tetano
Editor, Senior Moderator
One Health
. 2026 Jan 7:22:101322.
doi: 10.1016/j.onehlt.2026.101322. eCollection 2026 Jun.
Integrated region-specific modeling of H5 avian influenza in Asia using ENSO-based forecasts
Yinghui Su[SUP] 1 [/SUP], Ruoxuan Wu[SUP] 1 [/SUP], Pengfei Liu[SUP] 1 [/SUP], Zhichao Li[SUP] 2 [/SUP], Juan Pu[SUP] 1 [/SUP], Lu Wang[SUP] 1 [/SUP]
Affiliations
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.
Keywords: Climate-informed early warning; El Niño-southern oscillation; Generalized additive models; H5 highly pathogenic avian influenza; Multivariate ENSO index; One Health surveillance.
. 2026 Jan 7:22:101322.
doi: 10.1016/j.onehlt.2026.101322. eCollection 2026 Jun.
Integrated region-specific modeling of H5 avian influenza in Asia using ENSO-based forecasts
Yinghui Su[SUP] 1 [/SUP], Ruoxuan Wu[SUP] 1 [/SUP], Pengfei Liu[SUP] 1 [/SUP], Zhichao Li[SUP] 2 [/SUP], Juan Pu[SUP] 1 [/SUP], Lu Wang[SUP] 1 [/SUP]
Affiliations
- PMID: 41567856
- PMCID: PMC12818151
- DOI: 10.1016/j.onehlt.2026.101322
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.
Keywords: Climate-informed early warning; El Niño-southern oscillation; Generalized additive models; H5 highly pathogenic avian influenza; Multivariate ENSO index; One Health surveillance.