Past and future phenology changes of vector-borne zoonotic diseases under climate and land-use change

Tick-borne encephalitis (TBE) and West Nile virus (WNV) are zoonotic, vector-borne diseases of increasing public health concern across Europe, reflected in rising case numbers and the emergence of new areas of infection. Their transmission risk follows characteristic seasonal patterns, largely driven by the weather-dependent activity of their arthropod vectors. Consequently, climatic warming - particularly alongside land-use changes – has the potential to significantly alter the phenology and seasonal dynamics of TBE and WNV by shifting the geographic and temporal distribution of both vectors and viruses. In this study, we aim to assess the impacts of climate and land-use changes on the seasonal transmission dynamics of TBE and WNV in Europe, both historically and under future scenarios. Specifically, we focus on phenological shifts in the intensity of infection risk and in the duration of the transmission season throughout the year.

To achieve this, we developed spatiotemporal species distribution models (SDMs) for both viruses and their primary vector species, generating monthly habitat suitability predictions across Europe for past and projected future conditions. For virus modelling, we employed a nested approach that incorporates vector habitat suitability as an additional predictor, capturing the dependence of virus occurrence on vector presence. To disentangle the drivers of observed changes, we applied counterfactual historical scenarios, allowing us to attribute shifts in seasonal transmission risk to either climate or land-use change.

Our analysis offers valuable insights into the changing phenology of TBE and WNV in response to environmental shifts. By identifying temporal shifts in infection risk and in the duration of disease transmission periods, as well as their spatial manifestations through the emergence or intensification of suitability hotspots, our results contribute to a better understanding of how transmission patterns have evolved in the past. Importantly, our results also provide a basis for anticipating future spatiotemporal trends in infection risk, thereby supporting more effective disease risk management through informed early warning systems, targeted surveillance, and adaptive public health strategies.