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Overview of the MIGHTI Simulation Framework

What is MIGHTI?

MIGHTI (Model of Inter-Generational Health, Transmission, and Interventions) is a Python-based agent-based modeling framework designed to simulate the dynamics of health and disease across a population. Built upon the Starsim simulation engine, MIGHTI enables complex interactions between diseases, interventions, social determinants, and individual agent characteristics.

MIGHTI simulates a wide range of health conditions (HCs), including both noncommunicable diseases (NCDs) such as diabetes, cardiovascular disease, and depression, and infectious diseases (IDs) such as HIV, tuberculosis, and HPV.

Core Inputs

MIGHTI requires the following input datasets:

• Demographics: Age distribution, fertility, and mortality (via life tables)
• Disease Parameters: Acquisition, remission, mortality, relative risks, condition-specific attributes
• Intervention Coverage: Time-varying data (e.g., ART scale-up, T2D treatment programs)
• Social Determinants (SDoH): Individual-level states like housing instability, education, and income

Prevalence-matching calibration (e.g., for p_acquire) is used to ensure realistic simulations.

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What Makes MIGHTI Unique?

Structured Disease Interactions

• HIV-HC and HC-HC interactions are supported via connectors
• Interactions adjust susceptibility or mortality dynamically
• Input via interpretable CSV files

Intervention Logic

• Supports screening, treatment, and prevention
• Coverage can be static or time-varying
• Interventions can modify p_acquire, p_death, remission, etc.
• Flexible enough to include SDoH-targeting interventions

Social Determinants of Health (SDoH)

• Modules like HousingSituation, IncomeSituation, and EducationSituation
• Used to drive disparities in access, eligibility, and outcomes

CASM-Based Adherence Disruption

• CASM stands for constellation of alcohol, substance, and mood-related
• Adherence affected by binary flags for 7 CASM conditions:
  • Alcohol use, depression, anxiety, pain, tobacco, opioid, stimulant use
• Uses a multidimensional lookup table of adherence probabilities
• Treatment of CASM conditions can improve adherence via spillover effects

Pathways from interventions to Health Outcomes

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Model Capabilities

• Estimate life expectancy by sex and scenario
• Track incidence, prevalence, and mortality for each disease
• Simulate intervention rollouts and counterfactuals
• Quantify effect of SDoH and adherence disruptors
• Use Starsim’s built-in calibration tools for prevalence fitting

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Wiki Pages for MIGHTI

• Getting Started Installation, dependencies, and how to run your first simulation.

• Parameter Preparation Guide to preparing and formatting inputs such as p_death, dur_condition, and demographic files.

• Health Conditions Module Overview of how diseases (e.g., HIV, diabetes, cancer) are modeled in disease directry.

• Interactions Module Overview of how HC-HC and HC-HIV interactions are modeled in interaction.py.

• CASM Conditions and Adherence Modeling adherence disrupters like depression, alcohol, tobacco, etc. via adherence.py.

• SDoH Module

• Interventions Module How interventions (e.g., ART, PrEP, lifestyle changes) are defined and targeted in interventions.py and each health condition class.

• Analyzers

• Calibration Steps for fitting acquisition probabilities to prevalence data via Starsim’s calibration tools.

• Simulation Examples End-to-end run of mighti_main.py, life expectancy estimation, and sample results.

• Microcosting and Cost-Effectiveness Analysis