Interventions

This page provides an overview to the available interventions and their implementation in each model as used in MultiMalModPy.
The function and implementation/use in the framework can be found in the API, specifically the intervention.py script for each model.

⚠️NOTE: For latest and complete documentation, please visit the documentation sites from the maintaining institutions under the provided links.

Other interventions than case management are not part of the initial release and will be added in near future.

Case management

Each model simulates case management differently and has a different definition of a malaria case.

🚩 Model alignment and implementation in MultiMalModPy:
Box is checked if aligned

✗ Mode of action The level of detail varies across the models which cannot be aligned
✗ Case definition The case definition for each model has been checked and defaults for each model were kept.
✔ Drug used All models use ACT as the first line treatment for uncomplicated malaria.
✔ Coverage Treatment coverage is implemented differently in each model and was aligned to correspond to a 14 day coverage.
✔ Target population Whole population targeted and treated equally, i.e. no distinction by age.
✔ Resistance Drug resistance not included.
✔ Deployment time and duration Intervention starts from the beginning of the simulation and stays constant throughout.

EMOD

treatment_seeking 

def add_treatment_seeking(campaign,
                          start_day: int = 1,
                          targets: list = None,
                          drug: list = None,
                          node_ids: list = None,
                          ind_property_restrictions: list = None,
                          drug_ineligibility_duration: float = 0,
                          duration: int = -1,
                          broadcast_event_name: str = 'Received_Treatment'):
    camp_events = _get_events(campaign=campaign, start_day=start_day, targets=targets, drug=drug, node_ids=node_ids,
                              ind_property_restrictions=ind_property_restrictions,
                              drug_ineligibility_duration=drug_ineligibility_duration, duration=duration,
                              broadcast_event_name=broadcast_event_name)
    for event in camp_events:
        campaign.add(event)

 targets is a list of dictionaries defining the trigger event and coverage for and
    properties of individuals to target with the intervention with all possible options being:
    [{"trigger":"NewClinicalCase","coverage":0.8,"agemin":15,"agemax":70, "rate":0.3}]
    "rate" is the inverse of the average delay in time to treatment seeking from an exponential distribution
    "trigger" must be defined, but everything else has defaults:
    coverage = 1, affects all
    agemin/agemax = 0/125, affects all
    rate = 0, no delay, seek treatment immediately
json snippet (example) 

{
  "class": "CampaignEvent",
  "Event_Coordinator_Config": {
    "class": "StandardInterventionDistributionEventCoordinator",
    "Demographic_Coverage": 1,
    "Individual_Selection_Type": "DEMOGRAPHIC_COVERAGE",
    "Intervention_Config": {
      "class": "NodeLevelHealthTriggeredIV",
      "Actual_IndividualIntervention_Config": {
        "class": "DelayedIntervention",
        "Actual_IndividualIntervention_Configs": [
          {
            "class": "AntimalarialDrug",
            "Cost_To_Consumer": 0,
            "Disqualifying_Properties": [],
            "Dont_Allow_Duplicates": 0,
            "Drug_Type": "Artemether",
            "Intervention_Name": "AntimalarialDrug_Artemether",
            "New_Property_Value": ""
          },
          {
            "class": "AntimalarialDrug",
            "Cost_To_Consumer": 0,
            "Disqualifying_Properties": [],
            "Dont_Allow_Duplicates": 0,
            "Drug_Type": "Lumefantrine",
            "Intervention_Name": "AntimalarialDrug_Lumefantrine",
            "New_Property_Value": ""
          },
          {
            "class": "BroadcastEvent",
            "Broadcast_Event": "Received_Treatment",
            "Disqualifying_Properties": [],
            "Dont_Allow_Duplicates": 0,
            "Intervention_Name": "BroadcastEvent",
            "New_Property_Value": ""
          }
        ],
        "Coverage": 1,
        "Delay_Period_Distribution": "EXPONENTIAL_DISTRIBUTION",
        "Delay_Period_Exponential": 3.3333333333333335,
        "Disqualifying_Properties": [],
        "Dont_Allow_Duplicates": 0,
        "Intervention_Name": "DelayedIntervention",
        "New_Property_Value": ""
      },
      "Blackout_Event_Trigger": "",
      "Blackout_On_First_Occurrence": 0,
      "Blackout_Period": 0,
      "Demographic_Coverage": 0.0,
      "Disqualifying_Properties": [],
      "Distribute_On_Return_Home": 0,
      "Dont_Allow_Duplicates": 0,
      "Duration": -1,
      "Intervention_Name": "NodeLevelHealthTriggeredIV",
      "New_Property_Value": "",
      "Node_Property_Restrictions": [],
      "Property_Restrictions": [],
      "Target_Age_Max": 115,
      "Target_Age_Min": 0,
      "Target_Demographic": "ExplicitAgeRanges",
      "Target_Gender": "All",
      "Target_Residents_Only": 0,
      "Targeting_Config": {
        "HasIP": {},
        "HasIntervention": {},
        "IsPregnant": {},
        "TargetingLogic": {}
      },
      "Trigger_Condition_List": [
        "NewClinicalCase"
      ]
    },
    "Node_Property_Restrictions": [],
    "Number_Repetitions": 1,
    "Property_Restrictions": [],
    "Property_Restrictions_Within_Node": [],
    "Target_Demographic": "Everyone",
    "Target_Gender": "All",
    "Target_Residents_Only": 0,
    "Targeting_Config": {
      "HasIP": {},
      "HasIntervention": {},
      "IsPregnant": {},
      "TargetingLogic": {}
    },
    "Timesteps_Between_Repetitions": -1
  },
  "Nodeset_Config": {
    "class": "NodeSetAll"
  },
  "Start_Day": 1
}


