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On a Reinforcement Learning Methodology for Epidemic Control, with application to COVID-19

arXiv 2511.18035 ↗First posted 22 Nov 2025stat.ME cs.LG stat.COcited by 0

On the map: Deep reinforcement learning methods → RL for clinical treatment and surgery

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for clinical treatment and surgery, one of 15 directions in Deep reinforcement learning methods. Builds on 4 of its 50 references that are in the map. First posted on arXiv 22 Nov 2025.

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4 of 50 references.

References · 4

  1. 2020Deep reinforcement learning for large-scale epidemic controlcited by 16
  2. 2022Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learningcited by 9
  3. 2020Multi-Objective Model-based Reinforcement Learning for Infectious Disease Controlcited by 7
  4. 2022Rational social distancing policy during epidemics with limited healthcare capacitycited by 1

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  1. 2026Empowering Epidemic Response: The Role of Reinforcement Learning in Infectious Disease Control
  2. 2024Modeling and Optimization of Epidemiological Control Policies Through Reinforcement Learning
  3. 2025Learning Pareto-Optimal Pandemic Intervention Policies with MORL
  4. 2026Optimizing Resource-Constrained Non-Pharmaceutical Interventions for Multi-Cluster Outbreak Control Using Hierarchical Reinforcement Learning
  5. 2025Optimization of Infectious Disease Intervention Measures Based on Reinforcement Learning -- Empirical analysis based on UK COVID-19 epidemic data
  6. 2024SIR-RL: Reinforcement Learning for Optimized Policy Control during Epidemiological Outbreaks in Emerging Market and Developing Economies
  7. 2025H2-MARL: Multi-Agent Reinforcement Learning for Pareto Optimality in Hospital Capacity Strain and Human Mobility during Epidemic
  8. 2024Spatio-Temporal SIR Model of Pandemic Spread During Warfare with Optimal Dual-use Healthcare System Administration using Deep Reinforcement Learning