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Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing

Author

Listed:
  • William E. Allen

    (The How We Feel Project
    Harvard University
    Broad Institute of MIT and Harvard)

  • Han Altae-Tran

    (The How We Feel Project
    Broad Institute of MIT and Harvard
    Massachusetts Institute of Technology)

  • James Briggs

    (The How We Feel Project
    Broad Institute of MIT and Harvard
    Schmidt Science Fellows)

  • Xin Jin

    (The How We Feel Project
    Harvard University
    Broad Institute of MIT and Harvard)

  • Glen McGee

    (The How We Feel Project
    Harvard T.H. Chan School of Public Health)

  • Andy Shi

    (The How We Feel Project
    Harvard T.H. Chan School of Public Health)

  • Rumya Raghavan

    (The How We Feel Project
    Broad Institute of MIT and Harvard
    Health Sciences and Technology Program, Massachusetts Institute of Technology and Harvard Medical School)

  • Mireille Kamariza

    (The How We Feel Project
    Harvard University
    Broad Institute of MIT and Harvard)

  • Nicole Nova

    (The How We Feel Project
    Stanford University)

  • Albert Pereta

    (The How We Feel Project)

  • Chris Danford

    (The How We Feel Project)

  • Amine Kamel

    (The How We Feel Project)

  • Patrik Gothe

    (The How We Feel Project)

  • Evrhet Milam

    (The How We Feel Project)

  • Jean Aurambault

    (The How We Feel Project)

  • Thorben Primke

    (The How We Feel Project)

  • Weijie Li

    (The How We Feel Project)

  • Josh Inkenbrandt

    (The How We Feel Project)

  • Tuan Huynh

    (The How We Feel Project)

  • Evan Chen

    (The How We Feel Project)

  • Christina Lee

    (The How We Feel Project)

  • Michael Croatto

    (The How We Feel Project)

  • Helen Bentley

    (The How We Feel Project)

  • Wendy Lu

    (The How We Feel Project)

  • Robert Murray

    (The How We Feel Project)

  • Mark Travassos

    (The How We Feel Project
    University of Maryland School of Medicine)

  • Brent A. Coull

    (Harvard T.H. Chan School of Public Health)

  • John Openshaw

    (The How We Feel Project
    Stanford University School of Medicine)

  • Casey S. Greene

    (The How We Feel Project
    University of Pennsylvania Perelman School of Medicine)

  • Ophir Shalem

    (The How We Feel Project
    University of Pennsylvania Perelman School of Medicine)

  • Gary King

    (The How We Feel Project
    Harvard University)

  • Ryan Probasco

    (The How We Feel Project)

  • David R. Cheng

    (The How We Feel Project)

  • Ben Silbermann

    (The How We Feel Project)

  • Feng Zhang

    (The How We Feel Project
    Broad Institute of MIT and Harvard
    Massachusetts Institute of Technology
    McGovern Institute for Brain Research, Massachusetts Institute of Technology)

  • Xihong Lin

    (The How We Feel Project
    Broad Institute of MIT and Harvard
    Harvard T.H. Chan School of Public Health
    Harvard University)

Abstract

Despite the widespread implementation of public health measures, coronavirus disease 2019 (COVID-19) continues to spread in the United States. To facilitate an agile response to the pandemic, we developed How We Feel, a web and mobile application that collects longitudinal self-reported survey responses on health, behaviour and demographics. Here, we report results from over 500,000 users in the United States from 2 April 2020 to 12 May 2020. We show that self-reported surveys can be used to build predictive models to identify likely COVID-19-positive individuals. We find evidence among our users for asymptomatic or presymptomatic presentation; show a variety of exposure, occupational and demographic risk factors for COVID-19 beyond symptoms; reveal factors for which users have been SARS-CoV-2 PCR tested; and highlight the temporal dynamics of symptoms and self-isolation behaviour. These results highlight the utility of collecting a diverse set of symptomatic, demographic, exposure and behavioural self-reported data to fight the COVID-19 pandemic.

Suggested Citation

  • William E. Allen & Han Altae-Tran & James Briggs & Xin Jin & Glen McGee & Andy Shi & Rumya Raghavan & Mireille Kamariza & Nicole Nova & Albert Pereta & Chris Danford & Amine Kamel & Patrik Gothe & Evr, 2020. "Population-scale longitudinal mapping of COVID-19 symptoms, behaviour and testing," Nature Human Behaviour, Nature, vol. 4(9), pages 972-982, September.
    • Handle: RePEc:nat:nathum:v:4:y:2020:i:9:d:10.1038_s41562-020-00944-2
    DOI: 10.1038/s41562-020-00944-2
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    References listed on IDEAS

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    Cited by:

    1. Younjung Kim & Christl A. Donnelly & Pierre Nouvellet, 2023. "Drivers of SARS-CoV-2 testing behaviour: a modelling study using nationwide testing data in England," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    2. Elizabeth Dolan & James Goulding & Harry Marshall & Gavin Smith & Gavin Long & Laila J. Tata, 2023. "Assessing the value of integrating national longitudinal shopping data into respiratory disease forecasting models," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    3. Bo Huang & Jionghua Wang & Jixuan Cai & Shiqi Yao & Paul Kay Sheung Chan & Tony Hong-wing Tam & Ying-Yi Hong & Corrine W. Ruktanonchai & Alessandra Carioli & Jessica R. Floyd & Nick W. Ruktanonchai & , 2021. "Integrated vaccination and physical distancing interventions to prevent future COVID-19 waves in Chinese cities," Nature Human Behaviour, Nature, vol. 5(6), pages 695-705, June.
    4. Jina Suh & Eric Horvitz & Ryen W. White & Tim Althoff, 2022. "Disparate impacts on online information access during the Covid-19 pandemic," Nature Communications, Nature, vol. 13(1), pages 1-15, December.

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