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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\u2019s 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.<\/jats:p>","DOI":"10.1038\/s41598-025-28331-9","type":"journal-article","created":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T09:45:55Z","timestamp":1764841555000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Bayesian spatio-temporal modeling of COVID-19 incidence in Algerian provinces using integrated nested Laplace approximations"],"prefix":"10.1038","volume":"15","author":[{"given":"Ayoub","family":"Asri","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,4]]},"reference":[{"key":"28331_CR1","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.ijbiomac.2022.01.118","volume":"204","author":"RM El-Shabasy","year":"2022","unstructured":"El-Shabasy, R. 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