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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.
Varsavsky, Thomas; Graham, Mark S; Canas, Liane S; Ganesh, Sajaysurya; Pujol, Joan Capdevila; Sudre, Carole H; Murray, Benjamin; Modat, Marc; Cardoso, M Jorge; Astley, Christina M; Drew, David A; Nguyen, Long H; Fall, Tove; Gomez, Maria F; Franks, Paul W; Chan, Andrew T; Davies, Richard; Wolf, Jonathan; Steves, Claire J; Spector, Tim D; Ourselin, Sebastien.
  • Varsavsky T; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
  • Graham MS; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
  • Canas LS; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
  • Ganesh S; Zoe Global Limited, London, UK.
  • Pujol JC; Zoe Global Limited, London, UK.
  • Sudre CH; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
  • Murray B; MRC Unit for Lifelong Health and Ageing,Department of Population Science and Experimental Medicine, University College London, UK.
  • Modat M; Centre for Medical Image Computing, Department of Computer Science, University College London, UK.
  • Cardoso MJ; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
  • Astley CM; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
  • Drew DA; School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
  • Nguyen LH; Division of Endocrinology and Computational Epidemiology, Boston Children's Hospital, Harvard Medical School, USA.
  • Fall T; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, MA, USA.
  • Gomez MF; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, MA, USA.
  • Franks PW; Department of Medical Sciences, Uppsala University, Uppsala, Sweden.
  • Chan AT; Department of Clinical Sciences, Lund University Diabetes Centre, Sweden.
  • Davies R; Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Sweden.
  • Wolf J; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, MA, USA.
  • Steves CJ; Zoe Global Limited, London, UK.
  • Spector TD; Zoe Global Limited, London, UK.
  • Ourselin S; Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
medRxiv ; 2020 Nov 17.
Article in English | MEDLINE | ID: covidwho-915975
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:

We performed modelling on longitudinal, self-reported data from users of the COVID Symptom Study app in England between 24 March and 29 September, 2020. Combining a symptom-based predictive model for COVID-19 positivity and RT-PCR tests provided by the Department of Health we were able to estimate disease incidence, prevalence and effective reproduction number. Geographically granular estimates were used to highlight regions with rapidly increasing case numbers, or hotspots.

FINDINGS:

More than 2.8 million app users in England provided 120 million daily reports of their symptoms, and recorded the results of 170,000 PCR tests. On a national level our estimates of incidence and prevalence showed similar sensitivity to changes as two national community surveys the ONS and REACT-1 studies. On 28 September 2020 we estimated 15,841 (95% CI 14,023-17,885) daily cases, a prevalence of 0.53% (95% CI 0.45-0.60), and R(t) of 1.17 (95% credible interval 1.15-1.19) in England. On a geographically granular level, on 28 September 2020 we detected 15 of the 20 regions with highest incidence according to Government test data, with indications that our method may be able to detect rapid case increases in regions where Government testing provision is more limited.

INTERPRETATION:

Self-reported data from mobile applications can provide an agile resource to inform policymakers during a fast-moving pandemic, serving as an independent and complementary resource to more traditional instruments for disease surveillance.

FUNDING:

Zoe Global Limited, Department of Health, Wellcome Trust, EPSRC, NIHR, MRC, Alzheimer's Society.

Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study / Observational study / Prognostic study Topics: Variants Language: English Year: 2020 Document Type: Article Affiliation country: 2020.10.26.20219659

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Diagnostic study / Observational study / Prognostic study Topics: Variants Language: English Year: 2020 Document Type: Article Affiliation country: 2020.10.26.20219659