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Geo-social gradients in predicted COVID-19 prevalence in Great Britain: results from 1 960 242 users of the COVID-19 Symptoms Study app
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 govern...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8223682/ https://www.ncbi.nlm.nih.gov/pubmed/33376145 http://dx.doi.org/10.1136/thoraxjnl-2020-215119 |
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author | Bowyer, Ruth C E Varsavsky, Thomas Thompson, Ellen J Sudre, Carole H Murray, Benjamin A K Freidin, Maxim B Yarand, Darioush Ganesh, Sajaysurya Capdevila, Joan Bakker, Elco Cardoso, M Jorge Davies, Richard Wolf, Jonathan Spector, Tim D Ourselin, Sebastien Steves, Claire J Menni, Cristina |
author_facet | Bowyer, Ruth C E Varsavsky, Thomas Thompson, Ellen J Sudre, Carole H Murray, Benjamin A K Freidin, Maxim B Yarand, Darioush Ganesh, Sajaysurya Capdevila, Joan Bakker, Elco Cardoso, M Jorge Davies, Richard Wolf, Jonathan Spector, Tim D Ourselin, Sebastien Steves, Claire J Menni, Cristina |
author_sort | Bowyer, Ruth C E |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8223682 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-82236822021-07-09 Geo-social gradients in predicted COVID-19 prevalence in Great Britain: results from 1 960 242 users of the COVID-19 Symptoms Study app Bowyer, Ruth C E Varsavsky, Thomas Thompson, Ellen J Sudre, Carole H Murray, Benjamin A K Freidin, Maxim B Yarand, Darioush Ganesh, Sajaysurya Capdevila, Joan Bakker, Elco Cardoso, M Jorge Davies, Richard Wolf, Jonathan Spector, Tim D Ourselin, Sebastien Steves, Claire J Menni, Cristina Thorax Brief Communication 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. BMJ Publishing Group 2021-07 2020-12-29 /pmc/articles/PMC8223682/ /pubmed/33376145 http://dx.doi.org/10.1136/thoraxjnl-2020-215119 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Brief Communication Bowyer, Ruth C E Varsavsky, Thomas Thompson, Ellen J Sudre, Carole H Murray, Benjamin A K Freidin, Maxim B Yarand, Darioush Ganesh, Sajaysurya Capdevila, Joan Bakker, Elco Cardoso, M Jorge Davies, Richard Wolf, Jonathan Spector, Tim D Ourselin, Sebastien Steves, Claire J Menni, Cristina Geo-social gradients in predicted COVID-19 prevalence in Great Britain: results from 1 960 242 users of the COVID-19 Symptoms Study app |
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 |
title_full | Geo-social gradients in predicted COVID-19 prevalence in Great Britain: results from 1 960 242 users of the COVID-19 Symptoms Study app |
title_fullStr | Geo-social gradients in predicted COVID-19 prevalence in Great Britain: results from 1 960 242 users of the COVID-19 Symptoms Study app |
title_full_unstemmed | Geo-social gradients in predicted COVID-19 prevalence in Great Britain: results from 1 960 242 users of the COVID-19 Symptoms Study app |
title_short | Geo-social gradients in predicted COVID-19 prevalence in Great Britain: results from 1 960 242 users of the COVID-19 Symptoms Study app |
title_sort | geo-social gradients in predicted covid-19 prevalence in great britain: results from 1 960 242 users of the covid-19 symptoms study app |
topic | Brief Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8223682/ https://www.ncbi.nlm.nih.gov/pubmed/33376145 http://dx.doi.org/10.1136/thoraxjnl-2020-215119 |
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