COVID-19在欧洲的时空动态:时间序列聚类将5种不同的轨迹映射到空间格局。

IF 2.5 2区 医学 Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Sarah Habershon, Kolja Nenoff, Guido Kraemer, Lennart Schüler, Heinrich Zozmann, Justin M Calabrese, Sabine Attinger, Miguel D Mahecha
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引用次数: 0

摘要

2019冠状病毒病大流行对欧洲的影响不均匀,感染和死亡人数的激增在不同地区和时间段有所波动。高度局部化的热点和交错的时间线造成了强烈的、不同步的感染和死亡浪潮,扭曲了国家层面和累积数据,通过汇总掩盖了大流行的时空动态。尽管进行了广泛的研究,对各国进行了比较,并分析了个别国家内部的次国家差异,但尚未全面描述整个欧洲COVID-19大流行的详细次国家和跨国动态。在这里,我们表明,将时间序列聚类应用于欧洲27个国家的次国家级《NUTS3》行政区域的每周超额死亡率估计数,确定了与空间模式相对应的五种不同的大流行轨迹。轨迹包括两个亚组,代表了东欧和西欧不同的大流行动态。西欧的死亡率影响呈同心分布,二级和三级影响区围绕疫情中心。东欧表现出内部均匀的空间动态,可能是由于第一次主要死亡率浪潮的推迟。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

The spatiotemporal dynamics of COVID-19 in Europe: time-series clustering maps 5 distinct trajectories to spatial patterns.

The spatiotemporal dynamics of COVID-19 in Europe: time-series clustering maps 5 distinct trajectories to spatial patterns.

The spatiotemporal dynamics of COVID-19 in Europe: time-series clustering maps 5 distinct trajectories to spatial patterns.

The spatiotemporal dynamics of COVID-19 in Europe: time-series clustering maps 5 distinct trajectories to spatial patterns.

The spatiotemporal dynamics of COVID-19 in Europe: time-series clustering maps 5 distinct trajectories to spatial patterns.

The spatiotemporal dynamics of COVID-19 in Europe: time-series clustering maps 5 distinct trajectories to spatial patterns.

The spatiotemporal dynamics of COVID-19 in Europe: time-series clustering maps 5 distinct trajectories to spatial patterns.

The COVID-19 pandemic affected Europe unevenly, with surges in infections and deaths fluctuating across different regions and time periods. Hyper-localised hotspots and staggered timelines created intense, asynchronous waves of infections and deaths that distort country-level and cumulative data, obscuring the pandemic's spatiotemporal dynamics through aggregation. Despite extensive research comparing states and analysing subnational variance within individual countries, the detailed subnational and transnational dynamics of the COVID-19 pandemic across Europe as a whole have not been comprehensively described. Here we show that time-series clustering, applied to weekly excess mortality estimates for subnational NUTS3 administrative regions of 27 countries in Europe, identifies five distinct pandemic trajectories which map to spatial patterns. The trajectories comprise two subgroups, representing contrasting pandemic dynamics in eastern and western Europe. Western Europe exhibits concentric arrangements of mortality impact, with secondary and tertiary impact zones surrounding outbreak epicenters. Eastern Europe exhibits internally homogeneous spatial dynamics, possibly due to the deferral of the first major mortality wave.

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来源期刊
Population Health Metrics
Population Health Metrics PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-
CiteScore
6.50
自引率
0.00%
发文量
21
审稿时长
29 weeks
期刊介绍: Population Health Metrics aims to advance the science of population health assessment, and welcomes papers relating to concepts, methods, ethics, applications, and summary measures of population health. The journal provides a unique platform for population health researchers to share their findings with the global community. We seek research that addresses the communication of population health measures and policy implications to stakeholders; this includes papers related to burden estimation and risk assessment, and research addressing population health across the full range of development. Population Health Metrics covers a broad range of topics encompassing health state measurement and valuation, summary measures of population health, descriptive epidemiology at the population level, burden of disease and injury analysis, disease and risk factor modeling for populations, and comparative assessment of risks to health at the population level. The journal is also interested in how to use and communicate indicators of population health to reduce disease burden, and the approaches for translating from indicators of population health to health-advancing actions. As a cross-cutting topic of importance, we are particularly interested in inequalities in population health and their measurement.
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