Highly Pathogenic Avian Influenza Outbreak Analysis Reveals Shift in Persistent Risk Regions

Strategic Insights -

Recent research employing Bayesian additive regression trees to model species distribution of highly pathogenic avian influenza (HPAI) clade 2.3.4.4b presence has shed light on the factors driving intercontinental spread of the disease. The study identifies geospatial patterns of infection and projects risk distributions across Europe, revealing a shift in persistent, year-round risk towards cold, low-lying regions of northwest Europe associated with H5N1.

Key Takeaways:

  • The H5N1 outbreak within the H5 2.3.4.4b clade is the largest on record, with Bayesian additive regression trees used to construct species distribution models (SDMs) for HPAI clade 2.3.4.4b presence.
  • The models consider wild bird ecology, including estimates of bird species richness, abundance of specific taxa, and "abundance indices" describing total abundance of birds with high-risk behavioral traits.
  • While previous studies focused on physical geography, this research explicitly considers host ecology as a valuable refinement to SDMs of HPAI.
  • The projections of HPAI clade 2.3.4.4b indicate a shift in persistent, year-round risk towards cold, low-lying regions of northwest Europe associated with H5N1.
  • The study demonstrates that while most variation in risk can be explained by climate and physical geography, adding host ecology is essential for accurate modeling of HPAI.
  • The 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.

Statistics:

  • The H5N1 outbreak within the H5 2.3.4.4b clade is the largest on record.
  • The study identifies a shift in persistent, year-round risk towards cold, low-lying regions of northwest Europe associated with H5N1.
  • The Bayesian additive regression trees used in the study can capture complex nonlinear phenomena in HPAI ecology.

Sources:

  • biorxiv.org/content/10.1101/2024.07.17.603912v2 (preprint article)