The changing dynamics of highly pathogenic avian influenza H5N1: Next steps for management & science in North America
Bibliographic record
Abstract
Highly pathogenic avian influenza virus (HPAIV) H5N1 was introduced in North America in late 2021 through trans-Atlantic pathways via migratory birds. These introductions have resulted in an unprecedented epizootic, a widespread disease event in animals, heavily affecting poultry, wild birds, and recently mammals. The North American incursions occurred during the largest epidemic season (2021–2022) in Europe where H5N1 may now be endemic (i.e., continuously present). The continuing outbreak includes expansion into Mexico, Central and South America beginning in late 2022. Here, we provide an overview of the Eurasian origin H5N1 introduction to the Americas, including a significant shift in virus dynamics and severe disease in wild birds. Then, to investigate the global changes in confirmed detections in wild birds and poultry across time and geographic regions, we analyzed FAO's EMPRES-i + database. To examine the 2021 introduction and spread in North American wild birds and poultry, we collated publicly available data across USA and Canadian federal sources. Based on our assessment, the unique magnitude of the North American H5N1 spread indicates the need for effective decision framing to prioritize management needs and scientific inquiry, particularly for species at risk and interface areas for wildlife, poultry, and humans. We illustrate the rapidly occurring and likely increasing detrimental effects that this One Health issue has on wildlife, agriculture, and potentially human health, and we offer a reframing of HPAIV disease response towards a decision analytical context to guide scientific prioritization as a potentially valuable change in focus.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".