Departament d'Economia (ECO-SOS), Universitat Rovira i Virgili, Av. Universitat, 1, 43204 Reus, Catalonia, Spain.
Departament de Geografia, Universitat Rovira i Virgili, C. Joanot Martorell, 15, 43480 Vila-seca, Catalonia, Spain.
J Public Health (Oxf). 2021 Sep 22;43(3):455-461. doi: 10.1093/pubmed/fdaa238.
This paper concerns the spatial determinants of the first two waves of COVID-19 at the neighbourhood level.
Using data for the first and second waves of COVID-19 at the neighbourhood level in Barcelona, we analyse whether local characteristics acted in the same way during the two waves and identify typologies of areas depending on such determinants. Univariate and bivariate local Moran's I and count data models are used.
Some structural effects at the neighbourhood level consistently either boost (e.g. population density) or reduce (e.g. income) COVID-19 cases. Other effects differ between the two waves (i.e. age composition, schools and transport infrastructures).
Since certain characteristics influenced the virus diffusion in opposite ways between the two pandemic waves, territorial heterogeneity alone is insufficient to explain COVID-19 outbreaks-individual behaviour also needs to be factored in. Consequently, both econometric and spatial analysis techniques are recommended for tracking the spatiotemporal spread of this disease and for monitoring the effectiveness of policy measures across heterogeneous neighbourhoods.
本文关注了 COVID-19 在前两波疫情在社区层面的空间决定因素。
本研究使用了巴塞罗那社区层面 COVID-19 前两波的数据,分析了在这两波疫情中,局部特征是否以相同的方式发挥作用,并根据这些决定因素确定了区域的类型。使用了单变量和双变量局部 Moran's I 和计数数据模型。
一些社区层面的结构效应一致地促进(如人口密度)或减少(如收入)COVID-19 病例。其他效应在两波疫情之间有所不同(即年龄构成、学校和交通基础设施)。
由于某些特征在两波疫情中对病毒传播的影响相反,仅靠地域异质性不足以解释 COVID-19 疫情爆发——还需要考虑个人行为。因此,建议使用计量经济学和空间分析技术来跟踪这种疾病的时空传播,并监测不同社区的政策措施的有效性。