Data science approaches to confronting the COVID-19 pandemic: a narrative review. (10th January 2022)

• Record Type: Journal Article
• Authors: Zhang, Qingpeng; Gao, Jianxi; Wu, Joseph T.; Cao, Zhidong; Dajun Zeng, Daniel
• Is Part Of: Philosophical transactions. Volume 380:Number 2214(2022)
• Publication Date: 2022-01-10
• Subjects: infectious disease -- mathematical modelling -- data science -- big data -- COVID-19; Physical sciences -- Periodicals; Engineering -- Periodicals; Mathematics -- Periodicals; 500
• DOI: 10.1098/rsta.2021.0127
• Languages: English
• ISSNs: 1364-503X

Abstract

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'.