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贝叶斯时空分析加泰罗尼亚的 COVID-19 疫情。

Bayesian spatio-temporal analysis of the COVID-19 pandemic in Catalonia.

机构信息

Biostatistics Support and Research Unit, Germans Trias i Pujol Research Institute and Hospital (IGTP), Badalona, Barcelona, Spain.

出版信息

Sci Rep. 2024 Feb 20;14(1):4220. doi: 10.1038/s41598-024-53527-w.

Abstract

In this study, we modelled the incidence of COVID-19 cases and hospitalisations by basic health areas (ABS) in Catalonia. Spatial, temporal and spatio-temporal incidence trends were described using estimation methods that allow to borrow strength from neighbouring areas and time points. Specifically, we used Bayesian hierarchical spatio-temporal models estimated with Integrated Nested Laplace Approximation (INLA). An exploratory analysis was conducted to identify potential ABS factors associated with the incidence of cases and hospitalisations. High heterogeneity in cases and hospitalisation incidence was found between ABS and along the waves of the pandemic. Urban areas were found to have a higher incidence of COVID-19 cases and hospitalisations than rural areas, while socio-economic deprivation of the area was associated with a higher incidence of hospitalisations. In addition, full vaccination coverage in each ABS showed a protective effect on the risk of COVID-19 cases and hospitalisations.

摘要

在这项研究中,我们通过基本卫生区域(ABS)对加泰罗尼亚的 COVID-19 病例和住院人数进行建模。使用允许从邻近地区和时间点借鉴优势的估计方法描述了空间、时间和时空发病率趋势。具体来说,我们使用了基于集成嵌套拉普拉斯逼近(INLA)的贝叶斯层次时空模型进行估计。进行了探索性分析以确定与病例和住院人数发病率相关的潜在 ABS 因素。在 ABS 之间以及在大流行的各个波次中发现病例和住院人数的发病率存在高度异质性。发现城市地区的 COVID-19 病例和住院人数发病率高于农村地区,而该地区的社会经济贫困与住院人数发病率较高有关。此外,每个 ABS 的完全疫苗接种覆盖率显示出对 COVID-19 病例和住院风险的保护作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/076c/10879174/06b0b5cb74e5/41598_2024_53527_Fig1_HTML.jpg

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