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

arXiv: https://arxiv.org/abs/2511.18035 · HTML: https://mlsanity.com/paper/2511.18035/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for clinical treatment and surgery](https://mlsanity.com/deeprl/rl-for-clinical-treatment-and-surgery/)

## Builds on (most cited first, 4 of 4)

- [Deep reinforcement learning for large-scale epidemic control](https://mlsanity.com/paper/2003.13676/) (2020)
- [Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning](https://mlsanity.com/paper/2204.05027/) (2022)
- [Multi-Objective Model-based Reinforcement Learning for Infectious Disease Control](https://mlsanity.com/paper/2009.04607/) (2020)
- [Rational social distancing policy during epidemics with limited healthcare capacity](https://mlsanity.com/paper/2205.00684/) (2022)

## 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.