What’s new

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

EMOD v2.13

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

New configuration parameters

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

Parameter Data type Minimum Maximum Default Description Example
Custom_Reports_Filename string NA NA UNINITIALIZED STRING The name of the file containing custom report configuration parameters. Omitting this parameter or setting it to RunAllCustomReports will load all reporters found that are valid for the given simulation type. The file must be in JSON format.
{  
    "Custom_Reports_Filename": "custom_reports.json"
}
Cycle_Arrhenius_1 float 0 1.00E+15 4.09E+10 The Arrhenius equation, a_1^{-a_2/T}, with T in degrees Kelvin, parameterizes the mosquito feeding cycle rate. This duration is a decreasing function of temperature. The variable a1 is a temperature-independent scale factor on feeding rate. Temperature_Dependent_Feeding_Cycle must be set to ARRHENIUS_DEPENDENCE.
{
    "Temperature_Dependent_Feeding_Cycle": "ARRHENIUS_DEPENDENCE",
    "Vector_Species_Params": {
        "arabiensis": {
            "Cycle_Arrhenius_1": 99,
            "Cycle_Arrhenius_2": 88
        }
    }
}
Cycle_Arrhenius_2 float 0 1.00E+15 7740 The Arrhenius equation, a_1^{-a_2/T}, with T in degrees Kelvin, parameterizes the mosquito feeding cycle rate. This duration is a decreasing function of temperature. The variable a2 is a temperature-independent scale factor on feeding rate. Temperature_Dependent_Feeding_Cycle must be set to ARRHENIUS_DEPENDENCE.
{
    "Temperature_Dependent_Feeding_Cycle": "ARRHENIUS_DEPENDENCE",
    "Vector_Species_Params": {
        "arabiensis": {
            "Cycle_Arrhenius_1": 99,
            "Cycle_Arrhenius_2": 88
        }
    }
}
Cycle_Arrhenius_Reduction_Factor float 0 1 1 The scale factor applied to cycle duration (from oviposition to oviposition) to reduce the duration when primary follicles are at stage II rather than I in the case of newly emerged females. Temperature_Dependent_Feeding_Cycle must be set to ARRHENIUS_DEPENDENCE.
{
    "Temperature_Dependent_Feeding_Cycle": "ARRHENIUS_DEPENDENCE",
    "Vector_Species_Params": {
        "funestus": {
            "Cycle_Arrhenius_Reduction_Factor": 0.44
        }
    }
}
Drought_Egg_Hatch_Delay float 0 1 0.33 Proportion of regular egg hatching that still occurs when habitat dries up. Enable_Drought_Egg_Hatch_Delay must be set to 1.
{ 
    "Enable_Drought_Egg_Hatch_Delay": 1,
    "Drought_Egg_Hatch_Delay": 0.33
}
Egg_Arrhenius1 float 0 1.00E+10 6.16E+07 The Arrhenius equation, math:a_1^{-a_2/T}, with T in degrees Kelvin, parameterizes the daily rate of mosquito egg hatching. This duration is a decreasing function of temperature. The variable a1 is a temperature-independent scale factor on hatching rate. Enable_Temperature_Dependent_Egg_Hatching must be set to 1.
{
    "Enable_Temperature_Dependent_Egg_Hatching": 1,     
    "Egg_Arrhenius1": 61599956,
    "Egg_Arrhenius2": 5754
}
Egg_Arrhenius2 float 0 1.00E+10 5754.03 The Arrhenius equation, a_1^{-a_2/T}, with T in degrees Kelvin, parameterizes the daily rate of mosquito egg hatching. This duration is a decreasing function of temperature. The variable a2 is a temperature-dependent scale factor on hatching rate. Enable_Temperature_Dependent_Egg_Hatching must be set to 1.
{
    "Enable_Temperature_Dependent_Egg_Hatching": 1,     
    "Egg_Arrhenius1": 61599956,
    "Egg_Arrhenius2": 5754
}
Egg_Hatch_Density_Dependence enum NA NA NO_DENSITY_DEPENDENCE

