Exploring the Spatial Relative Risk of COVID-19 in Berlin-Neukölln

Christoph Lambio (), Tillman Schmitz, Richard Elson, Jeffrey Butler, Alexandra Roth, Silke Feller, Nicolai Savaskan and Tobia Lakes
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Christoph Lambio: Geography Department, Applied Geoinformation Science Lab, Humboldt-University Berlin, 10099 Berlin, Germany
Tillman Schmitz: Geography Department, Applied Geoinformation Science Lab, Humboldt-University Berlin, 10099 Berlin, Germany
Richard Elson: UK Health Security Agency, 61, Colindale Avenue, London NW9 5EQ, UK
Jeffrey Butler: Geography Department, Applied Geoinformation Science Lab, Humboldt-University Berlin, 10099 Berlin, Germany
Alexandra Roth: Local Health Department Berlin-Neukölln, Gesundheitsamt Neukölln, Blaschkoallee 32, 12359 Berlin, Germany
Silke Feller: Local Health Department Berlin-Neukölln, Gesundheitsamt Neukölln, Blaschkoallee 32, 12359 Berlin, Germany
Nicolai Savaskan: Local Health Department Berlin-Neukölln, Gesundheitsamt Neukölln, Blaschkoallee 32, 12359 Berlin, Germany
Tobia Lakes: Geography Department, Applied Geoinformation Science Lab, Humboldt-University Berlin, 10099 Berlin, Germany

IJERPH, 2023, vol. 20, issue 10, 1-22

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.

Keywords: COVID-19; infectious disease; spatial relative risk; kernel density; point data; modifiable areal unit problem (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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