Abstract
Avian influenza poses substantial risks to animal welfare and public health. The recent surge in highly pathogenic avian influenza (HPAI) outbreaks has led to extensive poultry culling, highlighting the need for early warning systems. Using data on H5 HPAI virus (HPAIV) occurrence from the World Organization for Animal Health and the Food and Agriculture Organization, we employed a spatial time-series modelling framework to predict occurrences in Japan and South Korea, 2020-2024. This framework decomposes time-series data into endemic and epidemic components and has previously been used to model HPAIV in Europe. We identified 1,310 HPAIV detections from 2020 to 2024, the majority being H5N1 (55.3%) and H5N8 (35.0%). These data consisted of 827 and 483 detections in wild and domestic birds, respectively. The model included seasonality and covariates in both endemic and epidemic components and revealed consistent yearly seasonal patterns. This contrasts with previous modelling of European data where seasonality changed over time. The model predicted 81% of detections as epidemic, primarily due to within-region transmission (53%), whereas only 19% were endemic. This model effectively predicts weekly H5 HPAIV detections, aiding decision-makers in identifying high-risk periods. This study confirms the robustness and usefulness of endemic-epidemic modelling of HPAIV in different regions of the world.