Different environmental factors predict the occurrence of tick-borne encephalitis virus (TBEV) and reveal new potential risk areas across Europe via geospatial models.

BACKGROUND

Tick-borne encephalitis (TBE) is the most serious tick-borne viral disease in Europe. Identifying TBE risk areas can be difficult due to hyper focal circulation of the TBE virus (TBEV) between mammals and ticks. To better define TBE hazard risks and elucidate regional-specific environmental factors that drive TBEV circulation, we developed two machine-learning (ML) algorithms to predict the habitat suitability (maximum entropy), and occurrence of TBEV (extreme gradient boosting) within distinct European regions (Central Europe, Nordics, and Baltics) using local variables of climate, habitat, topography, and animal hosts and reservoirs.

METHODS

Geocoordinates that reported the detection of TBEV in ticks or rodents and anti-TBEV antibodies in rodent reservoirs in 2000 or later were extracted from published and grey literature. Region-specific ML models were defined via K-means clustering and trained according to the distribution of extracted geocoordinates relative to explanatory variables in each region. Final models excluded colinear variables and were evaluated for performance.

RESULTS

521 coordinates (455 ticks; 66 rodent reservoirs) of TBEV occurrence (2000-2022) from 100 records were extracted for model development. The models had high performance across regions (AUC: 0.72-0.92). The strongest predictors of habitat suitability and TBEV occurrence in each region were associated with different variable categories: climate variables were the strongest predictors of habitat suitability in Central Europe; rodent reservoirs and elevation were strongest in the Nordics; and animal hosts and land cover contributed most to the Baltics. The models predicted several areas with few or zero reported TBE incidence as highly suitable (≥ 60%) TBEV habitats or increased probability (≥ 25%) of TBEV occurrence including western Norway coastlines, northern Denmark, northeastern Croatia, eastern France, and northern Italy, suggesting potential capacity for locally-acquired autochthonous TBEV infections or possible underreporting of TBE cases based on reported human surveillance data.

CONCLUSIONS

This study shows how varying environmental factors drive the occurrence of TBEV within different European regions and identifies potential new risk areas for TBE. Importantly, we demonstrate the utility of ML models to generate reliable insights into TBE hazard risks when trained with sufficient explanatory variables and to provide high resolution and harmonized risk maps for public use.