Predicting the spatio-temporal risk of human tick-borne encephalitis (TBE) in Europe by combining hazard and exposure drivers.

Background: Tick-borne encephalitis (TBE), caused by tick-borne encephalitis virus (TBEV), is a zoonotic disease that can lead to severe neurological symptoms. Given the increasing number of reported human TBE cases in Europe, we developed a spatio-temporal predictive model to infer the year-to-year probability of human TBE occurrence across Europe at the regional and municipal administrative levels. Methods: We derived the distribution of human TBE cases at the regional level during 2017-2022 by using data provided by the European Centre for Disease Prevention and Control (ECDC), and at the municipal level by using data provided by Austria, Finland, Italy, Lithuania, and Slovakia. We modeled the probability of presence of human TBE cases at the regional and municipal levels for the period 2017-2025 with a boosted regression trees model, including covariates that affect both the natural hazard of virus circulation and human exposure to tick bites. Findings: Areas with the highest probability of human TBE infections are located in central-eastern Europe, the Baltic states, and along the coastline of Nordic countries. Our results highlight a statistically significant rising trend in human TBE risk not only in north-western, but also in south-western European countries. Such areas are characterised by the presence of key tick host species, forested areas, intense human activity in forests, steep drops in late summer temperatures and high precipitation amounts during the driest months. The model showed good predictive performance, with a mean AUC of 0.84 (SD = 0.03), sensitivity of 0.83 (SD = 0.01), and specificity of 0.80 (SD = 0.01) at the regional level, and a mean AUC of 0.82 (SD = 0.03), sensitivity of 0.83 (SD = 0.01), and specificity of 0.69 (SD = 0.01) at the municipal level. Interpretation: With ongoing climate and land use changes, the number of human TBE cases is likely to increase and spread into new areas. This highlights the importance of predictive models that can identify potential risk areas to support disease prevention and control efforts by public health authorities. The approach adopted, by fitting a One Health framework and leveraging lagged covaries, enables timely one-year-ahead predictions and enhances our current understanding of TBE risk under a global change scenario.