Rapid implementation of mobile technology for real-time epidemiology of COVID-19
Author:
Drew David A.1ORCID, Nguyen Long H.1ORCID, Steves Claire J.23ORCID, Menni Cristina2, Freydin Maxim2ORCID, Varsavsky Thomas4, Sudre Carole H.4ORCID, Cardoso M. Jorge4ORCID, Ourselin Sebastien4, Wolf Jonathan5ORCID, Spector Tim D.25ORCID, Chan Andrew T.16ORCID, Chan Andrew T., Drew David A., Nguyen Long H., Joshi Amit D., Guo Chuan-Guo, Ma Wenjie, Lo Chun-Han, Mehta Raaj S., Kwon Sohee, Sikavi Daniel R., Magicheva-Gupta Marina V., Fatehi Zahra S., Flynn Jacqueline J., Leonardo Brianna M., Albert Christine M., Andreotti Gabriella, Beane-Freeman Laura E., Balasubramanian Bijal A., Brownstein John S., Bruinsma Fiona, Cowan Annie N., Deka Anusila, Ernst Michael E., Figueiredo Jane C., Franks Paul W., Gardner Christopher D., Ghobrial Irene M., Haiman Christopher A., Hall Janet E., Deming-Halverson Sandra L., Kirpach Brenda, Lacey James V., Le Marchand Loic, Marinac Catherine R., Martinez Maria Elena, Milne Roger L., Murray Anne M., Nash Denis, Palmer Julie R., Patel Alpa V., Rosenberg Lynn, Sandler Dale P., Sharma Shreela V., Schurman Shepherd H., Wilkens Lynne R., Chavarro Jorge E., Eliassen A. Heather, Hart Jamie E., Kang Jae Hee, Koenen Karestan C., Kubzansky Laura D., Mucci Lorelei A., Ourselin Sebastien, Rich-Edwards Janet W., Song Mingyang, Stampfer Meir J., Steves Claire J., Willett Walter C., Wolf Jonathan, Spector Tim,
Affiliation:
1. Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, 55 Fruit St., Boston, MA 02114, USA. 2. Department of Twin Research and Genetic Epidemiology, King’s College London, Westminster Bridge Road, London SE1 7EH, UK. 3. Department of Ageing and Health, Guys and St. Thomas’ NHS Foundation Trust, Lambeth Palace Road, London SE1 7EH, UK. 4. School of Biomedical Engineering & Imaging Sciences, King’s College London, 1 Lambeth Palace Road, London SE1 7EU, UK. 5. Zoe Global Limited, 164 Westminster Bridge Road, London SE1 7RW, UK. 6. Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, 665 Huntington Ave., Boston, MA 02114, USA.
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
Funder
Massachusetts General Hospital Wellcome Trust Centre for Mitochondrial Research Zoe Global Ltd.
Publisher
American Association for the Advancement of Science (AAAS)
Subject
Multidisciplinary
Cited by
319 articles.
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