App-based COVID-19 syndromic surveillance and prediction of hospital admissions in COVID Symptom Study Sweden
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Published:2022-04-21
Issue:1
Volume:13
Page:
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ISSN:2041-1723
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Container-title:Nature Communications
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language:en
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Short-container-title:Nat Commun
Author:
Kennedy Beatrice, Fitipaldi HugoORCID, Hammar Ulf, Maziarz MarlenaORCID, Tsereteli Neli, Oskolkov Nikolay, Varotsis Georgios, Franks Camilla A., Nguyen DiemORCID, Spiliopoulos Lampros, Adami Hans-OlovORCID, Björk JonasORCID, Engblom StefanORCID, Fall Katja, Grimby-Ekman Anna, Litton Jan-Eric, Martinell Mats, Oudin Anna, Sjöström Torbjörn, Timpka ToomasORCID, Sudre Carole H., Graham Mark S., du Cadet Julien Lavigne, Chan Andrew T., Davies RichardORCID, Ganesh Sajaysurya, May Anna, Ourselin Sébastien, Pujol Joan Capdevila, Selvachandran Somesh, Wolf JonathanORCID, Spector Tim D.ORCID, Steves Claire J.ORCID, Gomez Maria F.ORCID, Franks Paul W.ORCID, Fall ToveORCID
Abstract
AbstractThe 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.
Publisher
Springer Science and Business Media LLC
Subject
General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry,Multidisciplinary
Reference41 articles.
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