ArticlePloS one2021
Deep reinforcement learning approaches for global public health strategies for COVID-19 pandemic.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI-driven techniques for detection and mitigation of SARS-CoV-2 spread: a review, taxonomy, and trends.Clinical and experimental medicine · 2025Pooled it
- A novel intelligent hybrid reinforcement learning framework for autonomous decision making in complex health cognitive systems.Scientific reports · 2026Article
- Optimization of infectious disease intervention measures using reinforcement learning with UK COVID-19 epidemic data.Scientific reports · 2026Article
- A Multi-Agent Reinforcement Learning Framework for Public Health Decision Analysis.Healthcare analytics (New York, N.Y.) · 2025Article
- Reinforcement learning for healthcare operations management: methodological framework, recent developments, and future research directions.Health care management science · 2025Review
- Machine learning and public health policy evaluation: research dynamics and prospects for challenges.Frontiers in public health · 2025Review
- Optimal adaptive nonpharmaceutical interventions to mitigate the outbreak of respiratory infections following the COVID-19 pandemic: a deep reinforcement learning study in Hong Kong, China.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Interaction between travel restriction policies and the spread of COVID-19.Transport policy · 2023Article
- A deep reinforcement learning based decision-making approach for avoiding crowd situation within the case of Covid'19 pandemic.Computational intelligence · 2022Article
- Article
- Modeling approaches for early warning and monitoring of pandemic situations as well as decision support.Frontiers in public health · 2022Review
- Article
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3 authors.
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Abstract
backgroundUnprecedented public health measures have been used during this coronavirus 2019 (COVID-19) pandemic to control the spread of SARS-CoV-2 virus. It is a challenge to implement timely and appropriate public health interventions. METHODS AND
findingsPopulation and COVID-19 epidemiological data between 21st January 2020 to 15th November 2020 from 216 countries and territories were included with the implemented public health interventions. We used deep reinforcement learning, and the algorithm was trained to enable agents to try to find optimal public health strategies that maximized total reward on controlling the spread of COVID-19. The results suggested by the algorithm were analyzed against the actual timing and intensity of lockdown and travel restrictions. Early implementations of the actual lockdown and travel restriction policies, usually at the time of local index case were associated with less burden of COVID-19. In contrast, our agent suggested to initiate at least minimal intensity of lockdown or travel restriction even before or on the day of the index case in each country and territory. In addition, the agent mostly recommended a combination of lockdown and travel restrictions and higher intensity policies than the policies implemented by governments, but did not always encourage rapid full lockdown and full border closures. The limitation of this study was that it was done with incomplete data due to the emerging COVID-19 epidemic, inconsistent testing and reporting. In addition, our research focuses only on population health benefits by controlling the spread of COVID-19 without balancing the negative impacts of economic and social consequences.
interpretationCompared to actual government implementation, our algorithm mostly recommended earlier intensity of lockdown and travel restrictions. Reinforcement learning may be used as a decision support tool for implementation of public health interventions during COVID-19 and future pandemics.
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