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

doi:10.1098/rsta.2021.0127

CORC > 自动化研究所 > 中国科学院自动化研究所 > 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

	Data science approaches to confronting the COVID-19 pandemic: a narrative review
	Zhang, Qingpeng 5; Gao, Jianxi 1; Wu, Joseph T.4; Cao, Zhidong 2,3; Zeng, Daniel Dajun 2,3
刊名	PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
	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
DOI	10.1098/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'.
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关键词	BIG DATA ; PREPAREDNESS
WOS研究方向	Science & Technology - Other Topics
语种	英语
出版者	ROYAL SOC
WOS记录号	WOS:000720844400014
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/173211/46524]
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.