A One Health approach to mitigate the impact of influenza A virus (IAV) reverse zoonosis is by vaccinating humans and susceptible farmed and pet animals
Bibliographic record
Abstract
The term reverse zoonosis specifically refers to the natural transmission of disease and infection from humans to animals, with humans as the reservoir host replicating the infectious agent. In the last 20 years, reverse zoonosis has increasingly garnered attention because of human disease outbreaks. In this Currents in One Health article, the author will review host range as the main risk factor for reverse zoonosis, with an emphasis on influenza A virus (IAV) disease events in humans and other species in the context of a "One Health" approach to gain a better understanding of their transmission routes to facilitate their control and prevent them from occurring. The human-to-pig transmission of IAV represents the largest reverse zoonosis of a pathogen documented to date. At the same time, the 2022 farmed mink outbreak in Spain is the most sustained mammal-to-mammal transmission of the highly pathogenic avian influenza (HPAI) H5N1 since its re-emergence in humans in 2003. Without any prospect of eradicating IAVs, the best way to mitigate the impact of IAV reverse zoonosis is by vaccinating humans and susceptible farmed and pet animals. The recent major reverse zoonoses involving other virus groups (Coronaviridae, Poxviridae, arboviruses, and the human respiratory viruses transmitted to endangered non-human primate species) and the prevention and control of reverse zoonoses are addressed in the companion Currents in One Health by Kibenge, JAVMA, June 2023.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".