Exploring the spatial relative risk of COVID-19 in Berlin-Neukölln

Lambio, Christoph, Schmitz, Tillman, Elson, Richard ORCID: https://orcid.org/0000-0001-6350-5274, Butler, Jeffrey, Roth, Alexandra, Feller, Silke, Savaskan, Nicolai and Lakes, Tobia (2023) Exploring the spatial relative risk of COVID-19 in Berlin-Neukölln. International Journal of Environmental Research and Public Health, 20 (10). ISSN 1661-7827

Preview

PDF (ijerph-20-05830) - Published Version Available under License Creative Commons Attribution. Download (4MB) | Preview

Abstract

Identifying areas with high and low infection rates can provide important etiological clues. Usually, areas with high and low infection rates are identified by aggregating epidemiological data into geographical units, such as administrative areas. This assumes that the distribution of population numbers, infection rates, and resulting risks is constant across space. This assumption is, however, often false and is commonly known as the modifiable area unit problem. This article develops a spatial relative risk surface by using kernel density estimation to identify statistically significant areas of high risk by comparing the spatial distribution of address-level COVID-19 cases and the underlying population at risk in Berlin-Neukölln. Our findings show that there are varying areas of statistically significant high and low risk that straddle administrative boundaries. The findings of this exploratory analysis further highlight topics such as, e.g., Why were mostly affluent areas affected during the first wave? What lessons can be learned from areas with low infection rates? How important are built structures as drivers of COVID-19? How large is the effect of the socio-economic situation on COVID-19 infections? We conclude that it is of great importance to provide access to and analyse fine-resolution data to be able to understand the spread of the disease and address tailored health measures in urban settings.

Item Type:	Article
Uncontrolled Keywords:	covid-19,infectious disease,spatial relative risk,kernel density,point data,modifiable areal unit problem,sdg 3 - good health and well-being ,/dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_being
Faculty \ School:	Faculty of Science > School of Environmental Sciences
Depositing User:	LivePure Connector
Date Deposited:	30 Jan 2024 03:48
Last Modified:	30 Jan 2024 03:48
URI:	https://ueaeprints.uea.ac.uk/id/eprint/94324
DOI:	10.3390/ijerph20105830

Downloads

Actions (login required)

View Item