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

Kennedy B, Fitipaldi H, Hammar U, Maziarz M, Tsereteli N, Oskolkov N, Varotsis G, Franks CA, Nguyen D, Spiliopoulos L, Adami HO, Björk J, Engblom S, Fall K, Grimby-Ekman A, Litton JE, Martinell M, Oudin A, Sjöström T, Timpka T, Sudre CH, Graham MS, du Cadet JL, Chan AT, Davies R, Ganesh S, May A, Ourselin S, Pujol JC, Selvachandran S, Wolf J, Spector TD, Steves CJ, Gomez MF, Franks PW, Fall T

Nat Commun 13 (1) 2110 [2022-04-21; online 2022-04-21]

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.

Category: Health

Category: Public Health

Funder: H2020

Funder: Hjärt-Lungfonden

Funder: VR

Type: Journal article

PubMed 35449172

DOI 10.1038/s41467-022-29608-7

Crossref 10.1038/s41467-022-29608-7

pii: 10.1038/s41467-022-29608-7
pmc: PMC9023535

Publications 7.1.2