Optimizing Interventions for Agent-Based Infectious Disease Simulations

TL;DR

This paper introduces ADIOS, a system that uses grammar-guided genetic programming to optimize non-pharmaceutical interventions in agent-based infectious disease models, aiming to find effective strategies with minimal societal impact.

Contribution

The paper presents a novel domain-specific language and an optimization framework for automatically designing effective NPIs in agent-based epidemiological simulations.

Findings

ADIOS successfully generates optimized intervention strategies in a case study with GEMS.

The system reduces the search space by applying constraints to prevent invalid interventions.

Demonstrates the potential to support decision-makers in epidemic control.

Abstract

Non-pharmaceutical interventions (NPIs) are commonly used tools for controlling infectious disease transmission when pharmaceutical options are unavailable. Yet, identifying effective interventions that minimize societal disruption remains challenging. Agent-based simulation is a popular tool for analyzing the impact of possible interventions in epidemiology. However, automatically optimizing NPIs using agent-based simulations poses a complex problem because, in agent-based epidemiological models, interventions can target individuals based on multiple attributes, affect hierarchical group structures (e.g., schools, workplaces, and families), and be combined arbitrarily, resulting in a very large or even infinite search space. We aim to support decision-makers with our Agent-based Infectious Disease Intervention Optimization System (ADIOS) that optimizes NPIs for infectious disease simulations using Grammar-Guided Genetic Programming (GGGP). The core of ADIOS is a domain-specific language for expressing NPIs in agent-based simulations that structures the intervention search space through a context-free grammar. To make optimization more efficient, the search space can be further reduced by defining constraints that prevent the generation of semantically invalid intervention patterns. Using this constrained language and an interface that enables coupling with agent-based simulations, ADIOS adopts the GGGP approach for simulation-based optimization. Using the German Epidemic Micro-Simulation System (GEMS) as a case study, we demonstrate the potential of our approach to generate optimal interventions for realistic epidemiological models