Integrated region-specific modeling of H5 avian influenza in Asia using ENSO-based forecasts
Abstract
Highly pathogenic avian influenza (HPAI), particularly of the H5 subtype, remains a persistent threat to poultry, wildlife, and public health across Asia. This study quantifies the influence of the El Niño–Southern Oscillation (ENSO), using the Multivariate ENSO Index (MEI) as the primary predictor, on the climate-driven dynamics of H5 HPAI through region- and host-stratified generalized additive models (GAMs). Seven region–host strata across Asia were modeled separately, revealing pronounced heterogeneity in event frequency. A clear negative correlation with MEI was identified in domestic poultry across East and South Asia, where higher MEI values, corresponding to El Niño conditions, were linked to reduced event frequencies. In contrast, wild bird populations in East and South Asia displayed irregular, multimodal response patterns to MEI, suggesting phase-specific sensitivities to climate variability. A recurrent neural network (RNN) was further employed to forecast MEI trends, which were then incorporated into the GAMs to predict event dynamics. The forecasts highlighted continued epidemic pressure in East Asia's wild birds, in contrast to stable or declining trends elsewhere. Given the zoonotic potential of H5 viruses, these climate-informed risk forecasts could help inform timely interventions to prevent animal-to-human transmission and support integrated One Health preparedness frameworks. This integrative statistical–deep learning framework offers valuable support for short-term early warning and regionally targeted prevention strategies for H5 HPAI preparedness across Asia. • Region- and host-stratified GAMs revealed heterogeneous effects of ENSO on H5 HPAI event frequency across Asia. • Higher MEI values (El Niño conditions) were negatively correlated with poultry event counts in East and South Asia. • RNN-based MEI forecasts were successfully integrated into the GAM framework for forward-looking risk prediction. • Forecasts indicated continued epidemic pressure in East Asia's wild birds, contrasting with stable or declining trends in other regions. • The integrative framework supports One Health early warning systems and targeted cross-sector prevention strategies.
Fields of Study & Subjects
Influenza Virus Research Studies
Data-Driven Disease Surveillance
Respiratory viral infections research
Publication details
- Journal / source
- One Health
- Volume
- 22
- Pages
- 101322–101322
- Publication date
- 7 January 2026
- Language
- EN