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

ABSTRACT:

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 20

SUBMITTER: Kelly PH 

PROVIDER: S-EPMC11908066 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature