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Detecting COVID-19 infection hotspots in England using large-scale self-reported data from a mobile application: a prospective, observational study

Research output: Contribution to journalArticlepeer-review

Thomas Varsavsky, Mark S. Graham, Liane S. Canas, Sajaysurya Ganesh, Joan Capedevilla Pujol, Carole H. Sudre, Benjamin Murray, Marc Modat, M. Jorge Cardoso, Christina M. Astley, David A. Drew, Long H. Nguyen, Tove Fall, Maria F Gomez, Paul W. Franks, Andrew T. Chan, Richard Davies, Jonathan Wolf, Claire J. Steves, Tim D. Spector & 1 more Sebastien Ourselin

Original languageEnglish
Pages (from-to)E21-E29
JournalThe Lancet Public Health
Volume6
Issue number1
Early online date3 Dec 2020
DOIs
Accepted/In press10 Nov 2020
E-pub ahead of print3 Dec 2020
Published1 Jan 2021

Bibliographical note

Funding Information: Zoe Global provided in-kind support for all aspects of building, running, and supporting the app, and provided service to all users worldwide. Support for this study was provided by the National Institute for Health Research (NIHR)-funded Biomedical Research Centre (BRC) based at Guys' and St Thomas' NHS Foundation Trust. Investigators also received support from the Wellcome Trust, the UK Medical Research Council and British Heart Foundation, Alzheimer's Society, the EU, the UK NIHR, Chronic Disease Research Foundation, and the NIHR-funded BioResource, Clinical Research Facility and BRC based at Guys' and St Thomas' NHS Foundation Trust, in partnership with King's College London, the UK Research and Innovation London Medical Imaging and Artificial Intelligence Centre for Value Based Healthcare, the Wellcome Flagship Programme (WT213038/Z/18/Z), the Chronic Disease Research Foundation, and the DHSC. CMA is supported by the National Institute of Diabetes and Digestive and Kidney Diseases (K23 DK120899), as is DAD (K01DK120742). DAD and LHN are supported by the American Gastroenterological Association–Takeda COVID-19 Rapid Response Research Award (AGA2021-5102). The Massachusetts Consortium on Pathogen Readiness and Mark and Lisa Schwartz supported DAD, LHN, and ATC. LHN is supported by the American Gastroenterological Association Research Scholars Award and the National Institute of Diabetes and Digestive and Kidney Diseases (K23 DK125838). TF holds a European Research Council Starting Grant. ATC was supported in this work through a Stuart and Suzanne Steele MGH Research Scholar Award. Investigators from the COVID Symptom Study Sweden were funded in part by grants from the Swedish Research Council, Swedish Heart–Lung Foundation, and the Swedish Foundation for Strategic Research (LUDC-IRC 15-0067). We thank Catherine Burrows (Bulb, UK) for assistance with database querying. Funding Information: SG, JCP, RD, and JW are employees of Zoe Global. DAD and ATC previously served as investigators on a clinical trial of diet and lifestyle using a separate smartphone application that was supported by Zoe Global. TF reports grants from the European Research Council, Swedish Research Council, Swedish FORTE Research Council, and the Swedish Heart–Lung Foundation, outside the submitted work. MFG reports financial and in-kind support within the Innovative Medicines Initiative project BEAr-DKD from Bayer, Novo Nordisk, Astellas, Sanofi-Aventis, AbbVie, Eli Lilly, JDRF International, and Boehringer Ingelheim; personal consultancy fees from Lilly; financial and in-kind support within a project funded by the Swedish Foundation for Strategic Research on precision medicine in diabetes from Novo-Nordisk, Pfizer, Follicum, Abcentra; in-kind support on that project from Probi and Johnson and Johnson; and a grant from the EU, outside the submitted work. ATC reports grants from Massachusetts Consortium on Pathogen Readiness, during the conduct of the study, and personal fees from Pfizer and Boehringer Ingelheim and grants and personal fees from Bayer, outside the submitted work. TDS is a consultant to Zoe Global. All other authors declare no competing interests. Publisher Copyright: © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

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Abstract

Background: As many countries seek to slow the spread of COVID-19 without reimposing national restrictions, it has become important to track the disease at a local level to identify areas in need of targeted intervention. Methods: In this prospective, observational study, we did modelling using longitudinal, self-reported data from users of the COVID Symptom Study app in England between March 24, and Sept 29, 2020. Beginning on April 28, in England, the Department of Health and Social Care allocated RT-PCR tests for COVID-19 to app users who logged themselves as healthy at least once in 9 days and then reported any symptom. We calculated incidence of COVID-19 using the invited swab (RT-PCR) tests reported in the app, and we estimated prevalence using a symptom-based method (using logistic regression) and a method based on both symptoms and swab test results. We used incidence rates to estimate the effective reproduction number, R(t), modelling the system as a Poisson process and using Markov Chain Monte-Carlo. We used three datasets to validate our models: the Office for National Statistics (ONS) Community Infection Survey, the Real-time Assessment of Community Transmission (REACT-1) study, and UK Government testing data. We used geographically granular estimates to highlight regions with rapidly increasing case numbers, or hotspots. Findings: From March 24 to Sept 29, 2020, a total of 2 873 726 users living in England signed up to use the app, of whom 2 842 732 (98·9%) provided valid age information and daily assessments. These users provided a total of 120 192 306 daily reports of their symptoms, and recorded the results of 169 682 invited swab tests. On a national level, our estimates of incidence and prevalence showed a similar sensitivity to changes to those reported in the ONS and REACT-1 studies. On Sept 28, 2020, we estimated an incidence of 15 841 (95% CI 14 023–17 885) daily cases, a prevalence of 0·53% (0·45–0·60), and R(t) of 1·17 (1·15–1·19) in England. On a geographically granular level, on Sept 28, 2020, we detected 15 (75%) of the 20 regions with highest incidence according to government test data. Interpretation: Our method could help to detect rapid case increases in regions where government testing provision is lower. Self-reported data from mobile applications can provide an agile resource to inform policy makers during a quickly moving pandemic, serving as a complementary resource to more traditional instruments for disease surveillance. Funding: Zoe Global, UK Government Department of Health and Social Care, Wellcome Trust, UK Engineering and Physical Sciences Research Council, UK National Institute for Health Research, UK Medical Research Council and British Heart Foundation, Alzheimer's Society, Chronic Disease Research Foundation.

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