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

DENSITY_DEPENDENCE
Egg hatching is reduced when the habitat is nearing its carrying capacity.
NO_DENSITY_DEPENDENCE
Egg hatching is not dependent upon larval density.
{
    "Egg_Hatch_Density_Dependence": "NO_DENSITY_DEPENDENCE"
}
Enable_Drought_Egg_Hatch_Delay boolean 0 1 0 Controls whether or not eggs hatch at delayed rates, set by Drought_Egg_Hatch_Delay, when the habitat dries up completely.
{
    "Enable_Drought_Egg_Hatch_Delay": 1, 
    "Drought_Egg_Hatch_Delay": 0.33
}
Enable_Egg_Mortality boolean 0 1 0 Controls whether or not to include a daily mortality rate on the egg population, which is independent of climatic factors.
{
    "Enable_Egg_Mortality": 1,
}
Enable_Temperature_Dependent_Egg_Hatching boolean 0 1 0 Controls whether or not temperature has an effect on egg hatching, defined by Egg_Arrhenius_1 and Egg_Arrhenius_2.
{  
    "Enable_Temperature_Dependent_Egg_Hatching": 1,  
    "Egg_Arrhenius1": 61599956.864, 
    "Egg_Arrhenius2": 5754.033
}
Enable_Vector_Aging boolean 0 1 0 Controls whether or not vectors undergo senescence as they age.
{
    "Enable_Vector_Aging": 1
}
Incubation_Period_Log_Mean float 0 3.40E+38 6 The mean of log normal for the incubation period distribution. Incubation_Period_Distribution must be set to LOG_NORMAL_DURATION.
{
    "Incubation_Period_Distribution": "LOG_NORMAL_DURATION",
    "Incubation_Period_Log_Mean": 5.758,
    "Incubation_Period_Log_Width": 0.27
}
Incubation_Period_Log_Width float 0 3.40E+38 1 The log width of log normal for the incubation period distribution. Incubation_Period_Distribution must be set to LOG_NORMAL_DURATION.
{
    "Incubation_Period_Distribution": "LOG_NORMAL_DURATION",
    "Incubation_Period_Log_Mean": 5.758,
    "Incubation_Period_Log_Width": 0.27
}
Infectivity_Exponential_Baseline float 0 1 0 The scale factor applied to Base_Infectivity at the beginning of a simulation, before the infectivity begins to grow exponentially. Infectivity_Scale_Type must be set to EXPONENTIAL_FUNCTION_OF_TIME.
{
    "Infectivity_Exponential_Baseline": 0.1, 
    "Infectivity_Exponential_Delay": 90, 
    "Infectivity_Exponential_Rate": 45, 
    "Infectivity_Scale_Type": "EXPONENTIAL_FUNCTION_OF_TIME"
}
Infectivity_Exponential_Delay float 0 3.40E+38 0 The number of days before infectivity begins to ramp up exponentially. Infectivity_Scale_Type must be set to EXPONENTIAL_FUNCTION_OF_TIME.
{
    "Infectivity_Exponential_Baseline": 0.1, 
    "Infectivity_Exponential_Delay": 90, 
    "Infectivity_Exponential_Rate": 45, 
    "Infectivity_Scale_Type": "EXPONENTIAL_FUNCTION_OF_TIME"
}
Infectivity_Exponential_Rate float 0 3.40E+38 0 The daily rate of exponential growth to approach to full infectivity after the delay set by Infectivity_Exponential_Delay has passed. Infectivity_Scale_Type must be set to EXPONENTIAL_FUNCTION_OF_TIME.
{
    "Infectivity_Exponential_Rate": 45
}
Memory_Usage_Halting_Threshold_Working_Set_MB integer 0 1.00E+06 8000 The maximum size (in MB) of working set memory before the system throws an exception and halts.
{
    "Memory_Usage_Halting_Threshold_Working_Set_MB": 4000
}
Memory_Usage_Warning_Threshold_Working_Set_MB integer 0 1.00E+06 7000 The maximum size (in MB) of working set memory before memory usage statistics are written to the log regardless of log level.
{
    "Memory_Usage_Warning_Threshold_Working_Set_MB": 3500
}
Nighttime_Feeding_Fraction float 0 1 1 The fraction of feeds on humans (for a specific mosquito species) that occur during the nighttime. Thus the fraction of feeds on humans that occur during the day equals 1 - (value of this parameter).
{
    "Vector_Species_Params": {
        "arabiensis": {
            "Acquire_Modifier": 0.2, 
            "Adult_Life_Expectancy": 10, 
            "Anthropophily": 0.95, 
            "Aquatic_Arrhenius_1": 84200000000, 
            "Aquatic_Arrhenius_2": 8328, 
            "Aquatic_Mortality_Rate": 0.1, 
            "Cycle_Arrhenius_1": 0, 
            "Cycle_Arrhenius_2": 0, 
            "Cycle_Arrhenius_Reduction_Factor": 0, 
            "Days_Between_Feeds": 3, 
            "Egg_Batch_Size": 100, 
            "Immature_Duration": 4, 
            "Indoor_Feeding_Fraction": 0.5, 
            "Infected_Arrhenius_1": 117000000000, 
            "Infected_Arrhenius_2": 8336, 
            "Infected_Egg_Batch_Factor": 0.8, 
            "Infectious_Human_Feed_Mortality_Factor": 1.5, 
            "Larval_Habitat_Types": {
                "TEMPORARY_RAINFALL": 11250000000
            }, 
            "Nighttime_Feeding_Fraction": 1, 
            "Transmission_Rate": 0.5
        }
    }
}   
Serialization_Time_Steps array of integers 0 2.15E+09   The list of time steps after which to save the serialized state. 0 (zero) indicates the initial state before simulation, n indicates after the nth time step. By default, no serialized state is saved.
{
    "Serialization_Time_Steps": [
        0, 
        10
    ]
}
Serialized_Population_Filenames array of strings NA NA NA Array of filenames with serialized population data. The number of filenames must match the number of cores used for the simulation. The file must be in .dtk format.
{
    "Serialized_Population_Filenames": [
        "state-00010.dtk"
    ]
}

