Add New Interventions

This page explains how to create new interventions in MultiMalModPy.
If you’re looking to run simulations using existing interventions, see the User Guide – Interventions.

❗ Adding an intervention using **model-specific defaults** from different models requires **careful implementation**, **testing**, and **alignment** to ensure results remain comparable across models.

Intervention Structure

Interventions are modular and located under:

interventions/
│
├── init.py
├── ccstep.py
└── smc.py # Placeholder, not currently supported
└── .py # Any new interventions user may add

To activate interventions, import them in your experiment setup:

from interventions.ccstep import *
# from interventions.smc import *  # Currently not supported

How to add a new intervention (e.g., SMC)

Adding a new intervention involves:

  1. Create a file: interventions/smc.py

  2. Implement intervention logic for each of the models:

    • EMOD
    • EMOD/functions/interventions.py: Add or update functions that define EMOD-specific intervention parameters and how they are injected into the configuration or campaign builder.
    • malariasimulation
    • malariasimulation/functions/interventions.R: Add functions that define R-based intervention parameters.
    • OpenMalaria
    • OpenMalaria/functions/interventions.py: Define logic for what XML fragment(s) need to be added for the new intervention.
    • OpenMalaria/snippets.py: Add or update reusable XML chunks or template snippets for the new intervention.
    • OpenMalaria/scaffolds.py: Modify or extend XML scaffold generation to insert placeholders or dynamically fill intervention-specific tags.

  3. Define these functions: def smc_params(exp): ... def smc_columns(exp, df): ... def smc_input_check(exp): ...

  4. Update the dispatchers: Edit these functions (usually found in your experiment setup or helper file):

  5. exp_params_to_update: Reads exp.intervention_list and adds parameters for each supported intervention

  6. scenario_df_to_update: Modifies the scenario dataframe (e.g., removes duplicates, adds columns)
  7. intervention_params_to_check: Validates experiment parameters for each active intervention
    # In exp_params_to_update
    if 'smc' in exp.intervention_list:
        exp = smc_params(exp)

    # In scenario_df_to_update
    if 'smc' in exp.intervention_list:
        df = smc_columns(exp, df)

    # In intervention_params_to_check
    if 'smc' in exp.intervention_list:
        smc_input_check(exp)

  1. Consider intervention specific custom reporter or plotters.

  2. Test (example):

 python launch_sim.py --models EMOD --interventions smc --test