用强化学习优化防疫策略，平衡防控与民生
Empowering Epidemic Response: The Role of Reinforcement Learning in Infectious Disease Control
- 用强化学习动态调整防疫干预措施
- 支持资源分配与多政策协同决策
- 适合公共卫生政策制定者参考
强化学习（RL）因其在复杂动态系统中的适应性及在多种约束条件下最大化长期效益的能力，近年来被用于优化传染病防控的非药物和药物干预策略。其在应对新冠疫情及其他传染病暴发中的潜力逐渐显现，相关研究数量快速增长。然而，针对RL在公共卫生干预策略优化中应用的专门综述仍较少。本文系统回顾了最新文献，探讨了RL在资源分配、生命与生计平衡、多策略组合干预及跨区域协同控制等关键公共卫生需求中的应用，最后提出未来研究的潜在方向。
原文摘要 · Abstract (English)
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.
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