학술논문
App-based COVID-19 syndromic surveillance and prediction of hospital admissions in COVID Symptom Study Sweden
Document Type
article
Author
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)
Subject
Language
English
ISSN
2041-1723
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.