The article presents a predictive model to identify areas at high risk for tick‑borne encephalitis (TBE) in Poland. Using a combination of meteorological, environmental, and socio‑economic factors, the model identified regions with elevated TBE risk, including areas where no cases had previously been reported. We found that higher temperatures, increased precipitation, and proximity to forested areas were key factors associated with increased TBE risk. The study suggests that increasing awareness among physicians in these high‑risk regions and improving access to diagnostic testing could strengthen disease surveillance and prevention.
This analysis marked the culmination of nearly a decade of interdisciplinary work involving extensive data collection, expert collaboration, and numerous discussions. The model incorporated the best available data at the finest feasible resolution (NUTS‑5). Meteorological variables were developed using spatial interpolation methods by Professor Ustrnul and his team. Land‑use variables were prepared by the geographer Jakub Bratkowski based on data from the European project CORINE Land Cover 2006, while the remaining socio‑economic data were obtained from the Central Statistical Office. The predictive model itself was developed by a student of mathematics Basia Rubikowska and supervised by a brilliant statistician Magda Rosińska. We hoped that this modelling approach would be more routinely adopted by the national Public Health institutions.