Data science approaches to confronting the COVID-19 pandemic : a narrative review
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
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Detail(s)
Original language | English |
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Article number | ARTN 20210127 |
Journal / Publication | Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences |
Volume | 380 |
Issue number | 2214 |
Online published | 22 Nov 2021 |
Publication status | Published - 10 Jan 2022 |
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DOI | DOI |
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Attachment(s) | Documents
Publisher's Copyright Statement
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Link to Scopus | https://www.scopus.com/record/display.uri?eid=2-s2.0-85122319981&origin=recordpage |
Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(8eb46f70-e108-49a1-9cef-4b96002d3e43).html |
Abstract
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'.
This article is part of the theme issue 'Data science approaches to infectious disease surveillance'.
Research Area(s)
- infectious disease, mathematical modelling, data science, big data, COVID-19, PREPAREDNESS
Citation Format(s)
Data science approaches to confronting the COVID-19 pandemic: a narrative review. / Zhang, Qingpeng; Gao, Jianxi; Wu, Joseph T. et al.
In: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 380, No. 2214, ARTN 20210127, 10.01.2022.
In: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 380, No. 2214, ARTN 20210127, 10.01.2022.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
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