🤖 AI Summary
This work addresses the challenge of manually optimizing non-pharmaceutical intervention (NPI) strategies due to the vast combinatorial space of possible measures. To overcome this, the authors propose ADIOS, a system that automatically searches for highly effective NPI policies with minimal societal disruption. ADIOS integrates agent-based epidemic simulation with grammar-guided genetic programming (GGGP) and introduces a domain-specific language (DSL) tailored for NPIs. This DSL structures the policy space using a context-free grammar and incorporates semantic constraints to eliminate infeasible strategies, substantially improving search efficiency. Experiments on GEMS, a high-resolution microsimulation platform for epidemics in Germany, demonstrate that ADIOS efficiently discovers near-optimal intervention strategies applicable to complex real-world scenarios.
📝 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