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

• URL: http://arxiv.org/abs/2009.04607v3
• Date: Sat, 26 Feb 2022 21:00:20 GMT
• Title: Multi-Objective Model-based Reinforcement Learning for Infectious Disease Control
• Authors: Runzhe Wan, Xinyu Zhang, Rui Song
• Abstract summary: 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. We propose a Multi-Objective Model-based Reinforcement Learning framework to facilitate data-driven decision-making and minimize the overall long-term cost.
• Score: 19.022696762983017
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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