NChooserEventCoordinatorSTI¶
The NChooserEventCoordinatorSTI coordinator class distributes an individual-level intervention to exactly N people of a targeted demographic in STI simulations. This contrasts with other event coordinators that distribute an intervention to a percentage of the population, not to an exact count. This event coordinator is similar to the NChooserEventCoordinator for other simulation types, but replaces start and end days with start and end years. See the following JSON example and table, which shows all available parameters for this event coordinator.
Note
Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.
EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.
JSON 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_Year |
float |
1900 |
2200 |
2200 |
The year to stop distributing the intervention. Defines the time period to distribute the intervention along with Start_Year. The intervention is evenly distributed between each time step in the time period. |
{
"Start_Year": 1963,
"End_Year": 1963.5
}
|
Intervention_Config |
JSON object |
NA |
NA |
NA |
The nested JSON of the actual intervention to be distributed by this event coordinator. |
{
"Intervention_Config": {
"class": "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_Year |
float |
1900 |
2200 |
1900 |
The year to start distributing the intervention. Defines the time period to distribute the intervention along with End_Year. The intervention is evenly distributed between each time step in the time period. To have the intervention applied other than at the beginning of the year, you must enter a decimal value after the year. For example, 2010.5 would have the intervention applied halfway through the year 2010. |
{
"Start_Year": 1963,
"End_Year": 1963.5
}
|
{
"Use_Defaults": 1,
"Events": [{
"class": "CampaignEvent",
"Start_Day": 1,
"Nodeset_Config": {
"class": "NodeSetAll"
},
"Event_Coordinator_Config": {
"class": "NChooserEventCoordinatorSTI",
"Distributions": [{
"Start_Year": 1961,
"End_Year": 1961.25,
"Property_Restrictions_Within_Node": [],
"Age_Ranges_Years": [{
"Min": 10,
"Max": 19
}, {
"Min": 40,
"Max": 49
}],
"Num_Targeted": [600000, 300000]
},
{
"Start_Year": 1963,
"End_Year": 1963.5,
"Property_Restrictions_Within_Node": [],
"Age_Ranges_Years": [{
"Min": 20,
"Max": 29
}, {
"Min": 50,
"Max": 59
}],
"Num_Targeted_Males": [400000, 200000],
"Num_Targeted_Females": [300000, 100000]
},
{
"Start_Year": 1965,
"End_Year": 1965.25,
"Property_Restrictions_Within_Node": [],
"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]
}
],
"Intervention_Config": {
"class": "ControlledVaccine",
"Cost_To_Consumer": 10,
"Vaccine_Type": "AcquisitionBlocking",
"Vaccine_Take": 1.0
}
}
}]
}