• Symptom data help predict COVID-19 admis

    From ScienceDaily@1:317/3 to All on Thursday, April 21, 2022 22:30:48
    Symptom data help predict COVID-19 admissions

    Date:
    April 21, 2022
    Source:
    Uppsala University
    Summary:
    Researchers are conducting one of the largest citizen science
    projects in Sweden to date. Since the start of the pandemic, study
    participants have used an app to report how they feel daily even
    if they are well. This symptom data could be used to estimate
    COVID-19 infection trends across Sweden and predict hospital
    admissions due to COVID-19 a week in advance.



    FULL STORY ========================================================================== Researchers at Lund University and Uppsala University are conducting
    one of the largest citizen science projects in Sweden to date. Since the
    start of the pandemic, study participants have used an app to report how
    they feel daily even if they are well. This symptom data could be used
    to estimate COVID-19 infection trends across Sweden and predict hospital admissions due to COVID-19 a week in advance. The results have now been published in the scientific journal Nature Communications.


    ==========================================================================
    The analyses included more than 10 million daily reports from participants
    in COVID Symptom Study Sweden from April 2020 to February 2021. The
    scope of the study was to develop and evaluate a framework to estimate
    the regional prevalence of COVID-19 using symptom-based surveillance,
    and to test if these prevalence estimates could be used to predict
    subsequent trends in COVID-19 hospital admissions.

    "We show for the first time that symptom data can be informative in
    predicting subsequent regional trends in hospital admissions due to
    COVID-19, and confirm previous reports that trends in symptoms are related
    to community infection rates. These symptoms-based methods could be particularly useful in time periods and areas with low COVID-19-testing,"
    says Tove Fall, Professor of Molecular Epidemiology at the Department
    of Medical Sciences, Uppsala University, one of the lead authors of
    the study.

    The app used for data collection was originally developed by ZOE, a
    health science company, with support from physicians and researchers
    at King's College London and Guy's and St Thomas' Hospitals, for
    non-commercial purposes. The ZOE COVID Study was first launched in the
    UK and the US in March 2020. It was adapted and introduced in Sweden,
    where it is known as COVID Symptom Study Sweden, in April 2020. Any
    adult in Sweden can participate by downloading the app and providing
    in-app consent. Participants fill in a general baseline health survey,
    and can then report how they feel each day, even if they are well. Over
    209,000 participants in Sweden have contributed so far, providing daily
    reports on symptoms, COVID-19 test results and vaccinations.

    "This project would not have been possible without the dedication, hard
    work and collaborative spirit of our team members and colleagues in the
    UK and US.

    Above all, we have to thank each and every study participant for
    their contributions. Performing 'real-time' science is challenging,
    but of utmost importance during a pandemic. We are proud that we have
    been able to share real-time national and regional COVID-19 prevalence estimates on our dashboard almost every day since May 2020, and that
    COVID Symptom Study Sweden data was useful to Swedish municipalities and
    county councils. With over 4.7 million contributors globally, the ZOE
    COVID Study is one of the largest ongoing public science projects of its
    kind and has shown us the power of citizen science," says Maria Gomez, Professor of Physiology at the Department of Clinical Sciences and Lund University Diabetes Centre, one of the lead authors of the study.

    Researchers developed and validated a model to understand which
    symptoms were associated with a positive COVID-19 test, using data
    from participants who had reported symptoms and results from COVID-19 PCR-tests. That model could then be employed to estimate daily national
    and regional COVID-19 prevalence in the entire study population, as well
    as subsequently in the Swedish adult population. Combining app-based
    prevalence estimates with information on current hospital admissions, researchers were also able to predict future hospital admissions with
    moderate accuracy. Furthermore, the same model could be successfully
    applied to an English dataset to predict hospital admissions across the
    seven English healthcare regions, highlighting the transferability of
    the model to other countries.

    "Real-time and granular pandemic surveillance requires combining
    multiple sources of data," says Beatrice Kennedy, research fellow at the Department of Medical Sciences, Uppsala University and first author of the study. "Our findings highlight how app-based symptom-based surveillance
    may constitute a scalable and dynamic tool to monitor infection trends,
    and as such it should be considered in future pandemic preparedness
    plans."

    ========================================================================== Story Source: Materials provided by Uppsala_University. Note: Content
    may be edited for style and length.


    ========================================================================== Journal Reference:
    1. Beatrice Kennedy, Hugo Fitipaldi, Ulf Hammar, Marlena Maziarz, Neli
    Tsereteli, Nikolay Oskolkov, Georgios Varotsis, Camilla A. Franks,
    Diem Nguyen, Lampros Spiliopoulos, Hans-Olov Adami, Jonas Bjo"rk,
    Stefan Engblom, Katja Fall, Anna Grimby-Ekman, Jan-Eric Litton,
    Mats Martinell, Anna Oudin, Torbjo"rn Sjo"stro"m, Toomas Timpka,
    Carole H. Sudre, Mark S.

    Graham, Julien Lavigne du Cadet, Andrew T. Chan, Richard Davies,
    Sajaysurya Ganesh, Anna May, Se'bastien Ourselin, Joan Capdevila
    Pujol, Somesh Selvachandran, Jonathan Wolf, Tim D. Spector, Claire
    J. Steves, Maria F. Gomez, Paul W. Franks, Tove Fall. App-based
    COVID-19 syndromic surveillance and prediction of hospital
    admissions in COVID Symptom Study Sweden. Nature Communications,
    2022; 13 (1) DOI: 10.1038/s41467-022- 29608-7 ==========================================================================

    Link to news story: https://www.sciencedaily.com/releases/2022/04/220421094055.htm

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