Serialized_Population_Path string NA NA . The root path for the serialized population files.
{
    "Serialized_Population_Path": "../00_Generic_Version_1_save/output"
}

Temperature_Dependent_Feeding_Cycle enum NA NA NO_TEMPERATURE_DEPENDENCE

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

NO_TEMPERATURE_DEPENDENCE
No relation to temperature; days between feeds will be constant and specified by Days_Between_Feeds for each species.
ARRHENIUS_DEPENDENCE
Use the Arrhenius equation with parameters Cycle_Arrhenius_1 and Cycle_Arrhenius_2.
BOUNDED_DEPENDENCE
Use an equation bounded at 10 days at 15C and use Days_Between_Feeds to set the duration at 30C.
{
    "Temperature_Dependent_Feeding_Cycle": "BOUNDED_DEPENDENCE"
}

To view all available configuration parameters, see Configuration parameters.

New demographics parameters

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

For more information, see the table below.

Parameter Data type Minimum Maximum Default Description Example
NodeProperties array of objects NA NA NA An array that contains parameters that add properties to nodes in a simulation. For example, you can define values for intervention status, risk, and other properties and assign values to different nodes.
{
    "NodeProperties": [{
        "Property": "Risk",
        "Values": ["HIGH", "MEDIUM", "LOW"],
        "Initial_Distribution": [0.1, 0.4, 0.5]
    }]
}
Property enum NA NA NA

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

Age_Bin
Assign individuals to age bins. Use with Age_Bin_Edges_In_Years. Cannot be used with NodeProperties.
Accessibility
Tag individuals or nodes based on their accessibility.
Geographic
Tag individuals or nodes based on geographic characteristics.
HasActiveTB
Tag individuals or nodes based on active TB status. Typically used only with HIV ART staging interventions.
InterventionStatus
Tag individuals or nodes based on intervention status, so that receiving an intervention can affect how other interventions are distributed. Use with Disqualifying_Properties and New_Property_Value in the campaign file.
Place
Tag individuals or nodes based on place.
Risk
Tag individuals or nodes based on disease risk.
QualityofCare
Tag individuals or nodes based on the quality of medical care.
{
    "Defaults": {
        "IndividualProperties": [{
            "Property": "Age_Bin",
            "Age_Bin_Edges_In_Years": [ 0, 6, 10, 20, -1 ]
        }]
    }
}
{
    "NodeProperties": [{
        "Property": "InterventionStatus",
        "Values": ["NONE", "RECENT_SPRAY"],
        "Initial_Distribution": [1.0, 0.0]
    }]
}

New campaign parameters

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

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

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

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

CommunityHealthWorkerEventCoordinator

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

Parameter Data type Minimum Maximum Default Description Example
Amount_In_Shipment integer 1 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
}
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:

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.
{
    "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
}
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
}
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 individual-level intervention. See NodeProperties and IndividualProperties parameters for more information.
{
    "Property_Restrictions": [
        "Risk:HIGH"
    ]
}
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.
{
    "Node_Property_Restrictions": [{
            "Place": "URBAN",
            "Risk": "MED"
        },
        {
            "Place": "RURAL",
            "Risk": "LOW"
        }
    ]
}
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:

