Comparative Spatiotemporal Analysis of Global HIV-1 Subtype C Hotspots: Applying Bayesian Hierarchical Modeling, SaTScan, and Getis-Ord Gi* Statistics

Background: Despite the global importance of HIV-1 subtype C, a global-scale GIS characterization of its geographic clustering and the temporal persistence of hotspots is lacking, and systematic cross-method comparisons are scarce. Methods: We assembled 2,220 country-year observations from 111 countries between 2005 and 2024, comprising 161,025 subtype C sequences, and generated internally standardized expected counts. We compared hotspot detection using Getis-Ord Gi-star statistics in ArcGIS, SaTScan space-time scan statistics, and Bayesian hierarchical models with spatiotemporal smoothing, and quantified temporal persistence and cross-model concordance. Results: Documented subtype C sequences showed increasing geographic concentration over time, shifting from relatively widespread detection toward progressively localized clustering, with the strongest and intensifying concentration in Southern Africa. SaTScan and Bayesian models identified fewer hotspots but showed greater temporal stability, whereas Gi-star detected more localized and short-term spatial fluctuations. High-stability hotspots with sustained multi-year detection were predominantly located in Southern Africa. Zimbabwe was the only country classified as a high-stability hotspot across all three frameworks; Eswatini, Botswana, Malawi, and South Africa showed high stability in at least two models, indicating robust, model-consistent persistence. Conclusions: Integrating complementary hotspot methods reveals both convergent and method-specific patterns and provides a quantitative basis to prioritize long-term persistence for targeted surveillance, resource allocation, and precision prevention.