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德国新冠病毒疫情的时空模式

Spatio-Temporal Patterns of the SARS-CoV-2 Epidemic in Germany.

作者信息

Diebner Hans H

机构信息

Department of Medical Informatics, Biometry and Epidemiology, Ruhr-Universität Bochum, 44780 Bochum, Germany.

出版信息

Entropy (Basel). 2023 Jul 29;25(8):1137. doi: 10.3390/e25081137.

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 shows 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 of 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.

摘要

本文展示了一项探索性研究的结果,该研究揭示了德国SARS-CoV-2/COVID-19疫情的时空模式。我们摒弃了有争议的模型假设,展示了疫情动态的内在时空模式。分析基于德国联邦政府公共卫生研究所(RKI)提供的供公众使用的COVID-19发病率数据,这些数据按年龄分层并在县级进行空间解析。尽管400个与县相关的发病率时间序列在时间特征和空间分布方面都表现出巨大的异质性,但各县的发病率曲线形成了与东德和西德相吻合的明显不同的集群。分析基于降维、多维缩放、网络分析和多样性度量。动态变化通过差分方法来捕捉,这些方法与有效繁殖数的倍数变化相关。与年龄相关的动态模式表明,儿童、青少年和老年人对疫情活动的影响比先前预期的要大得多。除了这些具体解释外,这项工作主要旨在提供一个疫情时空模式图谱,作为不同学科(特别是社会学和政策制定者)进一步探索的基础。该研究也应被视为在应对COVID-19大流行异常复杂性方面的一项方法学贡献。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/30d1/10453630/2bb7687f076f/entropy-25-01137-g001.jpg

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