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Cited by 164Data-Driven Disease SurveillanceCOVID-19 epidemiological studiesHuman Mobility and Location-Based Analysis

Tracking COVID-19 using online search

Open access
Lampos, Vasileios, Majumder, Maimuna S., Yom-Tov, Elad, Edelstein, Michael, Moura, Simon, Hamada, Yohhei, Rangaka, Molebogeng X., McKendry, Rachel A. +1 more·2021·npj Digital Medicine
DOI 10.1038/s41746-021-00384-wPMC PMC7870878

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Abstract

Previous research has demonstrated that various properties of infectious diseases can be inferred from online search behaviour. In this work we use time series of online search query frequencies to gain insights about the prevalence of COVID-19 in multiple countries. We first develop unsupervised modelling techniques based on associated symptom categories identified by the United Kingdom's National Health Service and Public Health England. We then attempt to minimise an expected bias in these signals caused by public interest-as opposed to infections-using the proportion of news media coverage devoted to COVID-19 as a proxy indicator. Our analysis indicates that models based on online searches precede the reported confirmed cases and deaths by 16.7 (10.2-23.2) and 22.1 (17.4-26.9) days, respectively. We also investigate transfer learning techniques for mapping supervised models from countries where the spread of the disease has progressed extensively to countries that are in earlier phases of their respective epidemic curves. Furthermore, we compare time series of online search activity against confirmed COVID-19 cases or deaths jointly across multiple countries, uncovering interesting querying patterns, including the finding that rarer symptoms are better predictors than common ones. Finally, we show that web searches improve the short-term forecasting accuracy of autoregressive models for COVID-19 deaths. Our work provides evidence that online search data can be used to develop complementary public health surveillance methods to help inform the COVID-19 response in conjunction with more established approaches.

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  • Models for COVID-19, building on previously established internet search algorithms for influenza, are included in Public Health England's weekly reports.

    “Models for COVID-19 (ref. 33 ), building on previously established internet search algorithms for influenza 34 , are included in Public Health England's weekly reports 35 .”

    From Digital technologies in the public-health response to COVID-19 (2020) · cites this paper as [33]

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Cited by

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Authors
Lampos, V., Majumder, M. S., Yom-Tov, E., Edelstein, M., Moura, S., Hamada, Y., Rangaka, M. X., McKendry, R. A., & Cox, I. J.
Affiliations
  • University College London (GB)
  • Boston Children's Hospital (US)
  • Bar-Ilan University (IL)
  • University of Cape Town (ZA)
  • London Centre for Nanotechnology (GB)
  • University of Copenhagen (DK)
Corresponding author
Lampos, Vasileios
ORCID
Venue
npj Digital Medicine
Publisher
Springer Science and Business Media LLC
Published
2021-02-08
Volume
4
Issue
1
Pages
17-17
ISSN
2398-6352 (ISSN-L)
DOI
10.1038/s41746-021-00384-w
PMC
PMC7870878
OpenAlex
W3011613821
Cited by
164
References
4
Citations by year
2026: 10 · 2025: 12 · 2024: 17 · 2023: 27 · 2022: 28 · 2021: 50 · 2020: 20
FWCI
21.98
Type
journal article
Language
en
Open access
Licence
Locations
  • npj Digital Medicine (publishedVersion, cc-by)
  • arXiv (Cornell University) (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • UCL Discovery (University College London) (submittedVersion)
  • arXiv (Cornell University) (cc-by)
Funders
  • RCUK | Engineering and Physical Sciences Research Council (EP/K031953/1, EP/R00529X/1)
  • RCUK | Medical Research Council (MC_PC_19070)
Topics
  • Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
  • Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
  • Social Sciences › Transportation › Human Mobility and Location-Based Analysis
Keywords
COVID-19 pandemic, internet search data, nowcasting, disease surveillance, transfer learning, COVID-19 mortality, confirmed cases, autoregressive models

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