Bioclimatic modelling of the spread of Dirofilaria spp. in Europe, with a special focus on Ukraine

Background: The zoonotic disease dirofilariosis, caused by Dirofilaria spp., is expanding geographically in Europe, a phenomenon increasingly linked to climate change. Understanding the environmental drivers of this spread is crucial for surveillance and public health planning. Objective: This study aims to model the ecological niche of Dirofilaria spp. in Europe, identify key climatic drivers, and map areas of high transmission risk, with a specific focus on Ukraine. Methods: We employed a Species Distribution Modeling (SDM) framework, using the Maxent algorithm correlated with high-quality occurrence data and climatic predictors from the CMCC-BioClimInd dataset. Model interpretation was enhanced using SHAP (SHapley Additive exPlanations) to identify and rank influential variables. A complementary Growing Degree Days (GDD) model was used to validate the thermal constraints on parasite development. Results: The model achieved high predictive performance (AUC = 0.75, Boyce index = 0.93). At the European scale, the modified Kira warmth index, a measure of cumulative warmth, was the most important predictor, exhibiting a unimodal response curve that reveals a thermal optimum for transmission. For Ukraine, annual mean temperature and winter cold minima were the dominant drivers, reflecting the country's continental climate. GDD analysis confirmed a significant increase in thermally suitable areas in Ukraine from 2004-2024, indicating a likely northward expansion of transmission risk. Conclusion: Climate, particularly temperature, is the primary determinant of Dirofilaria distribution. The identified unimodal response to cumulative warmth refines future risk projections, suggesting that while warming facilitates spread in cooler regions, it may reduce suitability in already-warm areas. The provided high-resolution risk maps for Ukraine offer a critical tool for targeting surveillance and control efforts.