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Data science approaches to confronting the COVID-19 pandemic: a narrative review

文献类型：期刊论文

作者Zhang, Qingpeng5; Gao, Jianxi1; Wu, Joseph T.4; Cao, Zhidong2,3; Zeng, Daniel Dajun2,3
刊名PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
出版日期2022-01-10
卷号380期号:2214页码:20
关键词infectious disease mathematical modelling data science big data COVID-19
ISSN号1364-503X
DOI10.1098/rsta.2021.0127
通讯作者Zhang, Qingpeng(qingpeng.zhang@cityu.edu.hk)
英文摘要During the COVID-19 pandemic, more than ever, data science has become a powerful weapon in combating an infectious disease epidemic and arguably any future infectious disease epidemic. Computer scientists, data scientists, physicists and mathematicians have joined public health professionals and virologists to confront the largest pandemic in the century by capitalizing on the large-scale 'big data' generated and harnessed for combating the COVID-19 pandemic. In this paper, we review the newly born data science approaches to confronting COVID-19, including the estimation of epidemiological parameters, digital contact tracing, diagnosis, policy-making, resource allocation, risk assessment, mental health surveillance, social media analytics, drug repurposing and drug development. We compare the new approaches with conventional epidemiological studies, discuss lessons we learned from the COVID-19 pandemic, and highlight opportunities and challenges of data science approaches to confronting future infectious disease epidemics. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'.
WOS关键词BIG DATA ; PREPAREDNESS
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Science & Technology - Other Topics
语种英语
WOS记录号WOS:000720844400014
出版者ROYAL SOC
资助机构Research Grants Council of the Hong Kong Special Administrative Region, China
源URL[http://ir.ia.ac.cn/handle/173211/46524]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心
通讯作者Zhang, Qingpeng
作者单位1.Rensselaer Polytech Inst, Dept Comp Sci, Troy, NY 12180 USA
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
4.Univ Hong Kong, LKS Fac Med, WHO Collaborating Ctr Infect Dis Epidemiol & Cont, Sch Publ Hlth, Hong Kong, Peoples R China
5.City Univ Hong Kong, Sch Data Sci, Hong Kong, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Qingpeng,Gao, Jianxi,Wu, Joseph T.,et al. Data science approaches to confronting the COVID-19 pandemic: a narrative review[J]. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES,2022,380(2214):20.
APA Zhang, Qingpeng,Gao, Jianxi,Wu, Joseph T.,Cao, Zhidong,&Zeng, Daniel Dajun.(2022).Data science approaches to confronting the COVID-19 pandemic: a narrative review.PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES,380(2214),20.
MLA Zhang, Qingpeng,et al."Data science approaches to confronting the COVID-19 pandemic: a narrative review".PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES 380.2214(2022):20.

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来源：自动化研究所

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