Simulating COVID-19 contagion patterns using a machine-learning-augmented agent-based model

Zi Hen Lin, Yair Grinberger and Daniel Felsenstein

Chapter 16 in Handbook on Big Data, Artificial Intelligence and Cities, 2025, pp 327-348 from Edward Elgar Publishing

Abstract: Agent behavior in simulation models is often mechanistic and lacking in realism. This chapter shows how machine learning (ML) techniques can be harnessed to increase behavioral realism in agent-based models (ABM). We use an inverse reinforcement learning (IRL) algorithm to derive the decision rules governing agent actions in the context of mobility in cities under COVID-19 restrictions. We combine the trained IRL algorithm with a pre-existing spatial epidemiological ABM that simulates agent behavior for real-world environments at the building level. These feed into the ABM, generating agent mobility trajectories using a Monte Carlo Markov Chain (MCMC) process. This is illustrated using a case study of COVID-19 contagion in Jerusalem city center. Given the level of spatial granularity in this approach, simulations of COVID-19 mitigation measures are feasible for different urban scales (city block, neighborhood, central business district, and so on). The chapter outlines the future challenges for generating behaviorally enhanced agent-based simulations.

Keywords: Machine learning; Inverse reinforcement learning; Agent-based models; Epidemiological ABM; Decision rules; Behaviorally enhanced simulations (search for similar items in EconPapers)
Date: 2025
ISBN: 9781803928043
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