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- Publisher Website: 10.1098/rsta.2021.0127
- Scopus: eid_2-s2.0-85122319981
- PMID: 34802267
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Article: Data science approaches to confronting the COVID-19 pandemic: a narrative review
Title | Data science approaches to confronting the COVID-19 pandemic: a narrative review |
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Authors | |
Keywords | COVID-19 big data data science infectious disease mathematical modelling |
Issue Date | 2022 |
Publisher | The Royal Society Publishing. The Journal's web site is located at http://rsta.royalsocietypublishing.org |
Citation | Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2022, v. 380 n. 2214, p. article no. 20210127 How to Cite? |
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'. |
Persistent Identifier | http://hdl.handle.net/10722/309074 |
ISSN | 2023 Impact Factor: 4.3 2023 SCImago Journal Rankings: 0.870 |
PubMed Central ID | |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Zhang, Q | - |
dc.contributor.author | Gao, J | - |
dc.contributor.author | Wu, JTK | - |
dc.contributor.author | Cao, Z | - |
dc.contributor.author | Zeng, DD | - |
dc.date.accessioned | 2021-12-14T01:40:14Z | - |
dc.date.available | 2021-12-14T01:40:14Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2022, v. 380 n. 2214, p. article no. 20210127 | - |
dc.identifier.issn | 1364-503X | - |
dc.identifier.uri | http://hdl.handle.net/10722/309074 | - |
dc.description.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'. | - |
dc.language | eng | - |
dc.publisher | The Royal Society Publishing. The Journal's web site is located at http://rsta.royalsocietypublishing.org | - |
dc.relation.ispartof | Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | COVID-19 | - |
dc.subject | big data | - |
dc.subject | data science | - |
dc.subject | infectious disease | - |
dc.subject | mathematical modelling | - |
dc.title | Data science approaches to confronting the COVID-19 pandemic: a narrative review | - |
dc.type | Article | - |
dc.identifier.email | Wu, JTK: joewu@hku.hk | - |
dc.identifier.authority | Wu, JTK=rp00517 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1098/rsta.2021.0127 | - |
dc.identifier.pmid | 34802267 | - |
dc.identifier.pmcid | PMC8607150 | - |
dc.identifier.scopus | eid_2-s2.0-85122319981 | - |
dc.identifier.hkuros | 330803 | - |
dc.identifier.volume | 380 | - |
dc.identifier.issue | 2214 | - |
dc.identifier.spage | article no. 20210127 | - |
dc.identifier.epage | article no. 20210127 | - |
dc.identifier.isi | WOS:000720844400014 | - |
dc.publisher.place | United Kingdom | - |