The Arrival of Highly Pathogenic Avian Influenza Viruses in North America, Ensuing Epizootics in Poultry and Dairy Farms and Difficulties in Scientific Naming
Bibliographic record
Abstract
The highly pathogenic avian influenza virus (HPAIV) H5N1, first isolated in 1996 in China, spread rapidly across Eurasia and caused major epizootics in wild and domesticated birds, as well as spillover infections in humans characterised by high mortality. Avian influenza viruses are therefore candidate viruses for a human pandemic. Surprisingly, HPAIV was not isolated in North America until 2014. With the help of intensive biological sampling and viral genome sequencing, the intrusion of HPAIV into North America could be retraced to two separate events. First, migratory birds carried HPAIV from East Siberia via Beringia and dispersed the virus along the Pacific flyway. After reassortment with genes of local low pathogenic avian influenza viruses, HPAIV H5 caused 2015 a major epizootic on poultry farms in the US Mid-West. After costly containment, HPAIV dropped below the detection limit. In 2021, Eurasian HPAIV H5 viruses arrived a second time in North America, carried by migratory birds to Canada via the Atlantic flyway, using Iceland as a stop. The H5 virus then spread with water birds along the East Coast of the United States and dispersed across the United States. In contrast to the 2015 poultry outbreak, spillover infections into diverse species of mammals were now observed. The events culminated in the 2024 HPAIV H5 epizootic in dairy cows affecting 300 dairy herds in 14 US states. The cattle epizootic was spread mainly by milking machinery and animal transport. On affected farms infected cats developed fatal neurological diseases. Retail milk across the United States frequently contains viral RNA, but so far only a few milk farm workers have developed mild symptoms. The tracing of HPAIV with viral genome sequencing complicated the taxonomical naming of influenza viruses raising fundamental problems in how to mirror biological complexity in written plain language, rendering communication with the lay public difficult.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".