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

Research output: Contribution to journalArticle

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

Original languageEnglish
Pages (from-to)1037-1040
Number of pages4
JournalNature Medicine
Volume26
Issue number7
DOIs
Publication statusPublished - 1 Jul 2020

Documents

  • dd.lhn.ac.103515_Menni_final_1588085988_1

    dd.lhn.ac.103515_Menni_final_1588085988_1.docx, 67.1 KB, application/vnd.openxmlformats-officedocument.wordprocessingml.document

    15/07/2020

    Accepted author manuscript

King's Authors

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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