Rapid implementation of mobile technology for real-time epidemiology of COVID-19
Open accessPublished version of a preprint
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
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- Authors
- Drew, D. A., Nguyen, L. H., Steves, C. J., Menni, C., Freydin, M., Varsavsky, T., Sudre, C. H., Cardoso, M. J., Ourselin, S., Wolf, J., Spector, T. D., Chan, A. T., COPE Consortium, Chan, A. T., Drew, D. A., Nguyen, L. H., Joshi, A. D., Guo, C.-G., Ma, W., Lo, C.-H., Mehta, R. S., Kwon, S., Sikavi, D. R., Magicheva-Gupta, M. V., Fatehi, Z. S., Flynn, J. J., Leonardo, B. M., Albert, C. M., Andreotti, G., Beane-Freeman, L. E., Balasubramanian, B. A., Brownstein, J. S., Bruinsma, F., Cowan, A. N., Deka, A., Ernst, M. E., Figueiredo, J. C., Franks, P. W., Gardner, C. D., Ghobrial, I. M., Haiman, C. A., Hall, J. E., Deming-Halverson, S. L., Kirpach, B., Lacey, J. V., Le Marchand, L., Marinac, C. R., Martinez, M. E., Milne, R. L., Murray, A. M., Nash, D., Palmer, J. R., Patel, A. V., Rosenberg, L., Sandler, D. P., Sharma, S. V., Schurman, S. H., Wilkens, L. R., Chavarro, J. E., Eliassen, A. H., Hart, J. E., Kang, J. H., Koenen, K. C., Kubzansky, L. D., Mucci, L. A., Ourselin, S., Rich-Edwards, J. W., Song, M., Stampfer, M. J., Steves, C. J., Willett, W. C., Wolf, J., & Spector, T.
- Affiliations
- King's College London (GB)
- Department of Twin Research and Genetic Epidemiology, King’s College London, Westminster Bridge Road, London SE1 7EH, UK.
- Harvard University (US)
- ORCID
- Menni, Cristina
- Freydin, Maxim
- Varsavsky, Thomas
- Sudre, Carole H.
- Cardoso, M. Jorge
- Spector, Tim D.
- Joshi, Amit D.
- Guo, Chuan-Guo
- Ma, Wenjie
- Lo, Chun-Han
- Kwon, Sohee
- Magicheva-Gupta, Marina V.
- Fatehi, Zahra S.
- Albert, Christine M.
- Andreotti, Gabriella
- 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.
- Kirpach, Brenda
- Lacey, James V.
- Marinac, Catherine R.
- 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
- Kang, Jae Hee
- Koenen, Karestan C.
- Kubzansky, Laura D.
- Mucci, Lorelei A.
- Rich-Edwards, Janet W.
- Song, Mingyang
- Stampfer, Meir J.
- Willett, Walter C.
- Venue
- Science
- Published
- 2020-06-19
- Volume
- 368
- Issue
- 6497
- Pages
- 1362-1367
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC7200009
- OpenAlex
- W3022622144
- Cited by
- 389
- References
- 19
- Citations by year
- 2026: 8 · 2025: 9 · 2024: 23 · 2023: 47 · 2022: 82 · 2021: 119 · 2020: 101
- FWCI
- 62.76
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Science (publishedVersion, cc-by)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, other-oa)
- UCL Discovery (University College London) (submittedVersion)
- Research Portal (King's College London) (submittedVersion, cc-by)
- Funders
- Massachusetts General Hospital (Stuart and Suzanne Steele MGH Research Scholar Award)
- Wellcome Trust Centre for Mitochondrial Research
- Zoe Global Ltd.
- Topics
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Physical Sciences › Computer Science › Information Systems › COVID-19 Digital Contact Tracing
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- COVID-19 pandemic, SARS-CoV-2, mobile health apps, symptom monitoring, big data, risk factors, clinical outcomes, pandemic response
- MeSH terms
- Betacoronavirus, Big Data, COVID-19, SARS-CoV-2, Data Collection, United Kingdom, Humans, International Cooperation, Models, Theoretical, Pneumonia, Viral, United States, Global Health, Coronavirus Infections, Pandemics, Mobile Applications
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