Sustainable targeted interventions to mitigate the COVID-19 pandemic: A big data-driven modeling study in Hong Kong

doi:10.1063/5.0066086

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 互联网大数据与信息安全

	Sustainable targeted interventions to mitigate the COVID-19 pandemic: A big data-driven modeling study in Hong Kong
	Zhou, Hanchu 1; Zhang, Qingpeng 1; Cao, Zhidong2 ; Huang, Helai 3; Dajun Zeng, Daniel 2
发表期刊	CHAOS
ISSN	1054-1500
	2021-10-01
卷号	31 期号:10 页码:15
通讯作者	Zhang, Qingpeng(qingpeng.zhang@cityu.edu.hk)
摘要	Nonpharmaceutical interventions (NPIs) for contact suppression have been widely used worldwide, which impose harmful burdens on the well-being of populations and the local economy. The evaluation of alternative NPIs is needed to confront the pandemic with less disruption. By harnessing human mobility data, we develop an agent-based model that can evaluate the efficacies of NPIs with individualized mobility simulations. Based on the model, we propose data-driven targeted interventions to mitigate the COVID-19 pandemic in Hong Kong without city-wide NPIs. We develop a data-driven agent-based model for 7.55 x 10 6 Hong Kong residents to evaluate the efficacies of various NPIs in the first 80 days of the initial outbreak. The entire territory of Hong Kong has been split into 4905 500 x 500 m 2 grids. The model can simulate detailed agent interactions based on the demographics data, public facilities and functional buildings, transportation systems, and travel patterns. The general daily human mobility patterns are adopted from Google's Community Mobility Report. The scenario without any NPIs is set as the baseline. By simulating the epidemic progression and human movement at the individual level, we propose model-driven targeted interventions which focus on the surgical testing and quarantine of only a small portion of regions instead of enforcing NPIs in the whole city. The effectiveness of common NPIs and the proposed targeted interventions are evaluated by 100 extensive simulations. The proposed model can inform targeted interventions, which are able to effectively contain the COVID-19 outbreak with much lower disruption of the city. It represents a promising approach to sustainable NPIs to help us revive the economy of the city and the world.
DOI	10.1063/5.0066086
关键词[WOS]	CORONAVIRUS DISEASE 2019
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[72042018] ; National Natural Science Foundation of China[71971222] ; National Natural Science Foundation of China[71621002] ; Research Grants Council, University Grants Committee[C1143-20GF] ; Fundamental Research Funds for the Central Universities of Central South University[2019zzts868]
项目资助者	National Natural Science Foundation of China ; Research Grants Council, University Grants Committee ; Fundamental Research Funds for the Central Universities of Central South University
WOS研究方向	Mathematics ; Physics
WOS类目	Mathematics, Applied ; Physics, Mathematical
WOS记录号	WOS:000713632800002
出版者	AIP Publishing
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/46348
专题	多模态人工智能系统全国重点实验室_互联网大数据与信息安全
通讯作者	Zhang, Qingpeng
作者单位	1.City Univ Hong Kong, Sch Data Sci, Hong Kong, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China 3.Cent South Univ, Sch Traff & Transportat Engn, Changsha, Peoples R China
推荐引用方式 GB/T 7714	Zhou, Hanchu,Zhang, Qingpeng,Cao, Zhidong,et al. Sustainable targeted interventions to mitigate the COVID-19 pandemic: A big data-driven modeling study in Hong Kong[J]. CHAOS,2021,31(10):15.
APA	Zhou, Hanchu,Zhang, Qingpeng,Cao, Zhidong,Huang, Helai,&Dajun Zeng, Daniel.(2021).Sustainable targeted interventions to mitigate the COVID-19 pandemic: A big data-driven modeling study in Hong Kong.CHAOS,31(10),15.
MLA	Zhou, Hanchu,et al."Sustainable targeted interventions to mitigate the COVID-19 pandemic: A big data-driven modeling study in Hong Kong".CHAOS 31.10(2021):15.