Evaluation of transmission metrics in a slow-spreading highly pathogenic avian influenza (HPAI) outbreak in a commercial upland game bird system
Bibliographic record
Abstract
In 2022, highly pathogenic avian influenza virus (HPAIV) H5N1 clade 2.3.4.4b was detected in United States (US) poultry, quickly escalating into an outbreak that surpassed the 2014/15 HPAI US event in scale and impact. Unlike in 2014/15, the 2022/3/4/5 outbreak has included numerous HPAI detections in previously infrequently affected commodities such as broilers and commercially-raised upland game birds. Here, we describe H5 HPAI detections that occurred between December 2023 and January 2024 in a multipremises, commerical upland game bird system located in the Midwestern US. We used an approximate Bayesian computation algorithm and stochastic within-flock HPAI transmission model to estimate the following: (1) time of HPAI introduction onto each affected premises, (2) number of days between estimated times of introduction and times of detection, and (3) the adequate contact rates and basic reproduction numbers within individual barns. Clinical signs and mortality observed in the infected pheasant flocks were largely consistent with other pheasant H5 clade 2.3.4.4b outbreaks. Across the system, the estimated transmission metrics were noticeably lower than those calculated from outbreaks in other poultry species. However, times to detection were similar to HPAI outbreaks that have occurred in other commodities.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".