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

doi:10.1093/jamia/ocad116

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	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
	Yao, Yao 1; Zhou, Hanchu 1; Cao, Zhidong 4; Zeng, Daniel Dajun 4; Zhang, Qingpeng 2,3,5
刊名	JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
	2023-06-26
页码	9
关键词	Covid-19 reinforcement learning artificial intelligence machine learning mathematical modelling infectious diseases
ISSN号	1067-5027
DOI	10.1093/jamia/ocad116
通讯作者	Zhang, Qingpeng(qpzhang@hku.hk)
英文摘要	Background Long-lasting nonpharmaceutical interventions (NPIs) suppressed the infection of COVID-19 but came at a substantial economic cost and the elevated risk of the outbreak of respiratory infectious diseases (RIDs) following the pandemic. Policymakers need data-driven evidence to guide the relaxation with adaptive NPIs that consider the risk of both COVID-19 and other RIDs outbreaks, as well as the available healthcare resources. Methods Combining the COVID-19 data of the sixth wave in Hong Kong between May 31, 2022 and August 28, 2022, 6-year epidemic data of other RIDs (2014-2019), and the healthcare resources data, we constructed compartment models to predict the epidemic curves of RIDs after the COVID-19-targeted NPIs. A deep reinforcement learning (DRL) model was developed to learn the optimal adaptive NPIs strategies to mitigate the outbreak of RIDs after COVID-19-targeted NPIs are lifted with minimal health and economic cost. The performance was validated by simulations of 1000 days starting August 29, 2022. We also extended the model to Beijing context. Findings Without any NPIs, Hong Kong experienced a major COVID-19 resurgence far exceeding the hospital bed capacity. Simulation results showed that the proposed DRL-based adaptive NPIs successfully suppressed the outbreak of COVID-19 and other RIDs to lower than capacity. DRL carefully controlled the epidemic curve to be close to the full capacity so that herd immunity can be reached in a relatively short period with minimal cost. DRL derived more stringent adaptive NPIs in Beijing. Interpretation DRL is a feasible method to identify the optimal adaptive NPIs that lead to minimal health and economic cost by facilitating gradual herd immunity of COVID-19 and mitigating the other RIDs outbreaks without overwhelming the hospitals. The insights can be extended to other countries/regions.
资助项目	Research Grants Council of the Hong Kong Special Administrative Region, China[11218221] ; Research Grants Council of the Hong Kong Special Administrative Region, China[C7154-20GF] ; Research Grants Council of the Hong Kong Special Administrative Region, China[C7151-20GF] ; Research Grants Council of the Hong Kong Special Administrative Region, China[C1143-20GF]
WOS研究方向	Computer Science ; Health Care Sciences & Services ; Information Science & Library Science ; Medical Informatics
语种	英语
出版者	OXFORD UNIV PRESS
WOS记录号	WOS:001016260200001
资助机构	Research Grants Council of the Hong Kong Special Administrative Region, China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/53567]
专题	舆论大数据科学与技术应用联合实验室
通讯作者	Zhang, Qingpeng
作者单位	1.City Univ Hong Kong, Sch Data Sci, Hong Kong, Peoples R China 2.Univ Hong Kong, LKS Fac Med, Dept Pharmacol & Pharm, Hong Kong, Peoples R China 3.Univ Hong Kong, Musketeers Fdn Inst Data Sci, Hong Kong, Peoples R China 4.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 5.Univ Hong Kong, Musketeers Fdn Inst Data Sci, LKS Fac Med, Dept Pharmacol & Pharm, Hong Kong, Peoples R China
推荐引用方式 GB/T 7714	Yao, Yao,Zhou, Hanchu,Cao, Zhidong,et al. 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[J]. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION,2023:9.
APA	Yao, Yao,Zhou, Hanchu,Cao, Zhidong,Zeng, Daniel Dajun,&Zhang, Qingpeng.(2023).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,9.
MLA	Yao, Yao,et al."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 (2023):9.