Geo-social gradients in predicted COVID-19 prevalence in Great Britain: results from 1 960 242 users of the COVID-19 Symptoms Study app

Ruth C E Bowyer; Thomas Varsavsky; Ellen J Thompson; Carole H Sudre; Benjamin A K Murray; Maxim B Freidin; Darioush Yarand; Sajaysurya Ganesh; Joan Capdevila; Elco Bakker; M Jorge Cardoso; Richard Davies; Jonathan Wolf; Tim D Spector; Sebastien Ourselin; Claire J Steves; Cristina Menni

doi:10.1136/thoraxjnl-2020-215119

Geo-social gradients in predicted COVID-19 prevalence in Great Britain: results from 1 960 242 users of the COVID-19 Symptoms Study app

Ruth C E Bowyer, Thomas Varsavsky, Ellen J Thompson, Carole H Sudre, Benjamin A K Murray, Maxim B Freidin, Darioush Yarand, Sajaysurya Ganesh, Joan Capdevila, Elco Bakker, M Jorge Cardoso, Richard Davies, Jonathan Wolf, Tim D Spector, Sebastien Ourselin, Claire J Steves, Cristina Menni

King's College London
School of Imaging Sciences and Biomedical Engineering
MRC Unit for Lifelong Health and Ageing at University College London, London, UK.
Univ London, Birkbeck University London, University College London, University of London Royal Veterinary College, University of London, St Georges University London, Imperial College London, Kings College London, Royal Holloway University London, Queen Mary University London, Div Populat Hlth Sci & Educ
Zoe Ltd

Research output: Contribution to journal › Article › peer-review

8 Citations (Scopus)

Abstract

Understanding the geographical distribution of COVID-19 through the general population is key to the provision of adequate healthcare services. Using self-reported data from 1 960 242 unique users in Great Britain (GB) of the COVID-19 Symptom Study app, we estimated that, concurrent to the GB government sanctioning lockdown, COVID-19 was distributed across GB, with evidence of 'urban hotspots'. We found a geo-social gradient associated with predicted disease prevalence suggesting urban areas and areas of higher deprivation are most affected. Our results demonstrate use of self-reported symptoms data to provide focus on geographical areas with identified risk factors.

Original language	English
Journal	Thorax
DOIs	https://doi.org/10.1136/thoraxjnl-2020-215119
Publication status	E-pub ahead of print - 29 Dec 2020

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Bowyer, R. C. E., Varsavsky, T., Thompson, E. J., Sudre, C. H., Murray, B. A. K., Freidin, M. B., Yarand, D., Ganesh, S., Capdevila, J., Bakker, E., Cardoso, M. J., Davies, R., Wolf, J., Spector, T. D., Ourselin, S., Steves, C. J., & Menni, C. (2020). Geo-social gradients in predicted COVID-19 prevalence in Great Britain: results from 1 960 242 users of the COVID-19 Symptoms Study app. Thorax. Advance online publication. https://doi.org/10.1136/thoraxjnl-2020-215119

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title = "Geo-social gradients in predicted COVID-19 prevalence in Great Britain: results from 1 960 242 users of the COVID-19 Symptoms Study app",

abstract = "Understanding the geographical distribution of COVID-19 through the general population is key to the provision of adequate healthcare services. Using self-reported data from 1 960 242 unique users in Great Britain (GB) of the COVID-19 Symptom Study app, we estimated that, concurrent to the GB government sanctioning lockdown, COVID-19 was distributed across GB, with evidence of 'urban hotspots'. We found a geo-social gradient associated with predicted disease prevalence suggesting urban areas and areas of higher deprivation are most affected. Our results demonstrate use of self-reported symptoms data to provide focus on geographical areas with identified risk factors.",

author = "Bowyer, {Ruth C E} and Thomas Varsavsky and Thompson, {Ellen J} and Sudre, {Carole H} and Murray, {Benjamin A K} and Freidin, {Maxim B} and Darioush Yarand and Sajaysurya Ganesh and Joan Capdevila and Elco Bakker and Cardoso, {M Jorge} and Richard Davies and Jonathan Wolf and Spector, {Tim D} and Sebastien Ourselin and Steves, {Claire J} and Cristina Menni",

note = "{\textcopyright} Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ.",

year = "2020",

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doi = "10.1136/thoraxjnl-2020-215119",

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Bowyer, RCE, Varsavsky, T, Thompson, EJ , Sudre, CH , Murray, BAK , Freidin, MB, Yarand, D, Ganesh, S, Capdevila, J, Bakker, E, Cardoso, MJ, Davies, R, Wolf, J, Spector, TD , Ourselin, S , Steves, CJ & Menni, C 2020, 'Geo-social gradients in predicted COVID-19 prevalence in Great Britain: results from 1 960 242 users of the COVID-19 Symptoms Study app', Thorax. https://doi.org/10.1136/thoraxjnl-2020-215119

TY - JOUR

T1 - Geo-social gradients in predicted COVID-19 prevalence in Great Britain

T2 - results from 1 960 242 users of the COVID-19 Symptoms Study app

AU - Bowyer, Ruth C E

AU - Varsavsky, Thomas

AU - Thompson, Ellen J

AU - Sudre, Carole H

AU - Murray, Benjamin A K

AU - Freidin, Maxim B

AU - Yarand, Darioush

AU - Ganesh, Sajaysurya

AU - Capdevila, Joan

AU - Bakker, Elco

AU - Cardoso, M Jorge

AU - Davies, Richard

AU - Wolf, Jonathan

AU - Spector, Tim D

AU - Ourselin, Sebastien

AU - Steves, Claire J

AU - Menni, Cristina

PY - 2020/12/29

Y1 - 2020/12/29

N2 - Understanding the geographical distribution of COVID-19 through the general population is key to the provision of adequate healthcare services. Using self-reported data from 1 960 242 unique users in Great Britain (GB) of the COVID-19 Symptom Study app, we estimated that, concurrent to the GB government sanctioning lockdown, COVID-19 was distributed across GB, with evidence of 'urban hotspots'. We found a geo-social gradient associated with predicted disease prevalence suggesting urban areas and areas of higher deprivation are most affected. Our results demonstrate use of self-reported symptoms data to provide focus on geographical areas with identified risk factors.

AB - Understanding the geographical distribution of COVID-19 through the general population is key to the provision of adequate healthcare services. Using self-reported data from 1 960 242 unique users in Great Britain (GB) of the COVID-19 Symptom Study app, we estimated that, concurrent to the GB government sanctioning lockdown, COVID-19 was distributed across GB, with evidence of 'urban hotspots'. We found a geo-social gradient associated with predicted disease prevalence suggesting urban areas and areas of higher deprivation are most affected. Our results demonstrate use of self-reported symptoms data to provide focus on geographical areas with identified risk factors.

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U2 - 10.1136/thoraxjnl-2020-215119

DO - 10.1136/thoraxjnl-2020-215119

M3 - Article

C2 - 33376145

SN - 0040-6376

JO - Thorax

JF - Thorax

ER -

Geo-social gradients in predicted COVID-19 prevalence in Great Britain: results from 1 960 242 users of the COVID-19 Symptoms Study app

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