malariasimulation

set_clinical_treatment 

set_clinical_treatment <- function(parameters, drug, timesteps, coverages) {
  stopifnot(all(coverages >= 0) && all(coverages <= 1))
  n_drugs <- length(parameters$drug_efficacy)
  if (drug < 1 | drug > n_drugs) {
    stop('Drug index is invalid, please set drugs using set_drugs')
  }
  drug_index <- which(parameters$clinical_treatment_drugs == drug)
  if (length(drug_index) == 0) {
    drug_index <- length(parameters$clinical_treatment_drugs) + 1
  }
  parameters$clinical_treatment_drugs[[drug_index]] <- drug
  parameters$clinical_treatment_timesteps[[drug_index]] <- timesteps
  parameters$clinical_treatment_coverages[[drug_index]] <- coverages
  last_timestep <- max(unlist(parameters$clinical_treatment_timesteps))

  for (t in seq(last_timestep)) {
    if (sum(get_treatment_coverages(parameters, t)) > 1) {
      stop('The sum of drug coverages cannot be greater than 1 at any timestep')
    }
  }
  parameters
}


OpenMalaria

ImmediateOutcomes

⚠️ NOTE: Multiple options for simulating 'case management' exist. Below is the implementation as used in MultiMalModPy. 
<ImmediateOutcomes name="example name">
    <drugRegimen firstLine="ACT" inpatient="QN" secondLine="ACT"/>
    <initialACR>
        <ACT value="1.0"/>
        <QN value="1.0"/>
        <selfTreatment value="0.0"/>
    </initialACR>
    <compliance>
        <ACT value="1.00"/>
        <selfTreatment value="1.0"/>
    </compliance>
    <nonCompliersEffective>
        <ACT value="0"/>
        <selfTreatment value="0"/>
    </nonCompliersEffective>
    <treatmentActions> <!-- only from schema 32 -->
        <ACT name="clear blood-stage infections">
            <clearInfections stage="blood" timesteps="1"/>
        </ACT>
        <QN name="clear blood-stage infections">
            <clearInfections stage="blood" timesteps="1"/>
        </QN>
    </treatmentActions>
    <pSeekOfficialCareUncomplicated1 value="0.04"/>
    <pSelfTreatUncomplicated value="0.0"/>
    <pSeekOfficialCareUncomplicated2 value="0.04"/>
    <pSeekOfficialCareSevere value="0.48"/>
</ImmediateOutcomes>