Empowering Epidemic Response: The Role of Reinforcement Learning in Infectious Disease Control

2026-03-26

AI Summary

This study addresses the optimization of intervention strategies in infectious disease control, aiming to balance public health benefits against socioeconomic costs. Through a systematic review of reinforcement learning applications in epidemic response, it provides the first comprehensive synthesis of advances in optimizing both non-pharmaceutical and pharmaceutical interventions. Integrating epidemiological models with decision-making frameworks, the work analyzes policy-generation mechanisms in key scenarios—including resource allocation, coordination of multiple interventions, cross-regional collaboration, and trade-offs between lives and livelihoods. The research clarifies the unique advantages of reinforcement learning in dynamic epidemic interventions, identifies effective application contexts, and offers theoretical foundations and technical pathways for intelligent public health decision-making.

Abstract

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.

Problem

• reinforcement learning
• infectious disease control
• intervention strategies
• public health
• epidemic response

Innovation

• Reinforcement Learning
• Infectious Disease Control
• Intervention Optimization
• Public Health Policy
• Resource Allocation