Infection Rates from Covid-19 in Great Britain by Geographical Units: A Model-based Estimation from Mortality Data

Hill Kulu and Peter Dorey

No 84f3e, SocArXiv from Center for Open Science

Abstract: This study estimates cumulative infection rates from Covid-19 in Great Britain by geographical units 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 between 5 and 6% of people in Great Britain were infected by Covid-19 by the last third of April 2020. It is unlikely that the infection rate was lower than 3% or higher than 12%. Secondly, England had higher infection rates than Scotland and 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 more than 10% 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 in March and April.

Date: 2020-05-21
New Economics Papers: this item is included in nep-eur, nep-hea and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://osf.io/download/5ec7f294c7568600bd2d177a/

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:84f3e

DOI: 10.31219/osf.io/84f3e

Access Statistics for this paper

More papers in SocArXiv from Center for Open Science
Bibliographic data for series maintained by OSF ().