DelayedIntervention#

The DelayedIntervention intervention class introduces a delay between when the intervention is distributed to the individual and when they receive the actual intervention. This is due to the frequent occurrences of time delays as individuals seek care and receive treatment. This intervention allows configuration of the distribution type for the delay as well as the fraction of the population that receives the specified intervention.

Note

Parameters are case-sensitive. For Boolean parameters, set to 1 for true or 0 for false. Minimum, maximum, or default values of “NA” indicate that those values are not applicable for that parameter.

EMOD does not use true defaults; that is, if the dependency relationships indicate that a parameter is required, you must supply a value for it. However, many of the tools used to work with EMOD will use the default values provided below.

JSON format does not permit comments, but you can add “dummy” parameters to add contextual information to your files. Any keys that are not EMOD parameter names will be ignored by the model.

The table below describes all possible parameters with which this class can be configured. The JSON example that follows shows one potential configuration.

Parameter

Data type

Minimum

Maximum

Default

Description

Example

Actual_IndividualIntervention_Configs

array of json objects

NA

NA

NA

An array of nested interventions to be distributed at the end of a delay period, to covered fraction of the population.

 
{
  "Actual_IndividualIntervention_Configs": [
    {
      "Base_Sensitivity": 1,
      "Base_Specificity": 1,
      "Negative_Diagnosis_Event": "HIVNeedsMaleCircumcision",
      "New_Property_Value": "InterventionStatus:None",
      "Positive_Diagnosis_Event": "NoTreatment",
      "class": "HIVSimpleDiagnostic"
    }
  ]
}

Coverage

float

0

1

1

The proportion of individuals who receive the DelayedIntervention that actually receive the configured interventions. This can be useful to target the intervention to a subset of the people that got the delay. To target the subgroup more specifically, distribute a BroadcastEvent and use a NodeLevelHealthTriggeredIV to distributes the desired intervention.

 
{
  "Coverage": 1.0
}

Delay_Period_Constant

float

0

3.40282E+38

-1

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

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

Delay_Period_Distribution

enum

NA

NA

NOT_INITIALIZED

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

Possible values are:

NOT_INITIALIZED

No distribution set.

CONSTANT_DISTRIBUTION

Use the same value for each individual. Set Delay_Period_Constant.

UNIFORM_DISTRIBUTION

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

GAUSSIAN_DISTRIBUTION

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

EXPONENTIAL_DISTRIBUTION

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

WEIBULL_DISTRIBUTION

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

LOG_NORMAL_DISTRIBUTION

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

POISSON_DISTRIBUTION

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

DUAL_CONSTANT_DISTRIBUTION

Use a distribution where some individuals are set to a value of zero and the rest to a given value. Set Delay_Period_Proportion_0 and Peak_2_Value.

DUAL_EXPONENTIAL_DISTRIBUTION

Use two exponential distributions with given means. Set Delay_Period_Mean_1, Delay_Period_Mean_2, and Delay_Period_Proportion_1.

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

Delay_Period_Exponential

float

0

3.40282E+38

-1

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

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

Delay_Period_Gaussian_Mean

float

0

3.40282E+38

-1

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

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

Delay_Period_Gaussian_Std_Dev

float

1.17549E-38

3.40282E+38

-1

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

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

Delay_Period_Kappa

float

1.17549E-38

3.40282E+38

-1

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

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

Delay_Period_Lambda

float

1.17549E-38

3.40282E+38

-1

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

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

Delay_Period_Log_Normal_Mu

float

-3.40282e+38

1.70141e+38

3.40282e+38

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

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

Delay_Period_Log_Normal_Sigma

float

-3.40282e+38

1.70141e+38

3.40282E+38

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

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

Delay_Period_Max

float

0

3.40282E+38

-1

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

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

Delay_Period_Mean_1

float

1.17549E-38

3.40282E+38

-1

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

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

Delay_Period_Mean_2

float

1.17549E-38

3.40282E+38

-1

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

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

Delay_Period_Min

float

0

3.40282E+38

-1

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

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

Delay_Period_Peak_2_Value

float

0

3.40282E+38

-1

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

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

Delay_Period_Poisson_Mean

float

0

3.40282E+38

-1

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

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

Delay_Period_Proportion_0

float

0

1

-1

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

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

Delay_Period_Proportion_1

float

0

1

-1

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

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

Disqualifying_Properties

array of strings

NA

NA

[]

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

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

Dont_Allow_Duplicates

boolean

0

1

0

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

 
{
  "Dont_Allow_Duplicates": 0
}

Intervention_Name

string

NA

NA

DelayedIntervention

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

 
{
  "Intervention_Config": {
    "class": "DelayedIntervention",
    "Intervention_Name": "Treatment one week after diagnostic test"
  }
}

New_Property_Value

string

NA

NA

“”

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

 
{
  "New_Property_Value": "InterventionStatus:None"
}

{
  "Campaign_Name": "Initial Seeding",
  "Events": [
    {
      "Event_Name": "Outbreak",
      "class": "CampaignEvent",
      "Nodeset_Config": {
        "class": "NodeSetAll"
      },
      "Start_Day": 1,
      "Event_Coordinator_Config": {
        "class": "StandardInterventionDistributionEventCoordinator",
        "Target_Demographic": "Everyone",
        "Demographic_Coverage": 1.0,
        "Intervention_Config": {
          "class": "DelayedIntervention",
          "Delay_Period_Distribution": "CONSTANT_DISTRIBUTION",
          "Delay_Period_Constant": 25,
          "Actual_IndividualIntervention_Configs": [
            {
              "Outbreak_Source": "PrevalenceIncrease",
              "class": "OutbreakIndividual"
            }
          ]
        }
      }
    },
    {
      "Event_Name": "Outbreak",
      "class": "CampaignEvent",
      "Nodeset_Config": {
        "class": "NodeSetAll"
      },
      "Start_Day": 50,
      "Event_Coordinator_Config": {
        "class": "StandardInterventionDistributionEventCoordinator",
        "Target_Demographic": "Everyone",
        "Demographic_Coverage": 1.0,
        "Intervention_Config": {
          "class": "DelayedIntervention",
          "Delay_Period_Distribution": "UNIFORM_DISTRIBUTION",
          "Delay_Period_Min": 15,
          "Delay_Period_Max": 30,
          "Actual_IndividualIntervention_Configs": [
            {
              "Outbreak_Source": "PrevalenceIncrease",
              "class": "OutbreakIndividual"
            }
          ]
        }
      }
    }
  ],
  "Use_Defaults": 1
}