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 is tested and supported on a CentOS 7.1 on Azure virtual machine. 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/ to install Python 3.6.
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 check the Add Python 3.6 to PATH checkbox and click Customize installation.
On the Optional Features window, leave all default values selected and click Next. The Python package manager, pip, is used to install other Python packages.
On the Advanced Options window, we recommend that you customize the installation location to “C:Python36”.
You may install Python in another location, but the Python plotting scripts included in the EMODScenarios folder 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
If you need to keep Python 2.7 installed alongside Python 3.6, we recommend that you install
virtualenvandvirtualenvwrapper, tools used to create and work in isolated Python environments. See Pipenv & Virtual Environments for installation instructions.From Control Panel, select Advanced system settings, and then click Environment Variables.
To build the source code using Python 3.6, add a new variable called IDM_PYTHON3_PATH and set it to the directory where you installed Python 3.6, 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.
SCons (optional)¶
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 1.14.5 (64-bit) that is compatible with Python 3.6.
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.
Enter the following commands:
pip install python-dateutil pip install pyparsing pip install matplotlib
(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 on Azure prerequisites for EMOD source code¶
This section describes the software packages or utilities must be installed on computers running CentOS 7.1 on Azure to build the Eradication binary 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. 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 data files)
An Internet connection
Download and run the PrepareLinuxEnvironment.sh script on EMOD releases on GitHub.
Respond to the prompts for information while the script is running. If you choose not to download the EMOD source and input data files, do the following. This example assumes that a directory named IDM is in your home directory and contains the subdirectories EMOD, containing the EMOD source code, and EMOD-InputData, containing the input data files directory.
Set the EMOD_ROOT environment variable to the path to the EMOD source path:
EMOD_ROOT=~/IDM/EMOD
Put Scripts and . in the path:
export PATH=$PATH:.:$EMOD_ROOT/Scripts
Create a symlink from the EMOD directory to InputDataFiles:
ln -s /home/general/IDM/EMOD-InputData $EMOD_ROOT/InputData
If you run simulations using 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 on Azure, all prerequisites for building the Eradication binary are installed by the setup script described above. However, if you originally installed EMOD without including the source code and input data files that are optional for running simulations using a pre-built Eradication binary, 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 and 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, you can type git help git from Git Bash, or git help
<command> for information about a specific command.
Launch the Git Bash application. From the command line:
Go to the location where you want your copy of the EMOD source located:
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.
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 on Azure 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 data files.
Cache the actual data on your local machine:
git lfs fetch
Replace the metadata in the files with the actual contents:
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 CentOS 7.1 on Azure 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)
Vector-borne diseases (VECTOR_SIM)
Malaria (MALARIA_SIM)
Tuberculosis with HIV coinfection (TBHIV_SIM)
Sexually transmitted infections (STI_SIM)
HIV (HIV_SIM)
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 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 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
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 data 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 vector simulations, the following corresponding classes are derived from generic classes:
- SimulationVector
Creates NodeVector objects instead of generic Node objects.
- NodeVector
Creates IndividualHumanVector objects instead of generic IndividualHuman objects and creates and manages VectorPopulation objects to model the mosquito vectors.
- IndividualHumanVector
Represents a human being and provides the additional layer of functionality for how vectors interact with individual humans.
- VectorPopulation
The mosquito species at each node, which can be represented by a collection of cohorts that counts the population of a specific state of mosquitoes (VectorCohort) or by a collection of individual agent mosquitoes (VectorIndividual).
- SusceptibilityVector
Represents a human being’s susceptibility to vector-borne disease.
- InfectionVector
Represents a human being’s infection with a vector-borne disease.
Substitute these classes wherever you see the generic base classes in the architecture documentation.
For malaria simulations, the following corresponding classes are derived from vector classes:
- SimulationMalaria
Creates NodeMalaria objects instead of generic Node objects.
- NodeMalaria
Creates IndividualHumanMalaria objects instead of generic IndividualHuman objects and provides various malaria-specific counters for the purposes of reporting.
