Abstract
Avian influenza viruses (AIV) have a remarkable capacity to adapt through rapid mutation and cross-species transmission, posing a major global pandemic threat. The distinct spatial patterns of outbreaks suggest that the relationship between disease emergence and ecological drivers is complex, non-linear, and varies with virus pathogenicity and host characteristics. Here, we used a multi-algorithm machine learning statistical framework and 27 ecological features to analyze over 50,000 outbreaks reported across Eurasia and Africa from 2004 to 2025, stratifying the data into highly pathogenic (HPAI), low-pathogenic (LPAI), domestic, and wild-bird subsets. Our models achieved high predictive performance (accuracies ≥ 84%) and revealed distinct, strata-specific risk profiles driven by strong non-linear interactions. For the all-AIV and wild-bird cohorts, vegetation indices and wetland proximity were the most important predictors of spatial risk. Conversely, domestic duck and chicken densities dominated predictions for the LPAI and domestic poultry subsystems, while proximity to LPAI outbreaks was the strongest driver of HPAI risk. Although underreporting in wildlife and developing regions limits data quality, this interpretable framework provides a flexible platform to support risk-based surveillance and to optimize targeted interventions at regional and global scales.
Funding
This manuscript was funded in part by a grant from the United States Department of Agriculture Agriculture Research Service (USDA:ARS) awarded to the University of Minnesota Center for Animal Health and Food Safety.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Supplementary Material 1 (download DOCX )
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissions
About this article
Cite this article
Alkhamis, M.A., Fountain-Jones, N.M., Iglesias, I. et al. Complex interactions between ecological and host factors drive continental patterns of avian influenza outbreak risk in Eurasia and Africa.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-68565-9
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-68565-9
Keywords
- Avian influenza
- Machine learning
- Risk mapping
- Ecological niche models
- RAMSAR wetlands
- Interactions
Source: Ecology - nature.com
