Abstract
Avian influenza (AI) is a highly contagious viral disease affecting both domestic and wild birds, posing a significant threat to poultry farming worldwide. This study aims to analyze the key landscape and population factors associated with H7 avian influenza outbreaks across the Euro-Asian continent and to identify high-risk areas in Russia for the virus's introduction and subsequent spread. Two models were developed using the Maximum Entropy algorithm (MaxEnt): An introduction model predicting the likelihood of avian influenza presence based on climatic, landscape, wild waterfowl and semiaquatic bird population density data; and a spread model estimating outbreak risk in poultry farms using data on synanthropic birds, poultry flock density, and proximity to wild bird habitats. The first model was trained via maximum likelihood using data from H7 avian influenza outbreaks in Europe (Italy, Germany, France, Denmark, Lithuania, the Netherlands) and Southeast Asia (China, Hong Kong, Taiwan, Japan, Cambodia, North Korea, South Korea, Vietnam). The second model was trained using output from the first model. Specifically, areas with a predicted probability of H7 outbreak between 0.9 and 1.0 were used as occurrence points for the model in Russia. The results demonstrated that both models achieved high predictive reliability for avian influenza outbreaks in the Russian Federation: the introduction model (AUC = 0.855) and the spread model (AUC = 0.993). Areas with a high probability of disease occurrence were identified in the Central, Southern, North Caucasian, and Volga Federal Districts. These findings underscore the necessity of enhanced disease surveillance in these regions, as well as in the border areas of the Ural, Siberian, and Far Eastern Federal Districts. The authors recommend strengthening biosecurity measures, enhancing wild bird monitoring in high-risk areas, and maintaining stocks of relevant vaccines to timely contain the outbreaks.