Speaker
Description
Highly pathogenic avian influenza (HPAI) represents a serious 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. Although wild birds are known to be a key reservoir of HPAI, the factors driving prevalence within this reservoir remain poorly understood. In this study 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 presence and identify factors driving geospatial patterns of infection in wild birds across Europe. Our models are time-stratified to capture both seasonal changes in risk and shifts in epidemiology associated with the succession of earlier strains 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 “abundance indices” describing total abundance of birds with high-risk behavioural traits. Our model projections indicate a shift in persistent, year-round risk towards cold, low-lying regions of northwest Europe associated with H5N1. Methodologically, we demonstrate that while most variation in risk can be explained by climate and physical geography, adding host ecology is a valuable refinement to SDMs of HPAI.
