Avian influenza risk mapping in India using machine learning

Abstract

Highly pathogenic avian influenza (HPAI) remains a recurrent threat to poultry production and One-Health surveillance in India. We developed a national relative spatial risk map for India using curated outbreak records spanning January 2006 to April 2024 (predominantly HPAI H5N1 and H5N8), buffered pseudo-absence sampling, H3 resolution-7 hexagons, and 94 environmental, livestock, land-cover, and anthropogenic predictors. Under 5-fold spatial block cross-validation, eight base classifiers were trained and all performed above chance (AUC > 0.76). The Gaussian-process stacked meta-learner achieved the highest AUC (0.853), but the improvement over the strongest individual base learner, Random Forest (AUC 0.851; Brier 0.155; ECE 0.079), was small and statistically non-significant. Its principal added value was a companion uncertainty layer, the GPR posterior standard deviation, which showed internal consistency with ensemble disagreement across base models (r = 0.74, p < 0.001). Feature attribution ranked human population density, extensive chicken density, June precipitation, and seasonal humidity variables among the predictors most associated with model outputs, with interpretation constrained by passive-surveillance bias and multicollinearity. The resulting relative spatial risk surface, prediction-uncertainty surface, and subdistrict risk-uncertainty classification layers identify eastern, northeastern, coastal, and selected southern regions as priorities for targeted surveillance and prospective validation.