Optimizing Interventions for Agent-Based Infectious Disease Simulations

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

Figures (9)

• Figure 1: Example intervention tree for the intervention "All schools where at least one pupil is symptomatic have to close".
• Figure 2: Example intervention tree for the intervention "Symptomatic students have to self-isolate, and their school has to close".
• Figure 3: The parse tree for the example intervention tree in Figure \ref{['fig:interventionTree-ExampleCloseSchoolsAndSelfIsolate']}. In the brackets are the rules that are applied to the token above to reach the token below.
• Figure 4: Filtering a set repeatedly with the same filter will repeatedly result in the same set (here, $A_S$). Banning repeating identical filters prevents these semantically pointless interventions.
• Figure 5: Example of how the bans impact the expansion of symbols: Before the rule \ref{['eq:pa']}$\in Q$ for $\textcolor{teal}{p_a?}$ is applied, it is filtered by the bans attached to the previous intervention tree edge. An intervention tree edge corresponds to a parse tree's sub-tree starting with a node labeled with starta and ending before the next node labeled starta. Here, the nodes corresponding to the two intervention tree edges are grouped in the gray bubbles.