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

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

doi:10.1016/s2468-2667(20)30269-3

Abstract

BackgroundAs 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.MethodsIn 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.FindingsFrom 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.InterpretationOur 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.FundingZoe 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.

Highlights

The COVID-19 pandemic caused many countries to impose strict restrictions on their citizens’ mobility and behaviour to curb the rapid spread of disease, often termed lockdowns
We showed that the large number of users can be used to provide disease surveillance with high geographical granularity, potentially providing a valuable source of information for policy makers who are seeking to understand the spread of the disease
After excluding a further 4508 (0·1%) participants with invalid age information and 26 486 (0·6%) without any daily assessments logged, we report 2 842 732 users that participated in this study

Summary

Introduction

The COVID-19 pandemic caused many countries to impose strict restrictions on their citizens’ mobility and behaviour to curb the rapid spread of disease, often termed lockdowns. Since relaxing these restrictions, many countries sought to avoid their re-imposition through combinations of nonpharmaceutical interventions[1] and test-and-trace systems. Many countries sought to avoid their re-imposition through combinations of nonpharmaceutical interventions[1] and test-and-trace systems Despite these efforts, many countries have had increases in infection since re-opening and have often re-imposed either regional[2] or national lockdowns. There is a high unmet need for tools and methods that can facilitate the timely and cost-effective identification

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