Department of Rural Geography and Local Development, Institute of Geography and Spatial Organization, Polish Academy of Sciences, Twarda Str. 51/55, 00-818 Warsaw, Poland.
Department of Socio-Economic Geography, Institute of Geography and Earth Sciences, Jan Kochanowski University of Kielce, Uniwersytecka Str. 7, 25-406 Kielce, Poland.
Int J Environ Res Public Health. 2024 Oct 10;21(10):1342. doi: 10.3390/ijerph21101342.
The aim of this paper is to assess the influence of selected geographical factors on the diversity of the development of the COVID-19 pandemic in Europe's regions, and on its dynamics across the continent. The work took into account 250 of NUTS-2 regions. The datasets included the course of the COVID-19 pandemic (two dependent variables), intervening actions (four variables of the research background), and potential environmental and socio-economic conditioning (twelve independent variables). The dependent variables' set was composed of two indexes: morbidity and temporal inertia. The temporal scope of the research was 23 March 2020-15 May 2022, with weekly resolution. By means of multiple linear regression model, the influence of the administrative actions and of the selected natural and socio-economic factors was assessed. Finally, a synthetic Regional Epidemic Vulnerability Index (REVI) for each individual region was calculated. It allowed us to classify the regions into three categories: resistant, neutral, or sensitive. REVI's spatial distribution indicates that the zone of above-average vulnerability occurred in the western part of Europe and around the Alps. Therefore, focus ought to extend beyond regional statistics, towards spatial relationships, like contiguous or transit position. This research also validated the strong impact of national borders.
本文旨在评估选定的地理因素对欧洲地区 COVID-19 大流行发展多样性的影响，以及对整个欧洲大陆疫情动态的影响。本研究考虑了 250 个 NUTS-2 地区。数据集包括 COVID-19 大流行的进程（两个因变量）、干预措施（研究背景的四个变量）和潜在的环境和社会经济条件（十二个自变量）。因变量集由两个指数组成：发病率和时间惯性。研究的时间范围是 2020 年 3 月 23 日至 2022 年 5 月 15 日，分辨率为每周。通过多元线性回归模型，评估了行政措施和选定的自然及社会经济因素的影响。最后，为每个地区计算了综合区域疫情脆弱性指数（REVI）。这使我们能够将地区分为三类：抵抗、中立或敏感。REVI 的空间分布表明，脆弱性较高的地区出现在欧洲西部和阿尔卑斯山周围。因此，关注的焦点不应仅局限于区域统计数据，还应扩展到空间关系，如毗邻或过境位置。这项研究还验证了国家边界的强大影响。
