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 TimpkaCarole H Sudre, Mark S Graham, Julien Lavigne du 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, Tove Fall

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number2110
Number of pages12
JournalNature Communications
Volume13
Issue number1
DOIs
Publication statusPublished - 2022 Apr 21

Bibliographical note

© 2022. The Author(s).

Subject classification (UKÄ)

  • Public Health, Global Health, Social Medicine and Epidemiology

Free keywords

  • COVID-19/epidemiology
  • Hospitals
  • Humans
  • Mobile Applications
  • Sentinel Surveillance
  • Sweden/epidemiology
  • Computational models
  • Epidemiology
  • Viral infection

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