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

Kennedy, Beatrice ; Fitipaldi, Hugo LU ; Hammar, Ulf ; Maziarz, Marlena LU ; Tsereteli, Neli LU ; Oskolkov, Nikolay LU ; Varotsis, Georgios ; Franks, Camilla A LU ; Nguyen, Diem and Spiliopoulos, Lampros LU , et al. (2022) In Nature Communications 13(1).
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... (More)

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.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
COVID-19/epidemiology, Hospitals, Humans, Mobile Applications, Sentinel Surveillance, Sweden/epidemiology, Computational models, Epidemiology, Viral infection
in
Nature Communications
volume
13
issue
1
article number
2110
pages
12 pages
publisher
Nature Publishing Group
external identifiers
  • scopus:85128664211
  • pmid:35449172
ISSN
2041-1723
DOI
10.1038/s41467-022-29608-7
project
Improved preparedness for future pandemics and other health crises through large-scale disease surveillance
language
English
LU publication?
yes
additional info
© 2022. The Author(s).
id
8b05a33e-677d-4e91-a11e-91fb6fa60509
date added to LUP
2022-05-03 09:45:56
date last changed
2024-06-27 13:25:20
@article{8b05a33e-677d-4e91-a11e-91fb6fa60509,
  abstract     = {{<p>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.</p>}},
  author       = {{Kennedy, Beatrice and Fitipaldi, Hugo and Hammar, Ulf and Maziarz, Marlena and Tsereteli, Neli and Oskolkov, Nikolay and Varotsis, Georgios and Franks, Camilla A and Nguyen, Diem and Spiliopoulos, Lampros and Adami, Hans-Olov and Björk, Jonas and Engblom, Stefan and Fall, Katja and Grimby-Ekman, Anna and Litton, Jan-Eric and Martinell, Mats and Oudin, Anna and Sjöström, Torbjörn and Timpka, Toomas and Sudre, Carole H and Graham, Mark S and du Cadet, Julien Lavigne and Chan, Andrew T and Davies, Richard and Ganesh, Sajaysurya and May, Anna and Ourselin, Sébastien and Pujol, Joan Capdevila and Selvachandran, Somesh and Wolf, Jonathan and Spector, Tim D and Steves, Claire J and Gomez, Maria F and Franks, Paul W and Fall, Tove}},
  issn         = {{2041-1723}},
  keywords     = {{COVID-19/epidemiology; Hospitals; Humans; Mobile Applications; Sentinel Surveillance; Sweden/epidemiology; Computational models; Epidemiology; Viral infection}},
  language     = {{eng}},
  month        = {{04}},
  number       = {{1}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Nature Communications}},
  title        = {{App-based COVID-19 syndromic surveillance and prediction of hospital admissions in COVID Symptom Study Sweden}},
  url          = {{http://dx.doi.org/10.1038/s41467-022-29608-7}},
  doi          = {{10.1038/s41467-022-29608-7}},
  volume       = {{13}},
  year         = {{2022}},
}