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


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.

SUBMITTER: Kennedy B 

PROVIDER: S-EPMC9023535 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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

Kennedy Beatrice B   Fitipaldi Hugo H   Hammar Ulf U   Maziarz Marlena M   Tsereteli Neli N   Oskolkov Nikolay N   Varotsis Georgios G   Franks Camilla A CA   Nguyen Diem D   Spiliopoulos Lampros L   Adami Hans-Olov HO   Björk Jonas J   Engblom Stefan S   Fall Katja K   Grimby-Ekman Anna A   Litton Jan-Eric JE   Martinell Mats M   Oudin Anna A   Sjöström Torbjörn T   Timpka Toomas T   Sudre Carole H CH   Graham Mark S MS   du Cadet Julien Lavigne JL   Chan Andrew T AT   Davies Richard R   Davies Richard R   Ganesh Sajaysurya S   May Anna A   Ourselin Sébastien S   Pujol Joan Capdevila JC   Selvachandran Somesh S   Wolf Jonathan J   Spector Tim D TD   Steves Claire J CJ   Gomez Maria F MF   Franks Paul W PW   Fall Tove T  

Nature communications 20220421 1


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 individu  ...[more]

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