A spatio-temporal predictive model inferring the year-to-year probability of occurrence of TBE human cases in Europe

BACKGROUND The number of tick-borne encephalitis (TBE) human cases reported in Europe has increased in recent years, peaking during the Covid-19 pandemic phase. To improve the capability to identify high-risk areas, we developed a spatio-temporal predictive model inferring the year-to-year probability of occurrence of TBE human cases in Europe. METHODS We used data provided by the European Surveillance System (TESSy, ECDC) to infer the distribution of TBE human cases at the regional (NUTS3) level during the period 2017-2021. We included variables related to temperature, precipitation, land cover and ticks’ hosts presence to account for the natural hazard of viral circulation. We also used indexes based on recorded intensities of human outdoor activity in forests as proxies of human exposure to tick bites. We identified the yearly probability of TBE occurrence using a boosted regression tree modeling framework. RESULTS Areas with higher probability for transmission were identified in Central-Eastern Europe and along the coastline of Nordic countries up to the Bothnian Bay. Our results highlighted a westbound and northbound spread of TBEpositive regions throughout the years. Areas at higher risks are characterized by the occurrence of key rodent reservoir and cervid species, intense human recreational activities in forests, steep drops in late summer temperatures and high annual precipitation amounts. The predictive accuracy of the model was assessed through internal and external validation (AUC = 0.81; CBI =0.98). CONCLUSIONS Our study provides an assessment of the European regions at risk of TBE human infections on a yearly basis. Our results can therefore be used for year-to-year disease risk mapping in support of surveillance and prevention campaigns within endemic and potential new risk areas.