  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.