- IndividualHumanMalaria
Represents a human being and provides the additional layer of functionality for how malaria vectors interact with individual humans.
- SusceptibilityMalaria
Represents a human being’s susceptibility to malaria. It contains much of the intra-host model, by modeling the specific details of an individual’s immune system in the context of the malaria infection life cycle. It is highly configurable, and interacts closely with InfectionMalaria objects and the IndividualHumanMalaria object, its parent. From a software point of view, SusceptibilityMalaria derives from SusceptibilityVector, but in reality SusceptibilityVector provides minimal epidemiological functionality.
- InfectionMalaria
Represents a human being’s infection with malaria. It is the other part of the detailed intra-host malaria model. It models the progression of the malaria parasite through sporozoite, schizont, hepatocyte, merozoite, and gametocyte stages. InfectionMalaria objects are contained with IndividualMalaria objects. There can be multiple such objects. They all interact closely with the SusceptibilityMalaria object. From a software point of view, InfectionMalaria derives from InfectionVector, but in reality InfectionVector provides minimal epidemiological functionality.
For generic simulations, human-to-human transmission uses a homogeneous contagion pool for each node. Every individual in the node sheds disease into the pool and acquires disease from the pool. For vector-borne diseases, disease transmission is more complex as it must take into account the vector life cycle. See Disease transmission.
The relationship between these classes is captured in the following figure.
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 file structure 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 \(X_i\).
For vector-borne diseases, during an Update() for a node, the infectiousness of vectors is calculated on the local human population regarding the vector life cycle, along with the effects their interventions, such as bednets. Weather data from the Climate object is then used to update the available larval habitat for each local vector species. Multiple local vector species are supported, and after the weather updates, each vector species is advanced through a time step with the VectorPopulation::TimeStep() method. This calculates the life cycle and vector infection dynamics, along with the feeding cycle. It updates the indoor and outdoor bites, and indoor and outdoor infectious bites on the local human population. Each Individual in the local human population is then advanced in an Update(), propagating its infections forward in time, updating its interventions and infectivity, and acquiring any new infections.
NodeVector::updateInfectivity() calls the VectorPopulation::TimeStep() method for mortality adjustments due to weather and the human population and, when completed, processes each list. The effects of the human population are accounted for in VectorPopulation::Update_Host_Effects(), which determines the outcomes for indoor and outdoor attempted feeds on each individual human, and then on the human population as a whole, weighting by any heterogeneous biting. The processing order is:
All infectious female mosquitoes
All infected female mosquitoes
All adult uninfected female mosquitoes
All adult male mosquitoes
Immature mosquitoes
All mosquito larvae
All mosquito eggs
After each list is updated, all indoor and outdoor bites, both infectious and non-infectious, are tallied and updated within the Node infectivity objects.
Within a list update, each cohort of identical-state mosquitoes or each individual-agent mosquito experiences risk of mortality, may progress through any stage of development, such as sporogony (for infected) or larval maturation (for larval), and may attempt a blood feed (for adult females). For mosquitoes attempting a blood feed, outcomes are governed by the calculated host effects, with sequential conditional draws for choice of host type, choice of indoor or outdoor feeding location, and feed outcome. The potential feed outcomes are die before feeding, no host found, die during feeding, die post-feeding, and successful feed. Successful feeds result in ovipositions, and eggs laid start the next generation of mosquitoes.
Campaign management¶
Campaigns are structured into campaign events that determine when, where, and to whom the intervention will be distributed and interventions that determine what is distributed, for example vaccines or other treatments. This section describes the software architecture used for managing campaigns.
Campaigns can be very simple, such as a single intervention with fixed parameters at a point in time, or a complex, adaptive, repetitive, and responsive combination of interventions. A Campaign is a collection of events that modify a simulation. An Event is a distribution within a campaign, and an Intervention is an item that is given out, such as a vaccine, a drug or a bednet.
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 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, 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, 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.
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.
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.
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.
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 data files, binary file, and cluster settings.
For local Windows simulations, set the values under [WINDOWS].
For local CentOS on Azure simulations, set the values under [POSIX]. Note that CentOS on Azure 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.