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

TL;DR

This paper reviews recent advances in applying reinforcement learning to optimize infectious disease control strategies, highlighting its potential in public health responses and future research directions.

Contribution

It provides a comprehensive survey of RL applications in infectious disease intervention strategies, focusing on recent developments and future research avenues.

Findings

RL aids in resource allocation for disease control

RL helps balance health outcomes with economic impacts

RL supports coordinated multi-region epidemic responses

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.