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Article Abstract

Equine herpesvirus 1 (EHV-1) is one of the most dangerous viral diseases affecting ungulates, and is characterized by a wide range of clinical manifestations in horses, including rhinopneumonia, abortion, neonatal death, and myeloencephalopathy. It is well known for causing mass abortions in mares and respiratory diseases in young animals. Once introduced into a horse breeding farm of any type, EHV-1 tends to establish as a persistent infection. The disease is reported on nearly all continents and causes substantial annual economic losses to horse breeding operations. In Kazakhstan, 34 EHV-1 outbreaks were recorded between 2017 and 2024. The objective of our study was to identify potential risk factors associated with the presence of EHV-1 within the study area. We employed a forest-based classification and regression approach to explore a set of sociodemographic, environmental, and transportation-related factors associated with the presence or absence of EHV-1 at the level of administrative regions. A standard set of explanatory variables was supplemented with horse population density, derived from demographic data of horse-breeding farms obtained through a nationwide survey. Modeling results indicated that the most significant factor influencing EHV-1 presence was the average wind speed in January, followed by road density, the number of horse farms, and the number of livestock-related facilities targeted for surveillance. Horse population density was found to be among the least significant variable in the model. The resulting risk map highlights areas with a higher suitability for EHV-1 emergence, primarily located in regions with moderate-to-high horse population densities and characterized by steppe- and grassland-type landscapes, which are predominantly found in the northern, central, and south-western parts of Kazakhstan. These findings can serve as a foundation for further investigation into the spatial patterns of EHV-1 in the country and for enhancing veterinary surveillance and control measures.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12393953PMC
http://dx.doi.org/10.1155/tbed/5536099DOI Listing

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