Data science approaches to infectious disease surveillance

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)Editorial Preface

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Original languageEnglish
Article number20210115
Journal / PublicationPhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Issue number2214
Online published22 Nov 2021
Publication statusPublished - 10 Jan 2022



Novel data science approaches are needed to confront large-scale infectious disease epidemics such as COVID-19, human immunodeficiency viruses, African swine flu and Ebola. Human beings are now equipped with richer data and more advanced data analytics methodologies, many of which have become available only in the last decade. The theme issue Data Science Approaches to Infectious Diseases Surveillance reports the latest interdisciplinary research on developing novel data science methodologies to capitalize on the rich 'big data' of human behaviours to confront infectious diseases, with a particular focus on combating the ongoing COVID-19 pandemic. Compared to conventional public health research, articles in this issue present innovative data science approaches that were not possible without the growing human behaviour data and the recent advances in information and communications technology. This issue has 12 research papers and one review paper from a strong lineup of contributors from multiple disciplines, including data science, computer science, computational social sciences, applied maths, statistics, physics and public health. This introductory article provides a brief overview of the issue and discusses the future of this emerging field. 
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

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