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

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 Björk, Stefan Engblom, Katja Fall, Anna Grimby-Ekman, Jan-Eric Litton, Mats Martinell, Anna Oudin, Torbjörn Sjöström, Toomas Timpka, Carole H. Sudre, Mark S. Graham, Julien Lavigne Cadet, Andrew T. Chan, Richard Davies, Sajaysurya Ganesh, Anna May, Sébastien Ourselin, Joan Capdevila Pujol, Somesh Selvachandran, Jonathan Wolf, Tim D. Spector, Claire J. Steves, Maria F. Gomez, Paul W. Franks and Tove Fall ()
Additional contact information
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
Hans-Olov Adami: Institute of Health and Society, University of Oslo
Jonas Björk: Lund University
Stefan Engblom: Uppsala University
Katja Fall: Örebro University
Anna Grimby-Ekman: University of Gothenburg
Jan-Eric Litton: Karolinska Institutet
Mats Martinell: Uppsala University
Anna Oudin: Lund University
Torbjörn Sjöström: Novus Group International AB
Toomas Timpka: Linköping University
Carole H. Sudre: University College London
Mark S. Graham: King’s College London
Julien Lavigne Cadet: ZOE Limited
Andrew T. Chan: Massachusetts General Hospital and Harvard Medical School
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

Nature Communications, 2022, vol. 13, issue 1, 1-12

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-29608-7

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