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Real-time tracking of self-reported symptoms to predict potential COVID-19

Authors :
Menni, Cristina
Valdes, Ana M.
Freidin, Maxim B.
Sudre, Carole H.
Nguyen, Long H.
Drew, David A.
Ganesh, Sajaysurya
Varsavsky, Thomas
Cardoso, M. Jorge
El-Sayed Moustafa, Julia S.
Visconti, Alessia
Hysi, Pirro
Bowyer, Ruth C. E.
Mangino, Massimo
Falchi, Mario
Wolf, Jonathan
Ourselin, Sebastien
Chan, Andrew T.
Steves, Claire J.
Spector, Tim D.
Source :
Nature Medicine; July 2020, Vol. 26 Issue: 7 p1037-1040, 4p
Publication Year :
2020

Abstract

A total of 2,618,862 participants reported their potential symptoms of COVID-19 on a smartphone-based app. Among the 18,401 who had undergone a SARS-CoV-2 test, the proportion of participants who reported loss of smell and taste was higher in those with a positive test result (4,668 of 7,178 individuals; 65.03%) than in those with a negative test result (2,436 of 11,223 participants; 21.71%) (odds ratio = 6.74; 95% confidence interval = 6.31–7.21). A model combining symptoms to predict probable infection was applied to the data from all app users who reported symptoms (805,753) and predicted that 140,312 (17.42%) participants are likely to have COVID-19.

Details

Language :
English
ISSN :
10788956 and 1546170X
Volume :
26
Issue :
7
Database :
Supplemental Index
Journal :
Nature Medicine
Publication Type :
Periodical
Accession number :
ejs53207231
Full Text :
https://doi.org/10.1038/s41591-020-0916-2