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

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

doi:10.1038/s41591-020-0916-2

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

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

Department of Twin Research and Genetic Epidemiology, King's College London, London, UK. [email protected].
Department of Twin Research and Genetic Epidemiology, King's College London, SE1 7EHLondon, UK.
Nottingham City Hospital
Centre for the Developing Brain; School of Imaging Sciences & Biomedical Engineering; King's College London; London UK
Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Zoe Ltd
NIHR Biomedical Research Centre based at Guy's and St. Thomas' NHS Foundation Trust and King's College London, London, United Kingdom.
Department of Twin Research and Genetic Epidemiology, King's College London

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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.

Original language	English
Pages (from-to)	1037-1040
Number of pages	4
Journal	Nature Medicine
Volume	26
Issue number	7
Early online date	11 May 2020
DOIs	https://doi.org/10.1038/s41591-020-0916-2
Publication status	Published - 1 Jul 2020

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Menni, C., Valdes, A. M., Freidin, M. B., Sudre, C. H., Nguyen, L. H., Drew, D. A., Ganesh, S., Varsavsky, T., Cardoso, M. J., El-Sayed Moustafa, J. S., Visconti, A., Hysi, P., Bowyer, R. C. E., Mangino, M., Falchi, M., Wolf, J., Ourselin, S., Chan, A. T., Steves, C. J., & Spector, T. D. (2020). Real-time tracking of self-reported symptoms to predict potential COVID-19. Nature Medicine, 26(7), 1037-1040. https://doi.org/10.1038/s41591-020-0916-2

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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.",

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Menni, C , Valdes, AM , Freidin, MB , Sudre, CH, Nguyen, LH, Drew, DA, Ganesh, S, Varsavsky, T, Cardoso, MJ , El-Sayed Moustafa, JS , Visconti, A , Hysi, P , Bowyer, RCE, Mangino, M, Falchi, M, Wolf, J, Ourselin, S, Chan, AT, Steves, CJ & Spector, TD 2020, 'Real-time tracking of self-reported symptoms to predict potential COVID-19', Nature Medicine, vol. 26, no. 7, pp. 1037-1040. https://doi.org/10.1038/s41591-020-0916-2

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AU - Menni, Cristina

AU - Valdes, Ana M

AU - Freidin, Maxim B

AU - Sudre, Carole H

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AU - Drew, David A

AU - Ganesh, Sajaysurya

AU - Varsavsky, Thomas

AU - Cardoso, M Jorge

AU - El-Sayed Moustafa, Julia S

AU - Visconti, Alessia

AU - Hysi, Pirro

AU - Bowyer, Ruth C E

AU - Mangino, Massimo

AU - Falchi, Mario

AU - Wolf, Jonathan

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AU - Steves, Claire J

AU - Spector, Tim D

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

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