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- W3166754645 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." @default.
- W3166754645 created "2021-06-22" @default.
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- W3166754645 date "2021-08-14" @default.
- W3166754645 modified "2023-10-01" @default.
- W3166754645 title "Multi-Objective Model-based Reinforcement Learning for Infectious Disease Control" @default.
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- W3166754645 doi "https://doi.org/10.1145/3447548.3467303" @default.
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