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 and trans-Pacific pathways via migratory birds. These introductions have resulted in an unprecedented and widespread epizootic event for North America, heavily affecting poultry and free-living wild birds in the spring and summer of 2022. The North American incursions are occurring in the context of Europe’s largest epidemic season (2021 – 2022) where HPAIV may now be enzootic. A continued North American epizootic is expected in the fall of 2022 as migratory waterfowl return from their breeding grounds. The magnitude of the North American HPAIV spread indicates the need for effective decision framing to prioritize ongoing management needs and scientific inquiry, particularly for species at risk and interface areas for wildlife, poultry, and humans. The challenges of this global One Health disease could benefit from a decision framing which may result in improved collaboration across stakeholders, identification of management options, and prioritization of scientific needs. Here, we provide an overview of the Eurasian origin HPAIV H5N1 introduction, including a shift in the dynamics of disease, which has resulted in severe disease in wild birds. It is unclear if wild bird may have been previously not exposed or asymptomatic to disease. We seek to bring attention to the detrimental effects this One Health issue may have on wild birds, poultry, and potentially human health and to suggest that reframing ongoing disease management as decisions, rather than as scientific endeavors, could be a 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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".