Environmental drivers of rabies in the Volga region of Russia: application of the maxent model.

Introduction: Understanding the dynamics of rabies virus spread in wild populations is essential for experts working to developing strategies to that protect ecosystems and prevent conflicts between wild and domestic animals. This is particularly important in the context of increasing human-wildlife interactions. Predictive modeling serves as a valuable tool for understanding and managing rabies in a given region. Such models not only aid in the prevention of outbreaks but also help optimize resource allocation for disease control and surveillance. Investigating abiotic factors that influence the incidence of rabies can further enhance the effectiveness of management strategies and reduce the associated risks to humans, livestock, and wildlife. Materials and methods: The aim of this study was to model rabies outbreaks and predict areas at high risk of new outbreaks among wild animals, based on climatic, landscape, and socio-demographic risk factors. To identify high-risk areas for rabies in wild animals using the ecological niche modeling approach, a dataset was compiled that included records of rabies outbreaks, as well as climatic and socio-demographic variables, including fox population density in the Volga region of the Russian Federation. Results: As a result, an ecological niche model for rabies outbreaks among wild animals was developed, incorporating the most significant variables for the region, with an accuracy of AUC = 0.85. Among the analyzed factors, climatic and landscape variables were found to be the most influential in determining the spread of rabies in wild populations. The most significant predictors included average annual temperature, population density, temperature seasonality, soil type, isothermality, and vegetation type. The model predicts that regions such as Nizhny Novgorod Oblast, the Republic of Mordovia, the Republic of Chuvashia, Penza Oblast, Saratov Oblast, and Samara Oblast are at high risk of rabies spread among wild animals. Conclusion: Thus, using ecological niche modeling, key risk factors for rabies were identified, and a geographical zoning of the Volga region was performed according to the level of risk of rabies transmission in wild animal populations. This spatial delineation has fundamentally transformed the approach to rabies management. Instead of applying uniform measures across the entire region, veterinary services can now implement a targeted strategy. This includes prioritizing intensifying wildlife surveillance in these areas, thereby optimizing the use of limited resources and enhancing the overall effectiveness of rabies control programs.