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欧洲COVID-19的时空动态:时间序列集群地图5个不同的轨迹到空间模式

Sarah Habershon¹, Kolja Nenoff², Guido Kraemer²

¹Institute for Earth System Sciences and Remote Sensing, Talstraße 35, 04103, Leipzig, Germany. sarah.habershon@uni-leipzig.de.

Population health metrics

|August 6, 2025

概括

整个欧洲的COVID-19大流行轨迹各不相同. 过度死亡率的时间序列聚类揭示了五种不同的模式,西欧显示同心冲击,东欧显示均的动态.

科学领域:

流行病学流行病学
公共卫生公共卫生
空间分析空间分析

背景情况:

COVID-19大流行对欧洲的影响不均,感染和死亡激增的区域差异很大.
汇总的国家级数据掩盖了流行病的复杂的次国家和跨国时空动态.
之前的研究集中在国家间的比较或国家内部的次国家差异上,在全面的欧洲范围的次国家分析中留下了一个空白.

研究的目的:

综合描述欧洲各地COVID-19流行病的次国家和跨国动态.
使用超局部化数据识别不同的流行病轨迹及其空间模式.

主要方法:

应用时间序列聚类到每周的过度死亡率估计.
利用了来自27个欧洲国家的国家以下NUTS3行政区域的数据.
分析了与已识别的流行病轨迹相关的空间模式.

主要成果:

在欧洲次国家区域中确定了五种不同的COVID-19流行病轨迹.
这些轨迹映射到特定的空间模式,揭示了流行病影响的地理变化.
发现了两个主要的轨迹子组,区分东欧和西欧的流行病动态.
在西欧观察到同心的死亡率影响模式,在东欧观察到内部均的动态.

结论:

过度死亡率的时间序列聚类有效地捕捉了欧洲各地的次国家级流行病动态.
独特的空间和时间轨迹凸显了流行病演变的区域差异.
结果表明,观察到的模式可能有解释,例如,东欧首次重大死亡浪潮的推迟.

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