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Real-time Tracking of Self-reported Symptoms to Predict Potential COVID-19

Overview
Journal Nat Med
Specialties General Medicine
Molecular Biology
Date 2020 May 13
PMID 32393804
Citations 704
Authors
Cristina Menni
Ana M Valdes
Maxim B Freidin
Carole H Sudre
Long H Nguyen
David A Drew
Sajaysurya Ganesh
Thomas Varsavsky
M Jorge Cardoso
Julia S El-Sayed Moustafa
Alessia Visconti
Pirro Hysi
Ruth C E Bowyer
Massimo Mangino
Mario Falchi
Jonathan Wolf
Sebastien Ourselin
Andrew T Chan
Claire J Steves
Tim D Spector
Affiliations
Soon will be listed here.
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.

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