Scholarlink

<p>The COVID-19 pandemic in Algeria dissplayed significant spatial and temporal heterogeneity, especially during the severe summer 2021 wave driven by the Delta variant. Standard national-level statistics often obscure this critical local variation, creating a need for advanced modeling to inform precise public health interventions. This study aimed to perform a high-resolution spatio-temporal analysis of COVID-19 incidence across Algeria’s 48 provinces (Wilayas) to identify persistent high-risk areas and track the dynamics of viral spread. A spatio-temporal analysis was conducted on COVID-19 case data from all 48 Wilayas during epidemiological weeks 26-37 of 2021. We employed a Bayesian hierarchical model fitted using the Integrated Nested Laplace Approximation (INLA). The model incorporated structured spatial (Leroux prior), temporal (random walk of order 1), and spatio-temporal (Type IV interaction) random effects. Model selection was performed using the Watanabe-Akaike Information Criterion (WAIC) and Deviance Information Criterion (DIC) The spatio-temporally structured interaction model provided the best fit. Spatial heterogeneity was the dominant driver of transmission risk, accounting for 83.4% of the explained variance. Northeastern Wilayas, including Constantine and Tebessa, exhibited persistently high relative risks. The national temporal trend showed a sharp peak in early August 2021. The spatio-temporal interaction term (16.5% of variance) captured the progressive westward spread of the virus along the northern coast throughout the study period. This analysis demonstrates the critical utility of Bayesian spatio-temporal models in moving beyond national averages to identify specific high-risk areas and understand the evolving dynamics of an epidemic. The findings provide a valuable evidence base for designing targeted public health strategies. While this foundational study establishes the spatio-temporal risk patterns, future work incorporating socio-economic and environmental covariates will be essential to elucidate the underlying drivers of transmission.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Bayesian spatio-temporal modeling of COVID-19 incidence in Algerian provinces using integrated nested Laplace approximations

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月4日

查看全文
  • Ayoub Asri

摘要

The COVID-19 pandemic in Algeria dissplayed significant spatial and temporal heterogeneity, especially during the severe summer 2021 wave driven by the Delta variant. Standard national-level statistics often obscure this critical local variation, creating a need for advanced modeling to inform precise public health interventions. This study aimed to perform a high-resolution spatio-temporal analysis of COVID-19 incidence across Algeria’s 48 provinces (Wilayas) to identify persistent high-risk areas and track the dynamics of viral spread. A spatio-temporal analysis was conducted on COVID-19 case data from all 48 Wilayas during epidemiological weeks 26-37 of 2021. We employed a Bayesian hierarchical model fitted using the Integrated Nested Laplace Approximation (INLA). The model incorporated structured spatial (Leroux prior), temporal (random walk of order 1), and spatio-temporal (Type IV interaction) random effects. Model selection was performed using the Watanabe-Akaike Information Criterion (WAIC) and Deviance Information Criterion (DIC) The spatio-temporally structured interaction model provided the best fit. Spatial heterogeneity was the dominant driver of transmission risk, accounting for 83.4% of the explained variance. Northeastern Wilayas, including Constantine and Tebessa, exhibited persistently high relative risks. The national temporal trend showed a sharp peak in early August 2021. The spatio-temporal interaction term (16.5% of variance) captured the progressive westward spread of the virus along the northern coast throughout the study period. This analysis demonstrates the critical utility of Bayesian spatio-temporal models in moving beyond national averages to identify specific high-risk areas and understand the evolving dynamics of an epidemic. The findings provide a valuable evidence base for designing targeted public health strategies. While this foundational study establishes the spatio-temporal risk patterns, future work incorporating socio-economic and environmental covariates will be essential to elucidate the underlying drivers of transmission.