Publication date: Jul 04, 2026
The COVID-19 pandemic has demonstrated the critical importance of understanding how infectious diseases spread across space and time, as transmission patterns often vary considerably between regions. Despite the significant impact of COVID-19 in Saudi Arabia, limited research has comprehensively examined the evolution of its spatial clustering and hotspot dynamics at the provincial level throughout different phases of the pandemic. This study investigates the spatiotemporal dynamics of COVID-19 across Saudi Arabia’s administrative provinces from March 2020 to August 2022 using an integrated spatial autocorrelation and hotspot analysis approach. Spatiotemporal patterns were examined using Global and Local Moran’s I, Getis-Ord Gi* hotspot analysis, and clustering techniques to identify statistically significant spatial clusters, coldspots, and outliers, as well as their temporal evolution across distinct phases of the pandemic. The results reveal that COVID-19 transmission in Saudi Arabia was highly non-random and characterized by strong spatial dependence. Persistent hotspots were consistently identified in the Riyadh Region and the Eastern Province, reflecting the influence of population density, economic activity, and mobility networks. In contrast, southwestern provinces such as Asir, Jazan, and Al-Baha repeatedly emerged as coldspots, suggesting that geographic isolation and lower population density limited widespread transmission. The study demonstrates the effectiveness of integrating spatiotemporal analysis with spatial autocorrelation methods for understanding pandemic dynamics. The proposed framework provides valuable insights for identifying high-risk areas, optimizing resource allocation, and supporting spatially targeted interventions. This approach offers a transferable model for enhancing epidemic surveillance and preparedness in Saudi Arabia and similar geographic contexts.
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| Concepts | Keywords |
|---|---|
| August | COVID-19 |
| Economic | Hot spot analysis |
| Moran | Public health intervention |
| Pandemic | Saudi Arabia |
| Saudi | Spatial autocorrelation |
Semantics
| Type | Source | Name |
|---|---|---|
| disease | MESH | COVID-19 |
| disease | MESH | infectious diseases |
| pathway | REACTOME | Reproduction |
| disease | MESH | included |
| disease | MESH | severe acute respiratory syndrome |
| disease | MESH | infections |
| pathway | KEGG | Coronavirus disease |
| disease | MESH | death |
| drug | DRUGBANK | Aspartame |
| disease | MESH | Dis |
| disease | MESH | Dengue |
| drug | DRUGBANK | Diethylstilbestrol |
| disease | MESH | Des |
| disease | MESH | Plan |
| drug | DRUGBANK | Medical air |
| disease | MESH | Sens |