Preventing zoonotic spillover through regulatory frameworks governing wildlife trade: A scoping review
Bibliographic record
Abstract
Wildlife trade can create adverse impacts for biodiversity and human health globally, including increased risks for zoonotic spillover that can lead to pandemics. Institutional responses to zoonotic threats posed by wildlife trade are diverse; understanding regulations governing wildlife trade is an important step for effective zoonotic spillover prevention measures. In this review, we focused on peer-reviewed studies and grey literature conducted on regulatory approaches that govern domestic and international wildlife trade in order to assess the role of local, national and global-level institutions in the prevention of zoonotic spillover and infection transmission between humans. The five-stage scoping review protocol described by Arksey and O'Malley to map key concepts and main sources and types of evidence available was followed to understand and analyze empirical evidence from peer-reviewed studies and grey literature conducted on regulatory approaches that govern domestic and international wildlife. Sources were included if they discuss at least one of three points: regulatory approaches governing the wild animal trade, including wild animal markets, traditional medicine or exotic pets; regulatory approaches governing importation of wild animals and the international wildlife supply chain; or the role of local, national, and global-level institutions in regulating wild animal trade for food, traditional medicine or exotic pets. A total of 1598 sources were retrieved, from which 32 sources were included in the final review (30 studies + 2 grey literature reports). Based on published literature, regulations governing wildlife trade are inconsistent within and between countries. Organizations regulating wildlife trade may have competing interests, which can lead to fragmentation and a lack in coordination and oversight. National compliance with international regulations can be an issue. Reducing the probability of spillover events in wildlife trade is key to prevent future pandemics. Our results indicate a need for enhanced regulatory harmonization within and between national and supranational regulations. Coordination and collaboration for prevention of zoonotic infection and spillover may be enhanced through future research focused on the effectiveness of timely Information sharing and global- and national- level harmonization of wildlife trade regulations.
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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 | 0.020 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".