#Ecology and #environment predict spatially stratified #risk of #H5 highly pathogenic avian #influenza clade 2.3.4.4b in wild #birds across #Europe
Abstract Highly pathogenic avian influenza (HPAI) represents a threat to animal and human health, with the ongoing H5N1 outbreak within the H5 2.3.4.4b clade being one of the largest on record . However, it remains unclear what factors have contributed to its intercontinental spread . We use Bayesian additive regression trees , a machine learning method designed for probabilistic modelling of complex nonlinear phenomena , to construct species distribution models (SDMs) for HPAI clade 2.3.4.4b presence. We identify factors driving geospatial patterns of infection and project risk distributions across Europe . Our models are time-stratified to capture both seasonal changes in risk and shifts in epidemiology associated with the succession of H5N6/H5N8 by H5N1 within the clade . While previous studies aimed to model HPAI presence from physical geography, we explicitly consider wild bird ecology by including estimates of bird species richness, abundance of specific taxa, and “abundanc...