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

Authors :
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 Timpka
Carole 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
Source :
Nature Communications, Vol 13, Iss 1, Pp 1-12 (2022)
Publication Year :
2022
Publisher :
Nature Portfolio, 2022.

Abstract

The app-based COVID Symptom Study was launched in Sweden in April 2020 to contribute to real-time COVID-19 surveillance using daily symptom reports from study participants. Here, the authors show how syndromic surveillance can be used to estimate regional COVID-19 prevalence and to predict later COVID-19 hospital admissions.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20411723
Volume :
13
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
edsdoj.036669d2b480488aa89ce7128d28b6d3
Document Type :
article
Full Text :
https://doi.org/10.1038/s41467-022-29608-7