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American Association for the Advancement of Science, Science, 6497(368), p. 1362-1367, 2020

DOI: 10.1126/science.abc0473

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Rapid implementation of mobile technology for real-time epidemiology of COVID-19

Journal article published in 2020 by David A. Drew, Jonathan Wolf, Claire J. Steves, Thomas Varsavsky, Carole H. Sudre, Wenjie, M. Jorge Cardoso, Tim D. Spector, Long H. Nguyen, Sebastien Ourselin, Andrew T. Chan, Cristina Menni, Maxim Freydin, Daniel R. Sikavi, Wenjie Ma and other authors.
This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Abstract

Mobile symptom tracking The rapidity with which severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spreads through a population is defying attempts at tracking it, and quantitative polymerase chain reaction testing so far has been too slow for real-time epidemiology. Taking advantage of existing longitudinal health care and research patient cohorts, Drew et al. pushed software updates to participants to encourage reporting of potential coronavirus disease 2019 (COVID-19) symptoms. The authors recruited about 2 million users (including health care workers) to the COVID Symptom Study (previously known as the COVID Symptom Tracker) from across the United Kingdom and the United States. The prevalence of combinations of symptoms (three or more), including fatigue and cough, followed by diarrhea, fever, and/or anosmia, was predictive of a positive test verification for SARS-CoV-2. As exemplified by data from Wales, United Kingdom, mathematical modeling predicted geographical hotspots of incidence 5 to 7 days in advance of official public health reports. Science , this issue p. 1362