Article
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Spatio-Temporal Patterns of the SARS-CoV-2 Epidemic in Germany
Version 1
: Received: 21 June 2023 / Approved: 22 June 2023 / Online: 22 June 2023 (12:45:04 CEST)
A peer-reviewed article of this Preprint also exists.
Diebner, H.H. Spatio-Temporal Patterns of the SARS-CoV-2 Epidemic in Germany. Entropy 2023, 25, 1137. Diebner, H.H. Spatio-Temporal Patterns of the SARS-CoV-2 Epidemic in Germany. Entropy 2023, 25, 1137.
Abstract
Results from an explorative study revealing spatio-temporal patterns of the SARS-CoV-2/COVID-19 epidemic in Germany are presented. We dispense with contestable model assumptions and show the intrinsic spatio-temporal patterns of the epidemic dynamics. The analysis is based on COVID-19 incidence data, which are age-stratified and spatially resolved at the county level, provided by the Federal Government’s Public Health Institute of Germany (RKI) for public use. Although the 400 county-related incidence time series show enormous heterogeneity both with respect to temporal features as well as spatial distributions, the counties’ incidence curves organise into well distinguished clusters that coincide with East and West Germany. The analysis is based on dimensionality reduction, multidimensional scaling, network analysis, and diversity measures. Dynamical changes are captured by means of difference-in-difference methods which are related to fold changes of the effective reproduction numbers. The age-related dynamical patterns suggest a considerably stronger impact of children, adolescents and seniors on the epidemic activity than previously expected. Besides these concrete interpretations, the work mainly aims at providing an atlas for spatio-temporal patterns of the epidemic which serves as a basis to be further explored with the expertise from different disciplines, particularly sociology and policy makers. The study should also be understood as a methodological contribution to getting a handle on the unusual complexity of the COVID-19 pandemic.
Keywords
COVID-19; SARS-CoV-2; Epidemic Spatial Heterogeneity; Diversity; Spatio-Temporal Patterns; Cluster Analysis
Subject
Biology and Life Sciences, Other
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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