Borna disease (BD) is a severe endemic and fatal disorder caused by the neurotropic Borna disease virus (BDV) which mainly occurs in horses and sheep. Borna disease virus belongs to the order Mononegavirales, which includes many reservoir-bound viruses with high zoonotic and pathogenic properties including the filoviruses and lyssaviruses. Clinically manifest BD occurs in endemic areas of Germany, Switzerland, Liechtenstein, and Austria. A seasonal accumulation of cases in spring and summer, incidences that vary from year to year, and the recent detection of BDV in bicolored shrews (Crocidura leucodon) in Swiss endemic areas argue for a natural reservoir. We established a geographic information system analysis of the distribution of 485 equine BD cases in Bavarian (Germany) endemic areas and of the occurrence of 285 records of C. leucodon captured in Bavaria. Boosted regression trees were used to identify driving factors of habitat choice and virus prevalence. The distribution model of C. leucodon and the prevalence model for BDV had very good accuracy. Mean annual precipitation <900 mm, mean annual temperatures of 8 C, elevation <350 m, low forest cover, and a high percentage of urban fabric and arable land describe the optimal habitat for C. leucodon. Occurrence probability of C. leucodon was significantly higher in Bavarian BDV-endemic areas than in random areas in Bavaria. The prevalence of BD was higher in urban areas with annual mean precipitation of 800–900 mm, annual mean temperature of 8 C, and elevation >500 m. Our results indicate that the distribution model can accurately predict BD occurrence. Based on these results, practical safety precautions could be derived. The BDV model represents a suitable system for reservoir-bound, neurotropic Mononegavirales because it allows analyzing ecologic and biologic aspects that determine virus abundance, maintenance in reservoir species, and transmission to end host species.