Phylogenetic Inference of the 2022 Highly Pathogenic H7N3 Avian Influenza Outbreak in Northern Mexico
Bibliographic record
Abstract
The Mexican lineage H7N3 highly pathogenic avian influenza virus (HPAIV) has persisted in Mexican poultry since its first isolation in 2012. To date, the detection of this virus has gradually expanded from the initial one state to 18 states in Mexico. Despite the HPAIV H7N3 outbreak occurring yearly, the transmission pathways have never been studied, disallowing the establishment of effective control measures. We used a phylogenetic approach to unravel the transmission pathways of 2022 H7N3 HPAIVs in the new outbreak areas in Northern Mexico. We present genetic data of H7N3 viruses produced from 18 poultry farms infected in the spring of 2022. Our results indicate that the virus responsible for the current outbreak in Northern Mexico evolved from the Mexican lineage H7N3 HPAIV discovered in 2012. In the current outbreak, we identified five clusters of infection with four noticeably different genetic backgrounds. It is a cluster IV-like virus that was transmitted into one northern state causing an outbreak, then spreading to another neighboring northern state, possibly via a human-mediated mechanical transmission mechanism. The long-distance transmission event highlights the necessity for the more rigorous enforcement of biosafety measures in outbreaks. Additionally, we examined the evolutionary processes shaping the viral genetic and antigenic diversities. It is imperative to enhance active surveillance to include birds, the environment, and humans to detect HPAI in domestic poultry at an earlier point and eliminate it.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".