Rudkin, S., Webber, D. orcid.org/0000-0002-1488-3436 and Dlotko, P. (2026) The spatial spread of infection rates at the dawn of the pandemic: a socioeconomic approach. Papers in Regional Science. ISSN: 1056-8190
Abstract
This paper presents an innovative examination of the spread of COVID-19 infection rates across England over a seven-week period commencing prior to the initial lockdown and continuing until the relaxation of some COVID-19 lockdown restrictions. We examine the evolution in infection rates across socioeconomic characteristics using topological data analysis to map the joint distribution of socioeconomic characteristics and identify where and when infection rates grew differentially. Although the over 65s and those with chronic health conditions were socioeconomic groups most at risk of experiencing morbidity and mortality from COVID-19, these socioeconomic correlates reflect consequences of the spread rather than the carriers of the virus and the reasons for the spread. Our empirical results present new findings that International Territorial Level 3 areas with higher proportions of younger inhabitants who worked longer hours for higher pay experienced earlier and faster rates of increase in infections, and that commuter towns and rural areas lagged their urban metropolises in terms of infection rates. These results underscore who was infected, where labour market characteristics associated with infection rates interact across space, and the need to improve understanding of when and why areas experienced higher infection rates. This is the first paper to find that the spread of the COVID-19 virus in the UK was greater where there was a relative abundance of skilled workers who have greater professional geographical mobility.
Metadata
| Item Type: | Article |
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| Authors/Creators: |
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| Copyright, Publisher and Additional Information: | © 2026 The Authors. Except as otherwise noted, this author-accepted version of a journal article published in Papers in Regional Science is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ © 2026 The authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
| Keywords: | COVID-19Spatial contagion; Topological Data Analysis; Virus infections rates |
| Dates: |
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| Institution: | The University of Sheffield |
| Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Management School (Sheffield) |
| Date Deposited: | 13 Aug 2026 09:40 |
| Last Modified: | 13 Aug 2026 09:40 |
| Status: | Published online |
| Publisher: | Elsevier |
| Refereed: | Yes |
| Identification Number: | 10.1016/j.pirs.2026.100163 |
| Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:244285 |
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