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

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    Author
    Allen, William E.
    Altae-Tran, Han
    Briggs, James
    Jin, Xin
    McGee, Glen
    Shi, Andy
    Raghavan, Rumya
    Kamariza, Mireille
    Nova, Nicole
    Pereta, Albert
    Danford, Chris
    Kamel, Amine
    Gothe, Patrik
    Milam, Evrhet
    Aurambault, Jean
    Primke, Thorben
    Li, Weijie
    Inkenbrandt, Josh
    Huynh, Tuan
    Chen, Evan
    Lee, Christina
    Croatto, Michael
    Bentley, Helen
    Lu, Wendy
    Murray, Robert
    Travassos, Mark
    Coull, Brent A.
    Openshaw, John
    Greene, Casey S.
    Shalem, Ophir
    King, Gary
    Probasco, Ryan
    Cheng, David R.
    Silbermann, Ben
    Zhang, Feng
    Lin, Xihong
    Show allShow less

    Date
    2020-08-26
    Journal
    Nature Human Behaviour
    Publisher
    Springer Science and Business Media LLC
    Type
    Article
    
    Metadata
    Show full item record
    See at
    https://doi.org/10.1038/s41562-020-00944-2
    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. © 2020, The Author(s).
    Sponsors
    National Cancer Institute
    Keyword
    How We Feel Project
    COVID-19--epidemiology
    COVID-19--prevention & control
    Data Collection--methods
    Epidemiological Monitoring
    Mobile Applications
    Self Report
    Identifier to cite or link to this item
    http://hdl.handle.net/10713/13624
    ae974a485f413a2113503eed53cd6c53
    10.1038/s41562-020-00944-2
    Scopus Count
    Collections
    UMB Coronavirus Publications
    UMB Open Access Articles 2020

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