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

Author

Listed:
  • 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
    School of Environmental Sciences, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, 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
    IRI THESys, Integrative Research Institute on Transformations of Human-Environment Systems, Humboldt-Universität zu Berlin, 10099 Berlin, Germany)

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.

Suggested Citation

  • Christoph Lambio & Tillman Schmitz & Richard Elson & Jeffrey Butler & Alexandra Roth & Silke Feller & Nicolai Savaskan & Tobia Lakes, 2023. "Exploring the Spatial Relative Risk of COVID-19 in Berlin-Neukölln," IJERPH, MDPI, vol. 20(10), pages 1-22, May.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:10:p:5830-:d:1147827
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    References listed on IDEAS

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