Multi-Objective Model-based Reinforcement Learning for Infectious Disease Control

TL;DR

This paper introduces a multi-objective model-based reinforcement learning framework that helps policymakers balance health and economic impacts during infectious disease outbreaks, demonstrated through COVID-19 in China.

Contribution

It presents a novel multi-objective reinforcement learning approach using Bayesian epidemiological models for real-time policy decision support.

Findings

Effective Pareto-optimal policies identified for COVID-19 control

Framework provides real-time decision support with uncertainty quantification

Demonstrated applicability to COVID-19 in China

Abstract

Severe infectious diseases such as the novel coronavirus (COVID-19) pose a huge threat to public health. Stringent control measures, such as school closures and stay-at-home orders, while having significant effects, also bring huge economic losses. In the face of an emerging infectious disease, a crucial question for policymakers is how to make the trade-off and implement the appropriate interventions timely given the huge uncertainty. In this work, we propose a Multi-Objective Model-based Reinforcement Learning framework to facilitate data-driven decision-making and minimize the overall long-term cost. Specifically, at each decision point, a Bayesian epidemiological model is first learned as the environment model, and then the proposed model-based multi-objective planning algorithm is applied to find a set of Pareto-optimal policies. This framework, combined with the prediction bands for each policy, provides a real-time decision support tool for policymakers. The application is demonstrated with the spread of COVID-19 in China.