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Regional Characteristics of the Second Wave of SARS-CoV-2 Infections and COVID-19 Deaths in Germany

Author

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  • Gabriele Doblhammer

    (Department of Economics and Social Sciences, Institute for Sociology and Demography, University of Rostock, 18057 Rostock, Germany
    German Center for Neurodegenerative Diseases, 53127 Bonn, Germany)

  • Daniel Kreft

    (Department of Economics and Social Sciences, Institute for Sociology and Demography, University of Rostock, 18057 Rostock, Germany
    German Center for Neurodegenerative Diseases, 53127 Bonn, Germany)

  • Constantin Reinke

    (Department of Economics and Social Sciences, Institute for Sociology and Demography, University of Rostock, 18057 Rostock, Germany)

Abstract

(1) Background: In the absence of individual level information, the aim of this study was to identify the regional key features explaining SARS-CoV-2 infections and COVID-19 deaths during the upswing of the second wave in Germany. (2) Methods: We used COVID-19 diagnoses and deaths from 1 October to 15 December 2020, on the county-level, differentiating five two-week time periods. For each period, we calculated the age-standardized COVID-19 incidence and death rates on the county level. We trained gradient boosting models to predict the incidence and death rates by 155 indicators and identified the top 20 associations using Shap values. (3) Results: Counties with low socioeconomic status (SES) had higher infection and death rates, as had those with high international migration, a high proportion of foreigners, and a large nursing home population. The importance of these characteristics changed over time. During the period of intense exponential increase in infections, the proportion of the population that voted for the Alternative for Germany (AfD) party in the last federal election was among the top characteristics correlated with high incidence and death rates. (4) Machine learning approaches can reveal regional characteristics that are associated with high rates of infection and mortality.

Suggested Citation

  • Gabriele Doblhammer & Daniel Kreft & Constantin Reinke, 2021. "Regional Characteristics of the Second Wave of SARS-CoV-2 Infections and COVID-19 Deaths in Germany," IJERPH, MDPI, vol. 18(20), pages 1-18, October.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:20:p:10663-:d:654000
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    References listed on IDEAS

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    1. Sven Drefahl & Matthew Wallace & Eleonora Mussino & Siddartha Aradhya & Martin Kolk & Maria Brandén & Bo Malmberg & Gunnar Andersson, 2020. "A population-based cohort study of socio-demographic risk factors for COVID-19 deaths in Sweden," Nature Communications, Nature, vol. 11(1), pages 1-7, December.
    2. Martin Lange & Ole Monscheuer, 2022. "Spreading the disease: Protest in times of pandemics," Health Economics, John Wiley & Sons, Ltd., vol. 31(12), pages 2664-2679, December.
    3. Diehl, Claudia & Wolter, Felix, 2020. "End the lockdown? Why restrictions are being lifted too fast for some and not fast enough for others [Raus aus dem Lockdown? Warum es manchen zu schnell und anderen nicht schnell genug geht]," Policy Papers 03, University of Konstanz, Cluster of Excellence "The Politics of Inequality. Perceptions, Participation and Policies".
    4. Müller, Stephan & Rau, Holger A., 2021. "Economic preferences and compliance in the social stress test of the COVID-19 crisis," Journal of Public Economics, Elsevier, vol. 194(C).
    5. Anton Gollwitzer & Cameron Martel & William J. Brady & Philip Pärnamets & Isaac G. Freedman & Eric D. Knowles & Jay J. Van Bavel, 2020. "Partisan differences in physical distancing are linked to health outcomes during the COVID-19 pandemic," Nature Human Behaviour, Nature, vol. 4(11), pages 1186-1197, November.
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    Cited by:

    1. Boris Kauhl & Jörg König & Sandra Wolf, 2023. "Spatial Distribution of COVID-19 Hospitalizations and Associated Risk Factors in Health Insurance Data Using Bayesian Spatial Modelling," IJERPH, MDPI, vol. 20(5), pages 1-10, February.

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