Animal board invited review: Risks of zoonotic disease emergence at the interface of wildlife and livestock systems
Bibliographic record
Abstract
The ongoing coronavirus disease 19s pandemic has yet again demonstrated the importance of the human-animal interface in the emergence of zoonotic diseases, and in particular the role of wildlife and livestock species as potential hosts and virus reservoirs. As most diseases emerge out of the human-animal interface, a better understanding of the specific drivers and mechanisms involved is crucial to prepare for future disease outbreaks. Interactions between wildlife and livestock systems contribute to the emergence of zoonotic diseases, especially in the face of globalization, habitat fragmentation and destruction and climate change. As several groups of viruses and bacteria are more likely to emerge, we focus on pathogenic viruses of the Bunyavirales, Coronaviridae, Flaviviridae, Orthomyxoviridae, and Paramyxoviridae, as well as bacterial species including Mycobacterium sp., Brucella sp., Bacillus anthracis and Coxiella burnetii. Noteworthy, it was difficult to predict the drivers of disease emergence in the past, even for well-known pathogens. Thus, an improved surveillance in hotspot areas and the availability of fast, effective, and adaptable control measures would definitely contribute to preparedness. We here propose strategies to mitigate the risk of emergence and/or re-emergence of prioritized pathogens to prevent future epidemics.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.011 |
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".