Policies to prevent zoonotic spillover: a systematic scoping review of evaluative evidence
Bibliographic record
Abstract
BACKGROUND: Emerging infectious diseases of zoonotic origin present a critical threat to global population health. As accelerating globalisation makes epidemics and pandemics more difficult to contain, there is a need for effective preventive interventions that reduce the risk of zoonotic spillover events. Public policies can play a key role in preventing spillover events. The aim of this review is to identify and describe evaluations of public policies that target the determinants of zoonotic spillover. Our approach is informed by a One Health perspective, acknowledging the inter-connectedness of human, animal and environmental health. METHODS: In this systematic scoping review, we searched Medline, SCOPUS, Web of Science and Global Health in May 2021 using search terms combining animal health and the animal-human interface, public policy, prevention and zoonoses. We screened titles and abstracts, extracted data and reported our process in line with PRISMA-ScR guidelines. We also searched relevant organisations' websites for evaluations published in the grey literature. All evaluations of public policies aiming to prevent zoonotic spillover events were eligible for inclusion. We summarised key data from each study, mapping policies along the spillover pathway. RESULTS: Our review found 95 publications evaluating 111 policies. We identified 27 unique policy options including habitat protection; trade regulations; border control and quarantine procedures; farm and market biosecurity measures; public information campaigns; and vaccination programmes, as well as multi-component programmes. These were implemented by many sectors, highlighting the cross-sectoral nature of zoonotic spillover prevention. Reports emphasised the importance of surveillance data in both guiding prevention efforts and enabling policy evaluation, as well as the importance of industry and private sector actors in implementing many of these policies. Thoughtful engagement with stakeholders ranging from subsistence hunters and farmers to industrial animal agriculture operations is key for policy success in this area. CONCLUSION: This review outlines the state of the evaluative evidence around policies to prevent zoonotic spillover in order to guide policy decision-making and focus research efforts. Since we found that most of the existing policy evaluations target 'downstream' determinants, additional research could focus on evaluating policies targeting 'upstream' determinants of zoonotic spillover, such as land use change, and policies impacting infection intensity and pathogen shedding in animal populations, such as those targeting animal welfare.
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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 | 0.057 | 0.175 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".