Peste des petits ruminants (PPR) is a viral transboundary disease of small ruminants that causes significant damage to agriculture. This disease has not been previously registered in the Republic of Kazakhstan (RK). This paper presents an assessment of the susceptibility of the RK’s territory to the spread of the disease in the event of its importation from infected countries. The Generalized Linear Negative Binomial regression model that was trained on the PPR outbreaks in China was used to rank municipal districts in the RK in terms of PPR spread risk. The outbreaks count per administrative district was used as a risk indicator, while a number of socio-economic, landscape and climatic factors were considered as explanatory variables. Summary road length, altitude, the density of small ruminants, the maximum green vegetation fraction, cattle density and the Engel coefficient were the most significant factors. The model demonstrated a good performance in training data (R 2 = 0.69) and was transferred to the RK, suggesting a significantly lower susceptibility of this country to the spread of PPR. Hot Spot analysis identified three clusters of districts at the highest risk, located in the western, eastern and southern parts of Kazakhstan. As part of the study, a countrywide survey was conducted to collect data on the distribution of livestock populations, which resulted in the compilation of a complete geo-database of small ruminant holdings in the RK. The research results may be used to formulate a national strategy for preventing the importation and spread of PPR in Kazakhstan through targeted monitoring in high-risk areas.

Peste des petits ruminants (PPR) is a viral transboundary disease of small ruminants that causes significant damage to agriculture. The disease has not been previously registered in the Republic of Kazakhstan (RK). This paper presents an assessment of the susceptibility of the RK territory to the spread of this disease in case of its importation from infected countries. Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models trained on the PPR outbreaks in China were used to rank municipal districts of the RK in terms of the risk of PPR spread. Spatial density of outbreaks was used as a risk indicator while a number of socio-economic, landscape and climatic indicators were considered as explanatory variables. The Exploratory Regression tool was used to reveal a best combination of independent variables based on specified thresholds of R-squared, variables’ multicollinearity and residuals’ normality and autocorrelation. The small ruminants’ density, the maximum green vegetation fraction, the annual mean temperature, the road length and density as well as the cattle density were the most significant factors. Both OLS and GWR demonstrated nearly similar model performance providing a global adjusted R-squared of 0.61. Applied to the RK, the models show the greatest risk of PPR spread in the south-eastern and northern regions of the country, especially within Almaty, Zhambyl, Turkistan, West Kazakhstan and East Kazakhstan regions. As part of the study, a country-wise survey was carried out to collect data on the distribution of livestock population the RK, which resulted in compiling a complete geo-database of small ruminants’ holdings in the country. The research results can be used to form a national strategy for the prevention of the importation and spread of PPR in Kazakhstan through targeted monitoring in high-risk areas.