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

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

Menni, C; Valdes, AM; Freidin, MB; Sudre, CH; Nguyen, LH; Drew, DA; Ganesh, S; ... Spector, TD; + view all (2020) Real-time tracking of self-reported symptoms to predict potential COVID-19. Nature Medicine , 26 pp. 1037-1040. 10.1038/s41591-020-0916-2. Green open access

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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.

Type:	Article
Title:	Real-time tracking of self-reported symptoms to predict potential COVID-19
Open access status:	An open access version is available from UCL Discovery
DOI:	10.1038/s41591-020-0916-2
Publisher version:	https://doi.org/10.1038/s41591-020-0916-2
Language:	English
Additional information:	This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification:	UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science > Population Science and Experimental Medicine UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science > Population Science and Experimental Medicine > MRC Unit for Lifelong Hlth and Ageing UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI:	https://discovery.ucl.ac.uk/id/eprint/10120982