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Infection rates from Covid-19 in Great Britain by geographical units: A model-based estimation from mortality data
This study estimates cumulative infection rates from Covid-19 in Great Britain by local authority districts (LADs) and council areas (CAs) and investigates spatial patterns in infection rates. We propose a model-based approach to calculate cumulative infection rates from data on observed and expecte...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7572059/ https://www.ncbi.nlm.nih.gov/pubmed/33418438 http://dx.doi.org/10.1016/j.healthplace.2020.102460 |
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author | Kulu, Hill Dorey, Peter |
author_facet | Kulu, Hill Dorey, Peter |
author_sort | Kulu, Hill |
collection | PubMed |
description | This study estimates cumulative infection rates from Covid-19 in Great Britain by local authority districts (LADs) and council areas (CAs) and investigates spatial patterns in infection rates. We propose a model-based approach to calculate cumulative infection rates from data on observed and expected deaths from Covid-19. Our analysis of mortality data shows that 7% of people in Great Britain were infected by Covid-19 by the last third of June 2020. It is unlikely that the infection rate was lower than 4% or higher than 15%. Secondly, England had higher infection rates than Scotland and especially Wales, although the differences between countries were not large. Thirdly, we observed a substantial variation in virus infection rates in Great Britain by geographical units. Estimated infection rates were highest in the capital city of London where between 11 and 12% of the population might have been infected and also in other major urban regions, while the lowest were in small towns and rural areas. Finally, spatial regression analysis showed that the virus infection rates increased with the increasing population density of the area and the level of deprivation. The results suggest that people from lower socioeconomic groups in urban areas (including those with minority backgrounds) were most affected by the spread of coronavirus from March to June. |
format | Online Article Text |
id | pubmed-7572059 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-75720592020-10-20 Infection rates from Covid-19 in Great Britain by geographical units: A model-based estimation from mortality data Kulu, Hill Dorey, Peter Health Place Article This study estimates cumulative infection rates from Covid-19 in Great Britain by local authority districts (LADs) and council areas (CAs) and investigates spatial patterns in infection rates. We propose a model-based approach to calculate cumulative infection rates from data on observed and expected deaths from Covid-19. Our analysis of mortality data shows that 7% of people in Great Britain were infected by Covid-19 by the last third of June 2020. It is unlikely that the infection rate was lower than 4% or higher than 15%. Secondly, England had higher infection rates than Scotland and especially Wales, although the differences between countries were not large. Thirdly, we observed a substantial variation in virus infection rates in Great Britain by geographical units. Estimated infection rates were highest in the capital city of London where between 11 and 12% of the population might have been infected and also in other major urban regions, while the lowest were in small towns and rural areas. Finally, spatial regression analysis showed that the virus infection rates increased with the increasing population density of the area and the level of deprivation. The results suggest that people from lower socioeconomic groups in urban areas (including those with minority backgrounds) were most affected by the spread of coronavirus from March to June. Elsevier Ltd. 2021-01 2020-10-19 /pmc/articles/PMC7572059/ /pubmed/33418438 http://dx.doi.org/10.1016/j.healthplace.2020.102460 Text en © 2020 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Kulu, Hill Dorey, Peter Infection rates from Covid-19 in Great Britain by geographical units: A model-based estimation from mortality data |
title | Infection rates from Covid-19 in Great Britain by geographical units: A model-based estimation from mortality data |
title_full | Infection rates from Covid-19 in Great Britain by geographical units: A model-based estimation from mortality data |
title_fullStr | Infection rates from Covid-19 in Great Britain by geographical units: A model-based estimation from mortality data |
title_full_unstemmed | Infection rates from Covid-19 in Great Britain by geographical units: A model-based estimation from mortality data |
title_short | Infection rates from Covid-19 in Great Britain by geographical units: A model-based estimation from mortality data |
title_sort | infection rates from covid-19 in great britain by geographical units: a model-based estimation from mortality data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7572059/ https://www.ncbi.nlm.nih.gov/pubmed/33418438 http://dx.doi.org/10.1016/j.healthplace.2020.102460 |
work_keys_str_mv | AT kuluhill infectionratesfromcovid19ingreatbritainbygeographicalunitsamodelbasedestimationfrommortalitydata AT doreypeter infectionratesfromcovid19ingreatbritainbygeographicalunitsamodelbasedestimationfrommortalitydata |