EpiPredict: Agent-Based Modeling of Infectious Diseases

Suer, Janik; Ponge, Johannes; Hellingrath, Bernd

Abstract

The COVID-19 pandemic highlighted the impact of emerging infectious diseases on various aspects of public life. Decision-makers in the public-health sector faced the challenge of selecting effective countermeasures for a newly emerging disease with limited historical data and little understanding of its dynamics. To evaluate these decisions, infectious disease modeling has proven to be a valuable tool, providing insights into disease dynamics and predicting future outcomes for different scenarios. Agent-based models, which simulate populations at an individual level, are especially well-suited to capture the complex individual behaviors and the arising aggregated system evolution, making these models suitable tools to evaluate disease progression within highly heterogeneous populations. This paper focuses on the EpiPredict project, which has aimed to develop a flexible, easy-to-use simulation framework for constructing, executing, and analyzing agent-based infectious disease models. The project objective arose from the observation that epidemiologists or public-health decision-makers, i.e., people without a strong IT background, lacked simulation tools, as most available tools required extensive programming skills to create and simulate agent-based models. Within this paper, the EpiPredict project and platform will be presented, and the relation of agents to the field of artificial intelligence discussed.