| Title: |
App-based COVID-19 syndromic surveillance and prediction of hospital admissions in COVID Symptom Study Sweden |
| Authors: |
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 |
| Source: |
Nature Communications , 13 (1) , Article 2110. (2022) (In press). |
| Publisher Information: |
Springer Science and Business Media LLC |
| Publication Year: |
2022 |
| Collection: |
University College London: UCL Discovery |
| Subject Terms: |
Humans; Sentinel Surveillance; Hospitals; Sweden; Mobile Applications; COVID-19 |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| File Description: |
text |
| Language: |
English |
| Relation: |
https://discovery.ucl.ac.uk/id/eprint/10148157/ |
| Availability: |
https://discovery.ucl.ac.uk/id/eprint/10148157/1/s41467-022-29608-7.pdf; https://discovery.ucl.ac.uk/id/eprint/10148157/ |
| Rights: |
open |
| Accession Number: |
edsbas.4764E1F0 |
| Database: |
BASE |