Spatiotemporal Clustering of Animal Rabies in Kazakhstan: Insights from ArcGIS Pro-Based Analysis

Background and Aim. Rabies is a zoonotic viral disease that continues to pose a major public health threat worldwide, particularly in regions with limited access to vaccination and disease monitoring. Kazakhstan remains endemic for animal rabies, with variable patterns across geography and species. The objective of this study is to identify spatial and temporal clusters of rabies outbreaks among animals in Kazakhstan from 2013 to May 2025, using GIS-based methods to inform regional control and prevention strategies. Materials and Methods. This study applied three geospatial tools within ArcGIS ProHot Spot Analysis using Getis-Ord Gi*, Kernel Density Estimation (KDE), and Space-Time Kernel Density to a national dataset of confirmed rabies cases among animals. The database included geographic coordinates, species type, dates of confirmation, and number of infected animals. KDE and Getis-Ord Gi* were used to detect spatial clusters and density gradients, while Space-Time Kernel Density enabled tracking of outbreak patterns over time. Results. Hot Spot Analysis identified high-risk zones in northeastern (North Kazakhstan, Pavlodar oblasts) and western regions (Aktobe oblast), potentially linked to wild animal migration and insufficient vaccination coverage. KDE revealed additional high-density areas in southern and eastern regions. Space-time analysis showed persistent clusters in Zhambyl, Kostanay oblasts, and western Kazakhstan, while central regions exhibited low density likely due to geographic and demographic isolation. Conclusion. Spatial and spatiotemporal analyses demonstrated that rabies outbreaks in Kazakhstan are not randomly distributed but form distinct species- and region specific clusters. These insights support the need for differentiated veterinary approaches. Timely and geographically targeted vaccination programs particularly in identified hot spots are critical to reducing the incidence of rabies.