2026-03-19 コペンハーゲン大学（UCPH）
＜関連情報＞
- https://news.ku.dk/all_news/2026/03/bird-flu-risk-to-danish-cattle–new-tool-can-warn-farmers-before-infection-spreads/
- https://www.sciencedirect.com/science/article/pii/S0167587726000632
デンマークにおける野生鳥類から牛への高病原性鳥インフルエンザの伝播リスクのモデリング：データ駆動型リスク評価フレームワーク Modeling the spillover risk of highly pathogenic avian influenza from wild birds to cattle in Denmark: A data-driven risk assessment framework
You Chang, Jose L. Gonzales, Erik Rattenborg, Mart C.M. de Jong, Beate Conrady
Preventive Veterinary Medicine Available: online 7 March 2026
DOI:https://doi.org/10.1016/j.prevetmed.2026.106844
Abstract
Since early 2024, highly pathogenic avian influenza virus (HPAIV) H5N1 of clade 2.3.4.4b has spilled over from wild birds to dairy cattle in the United States (U.S.), spreading to more than 1000 herds and threatening both animal and public health. Denmark’s location along major migratory flyways and the lack of active HPAIV surveillance in cattle underscore the need to assess potential spillover risk from wild birds to cattle to strengthen preparedness. A quantitative spillover risk assessment model was developed to integrate data from Bird Flu Radar, eBird, and cattle density to estimate the weekly probability of HPAIV introduction from wild birds to cattle. The model was calibrated using observed U.S. spillover data and extrapolated to Denmark under the assumption of a comparable transmission rate parameter. Under the frequency-dependent model, the expected HPAIV introductions to Danish cattle via wild birds remain below 0.35 cases per week, with the highest temporal risk from December to March. High-risk areas were concentrated along the Danish coastline and near the German border. In contrast, applying a density-dependent model shifted the spatial risk toward regions with higher cattle densities, while the high-risk temporal periods remained unchanged. Overall, the results indicate a spatially and temporally variable risk of HPAIV spillover from wild birds to cattle in Denmark. The model establishes a data-driven framework to strengthen early warning and guide targeted surveillance efforts in high-risk regions.
