Often presented as a global pandemic spreading all over the world, COVID-19, however, hit not only countries but also regions differently. The objective of this paper is to focus on the spatial heterogeneity in the spread of the COVID-19 pandemic and to contribute to an understanding of the channels by which it spread, focusing on the regional socioeconomic dimension. For this, we use a dataset covering 125 European regions in 12 countries. Considering that the impact of the COVID-19 crisis differed sharply not only across countries but also across regions within the same country, the empirical strategy is based, on the one hand, on an exploratory analysis of spatial autocorrelations, which makes it possible to identify regional clusters of the disease. On the other hand, we use spatial regression models to capture the diffusion effect and the role of different families of regional factors in this process. We find that the share of older people in the population, GDP per capita, distance from achieving EU objectives, and the unemployment rate are correlated with high COVID-19 death rates. In contrast, the number of medical practitioners and hospital beds and the level of social trust are correlated with low COVID-19 death rates.

然而，COVID-19 通常表现为一种在世界范围内蔓延的全球大流行病，它不仅对国家而且对地区的影响也不同。本文的目的是关注 COVID-19 大流行传播中的空间异质性，并有助于了解其传播的渠道，重点关注区域社会经济层面。为此，我们使用了覆盖 12 个国家的 125 个欧洲地区的数据集。考虑到 COVID-19 危机的影响不仅在国家之间而且在同一国家内的区域之间也存在巨大差异，因此实证策略一方面基于对空间自相关的探索性分析，这使得确定疾病的区域性聚集。另一方面，我们使用空间回归模型来捕捉扩散效应以及不同区域因素族在此过程中的作用。我们发现老年人在人口中的比例、人均 GDP、实现欧盟目标的距离以及失业率与 COVID-19 的高死亡率相关。相比之下，医生和医院床位的数量以及社会信任水平与低 COVID-19 死亡率相关。