On a Reinforcement Learning Methodology for Epidemic Control, with application to COVID-19
This work addresses epidemic control for public health policymakers, offering an incremental improvement by combining existing methods like Bayesian inference and RL in a novel application to COVID-19 data.
The paper tackles the problem of epidemic control by developing a reinforcement learning framework that adaptively chooses interventions to balance ICU load and socio-economic costs, and finds that over 300 days, both RL controllers substantially reduce ICU burden compared to historical government strategies.
This paper presents a real time, data driven decision support framework for epidemic control. We combine a compartmental epidemic model with sequential Bayesian inference and reinforcement learning (RL) controllers that adaptively choose intervention levels to balance disease burden, such as intensive care unit (ICU) load, against socio economic costs. We construct a context specific cost function using empirical experiments and expert feedback. We study two RL policies: an ICU threshold rule computed via Monte Carlo grid search, and a policy based on a posterior averaged Q learning agent. We validate the framework by fitting the epidemic model to publicly available ICU occupancy data from the COVID 19 pandemic in England and then generating counterfactual roll out scenarios under each RL controller, which allows us to compare the RL policies to the historical government strategy. Over a 300 day period and for a range of cost parameters, both controllers substantially reduce ICU burden relative to the observed interventions, illustrating how Bayesian sequential learning combined with RL can support the design of epidemic control policies.