  • Everyone
  • ExplicitAgeRanges
  • ExplicitAgeRangesAndGender
  • ExplicitGender
  • PossibleMothers
  • ArrivingAirTravellers
  • DepartingAirTravellers
  • ArrivingRoadTravellers
  • DepartingRoadTravellers
  • AllArrivingTravellers
  • AllDepartingTravellers
  • ExplicitDiseaseState
{
    "Target_Age_Max": 20,
    "Target_Age_Min": 10,
    "Target_Demographic": "ExplicitAgeRanges"
}
Target_Gender enum NA NA All

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

  • Male
  • Female
  • All
{
    "Target_Gender": "Male"
}
Demographic_Coverage float 0 1 1 The fraction of individuals in the target demographic that will receive this intervention.
{
    "Demographic_Coverage": 1
}
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 node-level intervention. See NodeProperties and IndividualProperties parameters for more information.
{
    "Property_Restrictions_Within_Node": [{
        "Risk": "HIGH"
    }]
}
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
}

ImportPressure

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

Parameter Data type Minimum Maximum Default Description Example
Daily_Import_Pressures array of floats 0 3.40E+3 0 The rate of per-day importation for each node that the intervention is distributed to.
{
    "Intervention_Config": {
        "Antigen": 0,
        "Genome": 0,
        "Durations": [100, 100, 100, 100, 100, 100, 100],
        "Daily_Import_Pressures": [0.1, 5.0, 0.2, 1.0, 2.0, 0.0, 10.0],
        "class": "ImportPressure"
    }
}
Durations array of integers 0 2.15E+0 1 The durations over which to apply import pressure.
{
    "Intervention_Config": {
        "Antigen": 0,
        "Genome": 0,
        "Durations": [100, 100, 100, 100, 100, 100, 100],
        "Daily_Import_Pressures": [0.1, 5.0, 0.2, 1.0, 2.0, 0.0, 10.0],
        "class": "ImportPressure"
    }
}
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"
    }
}
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
}
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
    }
}

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
}
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
}
Cost_To_Consumer float 0 999999 10 The unit cost per vaccine (unamortized).
{
    "Cost_To_Consumer": 10.0
}

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"
    }
}
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"
    }
}
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"
    }
}
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
}
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
}
Cost_To_Consumer float 0 999999 10 The unit cost per vaccine (unamortized).
{
    "Cost_To_Consumer": 10.0
}
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
        }
    }
}
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
}
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"
}

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
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"
}
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
    }
}
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
    }
}
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
    }
}
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"
}

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"
    }
}
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"
    }
}
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
        }
    }
}
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"
    }
}
Vaccine_Type enum NA NA Generic

The type of vaccine to distribute in a vaccine intervention. Possible values are:

Generic
The vaccine can reduce transmission, acquisition, and mortality.
TransmissionBlocking
The vaccine will reduce pathogen transmission.
AcquisitionBlocking
The vaccine will reduce the acquisition of the pathogen by reducing the force of infection experienced by the vaccinated individual.
MortalityBlocking
The vaccine reduces the disease-mortality rate of a vaccinated individual.
{
    "Intervention_Config": {
        "class": "SimpleVaccine",
        "Cost_To_Consumer": 10,
        "Vaccine_Type": "AcquisitionBlocking",
        "Vaccine_Take": 1,
        "Efficacy_Is_Multiplicative": 0,
        "Waning_Config": {
            "class": "WaningEffectConstant",
            "Initial_Effect": 0.3
        }
    }
}
Cost_To_Consumer float 0 999999 10 The unit cost per vaccine (unamortized).
{
    "Cost_To_Consumer": 10.0
}
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
        }
    }
}
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
}
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"
}

UsageDependentBednet

The UsageDependentBednet intervention class is similar to SimpleBednet, as it distributes insecticide-treated nets to individuals in the simulation. However, bednet ownership and bednet usage are distinct in this intervention. As in SimpleBednet, net ownership is configured through the demographic coverage, and the blocking and killing rates of mosquitoes are time-dependent. Use of bednets is age-dependent and can vary seasonally. Once a net has been distributed to someone, the net usage is determined by the product of the seasonal and age-dependent usage probabilities until the net-retention counter runs out, and the net is discarded.

Parameter Data type Minimum Maximum Default Description Example
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
}
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.
{
    "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_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 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_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
}
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"
}
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
}
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"
    }
}
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
}
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"
}

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 Prerequisites for working with EMOD source code.

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.