Multi-Objective Model-based Reinforcement Learning for Infectious Disease Control
27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021
; : 1634-1644, 2021.
Article
in English
| Scopus | ID: covidwho-1430229
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. © 2021 ACM.
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021
Year:
2021
Document Type:
Article
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