Empowering Epidemic Response: The Role of Reinforcement Learning in Infectious Disease Control
On the map: Deep reinforcement learning methods → RL for clinical treatment and surgery
Abstract (arXiv)
Reinforcement learning (RL), owing to its adaptability to various dynamic systems in many real-world scenarios and the capability of maximizing long-term outcomes under different constraints, has been used in infectious disease control to optimize the intervention strategies for controlling infectious disease spread and responding to outbreaks in recent years. The potential of RL for assisting public health sectors in preventing and controlling infectious diseases is gradually emerging and being explored by rapidly increasing publications relevant to COVID-19 and other infectious diseases. However, few surveys exclusively discuss this topic, that is, the development and application of RL approaches for optimizing strategies of non-pharmaceutical and pharmaceutical interventions of public health. Therefore, this paper aims to provide a concise review and discussion of the latest literature on how RL approaches have been used to assist in controlling the spread and outbreaks of infectious diseases, covering several critical topics addressing public health demands: resource allocation, balancing between lives and livelihoods, mixed policy of multiple interventions, and inter-regional coordinated control. Finally, we conclude the paper with a discussion of several potential directions for future research.
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 6 of its 40 references that are in the map. First posted on arXiv 26 Mar 2026.
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6 of 40 references.
References · 6
- 2019Reinforcement Learning in Healthcare: A Surveycited by 310
- 2018Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applicationscited by 127
- 2020Optimising Lockdown Policies for Epidemic Control using Reinforcement Learningcited by 9
- 2023Cooperating Graph Neural Networks with Deep Reinforcement Learning for Vaccine Prioritizationcited by 9
- 2022A general framework for optimising cost-effectiveness of pandemic response under partial intervention measurescited by 2
- 2025H2-MARL: Multi-Agent Reinforcement Learning for Pareto Optimality in Hospital Capacity Strain and Human Mobility during Epidemiccited by 1
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- 2025Optimization of Infectious Disease Intervention Measures Based on Reinforcement Learning -- Empirical analysis based on UK COVID-19 epidemic data
- 2024SIR-RL: Reinforcement Learning for Optimized Policy Control during Epidemiological Outbreaks in Emerging Market and Developing Economies
- 2025On a Reinforcement Learning Methodology for Epidemic Control, with application to COVID-19
- 2024Modeling and Optimization of Epidemiological Control Policies Through Reinforcement Learning
- 2025Learning Pareto-Optimal Pandemic Intervention Policies with MORL
- 2024Reinforcement Learning in Dynamic Treatment Regimes Needs Critical Reexamination
- 2026Optimizing Resource-Constrained Non-Pharmaceutical Interventions for Multi-Cluster Outbreak Control Using Hierarchical Reinforcement Learning
- 2025Reward Engineering for Spatial Epidemic Simulations: A Reinforcement Learning Platform for Individual Behavioral Learning