Tools
Tools
Elson, Richard 
ORCID: https://orcid.org/0000-0001-6350-5274, Davies, Tilman M., Lake, Iain R. ORCID: https://orcid.org/0000-0003-4407-5357, Vivancos, Roberto, Blomquist, Paula B., Charlett, Andre and Dabrera, Gavin
(2021)
The spatio-temporal distribution of COVID-19 infection in England between January and June 2020.
Epidemiology and Infection, 149.
ISSN 0950-2688
Preview |
PDF (Accepted_Manuscript)
- Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (1MB) | Preview |
Abstract
The spatio-temporal dynamics of an outbreak provide important insights to help direct public health resources intended to control transmission. They also provide a focus for detailed epidemiological studies and allow the timing and impact of interventions to be assessed.A common approach is to aggregate case data to administrative regions. Whilst providing a good visual impression of change over space, this method masks spatial variation and assumes that disease risk is constant across space. Risk factors for COVID-19 (e.g. population density, deprivation and ethnicity) vary from place to place across England so it follows that risk will also vary spatially. Kernel density estimation compares the spatial distribution of cases relative to the underlying population, unfettered by arbitrary geographical boundaries, to produce a continuous estimate of spatially varying risk.Using test results from healthcare settings in England (Pillar 1 of the UK Government testing strategy) and freely available methods and software, we estimated the spatial and spatio-temporal risk of COVID-19 infection across England for the first 6 months of 2020. Widespread transmission was underway when partial lockdown measures were introduced on 23 March 2020 and the greatest risk erred towards large urban areas. The rapid growth phase of the outbreak coincided with multiple introductions to England from the European mainland. The spatio-temporal risk was highly labile throughout.In terms of controlling transmission, the most important practical application of our results is the accurate identification of areas within regions that may require tailored intervention strategies. We recommend that this approach is absorbed into routine surveillance outputs in England. Further risk characterisation using widespread community testing (Pillar 2) data is needed as is the increased use of predictive spatial models at fine spatial scales.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | covid-19,health resources,kernel density estimation,severe acute respiratory syndrome coronavirus 2,spatial analysis,epidemiology,infectious diseases,sdg 3 - good health and well-being ,/dk/atira/pure/subjectarea/asjc/2700/2713 |
| Faculty \ School: | Faculty of Science > School of Environmental Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Environmental Social Sciences University of East Anglia Schools > Faculty of Science > Tyndall Centre for Climate Change Research Faculty of Science > Research Centres > Tyndall Centre for Climate Change Research Faculty of Science > Research Centres > Centre for Ecology, Evolution and Conservation |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 16 Mar 2021 00:49 |
| Last Modified: | 23 Oct 2024 23:55 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/79463 |
| DOI: | 10.1017/S0950268821000534 |
Downloads
Downloads per month over past year
Actions (login required)
 |
View Item |