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App-based COVID-19 syndromic surveillance and prediction of hospital admissions in COVID Symptom Study Sweden

Author

Listed:
  • Beatrice Kennedy

    (Uppsala University)

  • Hugo Fitipaldi

    (Lund University)

  • Ulf Hammar

    (Uppsala University)

  • Marlena Maziarz

    (Lund University Diabetes Centre)

  • Neli Tsereteli

    (Lund University)

  • Nikolay Oskolkov

    (Lund University)

  • Georgios Varotsis

    (Uppsala University)

  • Camilla A. Franks

    (Lund University Diabetes Centre)

  • Diem Nguyen

    (Uppsala University)

  • Lampros Spiliopoulos

    (Lund University Diabetes Centre
    Skåne University Hospital)

  • Hans-Olov Adami

    (Institute of Health and Society, University of Oslo
    Karolinska Institutet
    Harvard T.H. Chan School of Public Health)

  • Jonas Björk

    (Lund University
    Skåne University Hospital)

  • Stefan Engblom

    (Uppsala University)

  • Katja Fall

    (Örebro University
    Karolinska Institutet)

  • Anna Grimby-Ekman

    (University of Gothenburg)

  • Jan-Eric Litton

    (Karolinska Institutet)

  • Mats Martinell

    (Uppsala University
    Primary Care and Health, Region Uppsala)

  • Anna Oudin

    (Lund University
    Umeå University)

  • Torbjörn Sjöström

    (Novus Group International AB)

  • Toomas Timpka

    (Linköping University)

  • Carole H. Sudre

    (University College London
    University College London
    King’s College London)

  • Mark S. Graham

    (King’s College London)

  • Julien Lavigne Cadet

    (ZOE Limited)

  • Andrew T. Chan

    (Massachusetts General Hospital and Harvard Medical School)

  • Richard Davies

    (ZOE Limited)

  • Sajaysurya Ganesh

    (ZOE Limited)

  • Anna May

    (ZOE Limited)

  • Sébastien Ourselin

    (King’s College London)

  • Joan Capdevila Pujol

    (ZOE Limited)

  • Somesh Selvachandran

    (ZOE Limited)

  • Jonathan Wolf

    (ZOE Limited)

  • Tim D. Spector

    (King’s College London)

  • Claire J. Steves

    (King’s College London)

  • Maria F. Gomez

    (Lund University Diabetes Centre)

  • Paul W. Franks

    (Lund University)

  • Tove Fall

    (Uppsala University)

Abstract

The app-based COVID Symptom Study was launched in Sweden in April 2020 to contribute to real-time COVID-19 surveillance. We enrolled 143,531 study participants (≥18 years) who contributed 10.6 million daily symptom reports between April 29, 2020 and February 10, 2021. Here, we include data from 19,161 self-reported PCR tests to create a symptom-based model to estimate the individual probability of symptomatic COVID-19, with an AUC of 0.78 (95% CI 0.74–0.83) in an external dataset. These individual probabilities are employed to estimate daily regional COVID-19 prevalence, which are in turn used together with current hospital data to predict next week COVID-19 hospital admissions. We show that this hospital prediction model demonstrates a lower median absolute percentage error (MdAPE: 25.9%) across the five most populated regions in Sweden during the first pandemic wave than a model based on case notifications (MdAPE: 30.3%). During the second wave, the error rates are similar. When we apply the same model to an English dataset, not including local COVID-19 test data, we observe MdAPEs of 22.3% and 19.0% during the first and second pandemic waves, respectively, highlighting the transferability of the prediction model.

Suggested Citation

  • Beatrice Kennedy & Hugo Fitipaldi & Ulf Hammar & Marlena Maziarz & Neli Tsereteli & Nikolay Oskolkov & Georgios Varotsis & Camilla A. Franks & Diem Nguyen & Lampros Spiliopoulos & Hans-Olov Adami & Jo, 2022. "App-based COVID-19 syndromic surveillance and prediction of hospital admissions in COVID Symptom Study Sweden," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29608-7
    DOI: 10.1038/s41467-022-29608-7
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    References listed on IDEAS

    as
    1. Sven Drefahl & Matthew Wallace & Eleonora Mussino & Siddartha Aradhya & Martin Kolk & Maria Brandén & Bo Malmberg & Gunnar Andersson, 2020. "A population-based cohort study of socio-demographic risk factors for COVID-19 deaths in Sweden," Nature Communications, Nature, vol. 11(1), pages 1-7, December.
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