Wildlife policy, the food system and One Health: a complex systems analysis of unintended consequences for the prevention of emerging zoonoses in China, the Democratic Republic of the Congo and the Philippines
Bibliographic record
Abstract
INTRODUCTION: Evolving human-wildlife interactions have contributed to emerging zoonoses outbreaks, and pandemic prevention policy for wildlife management and conservation requires enhanced consideration from this perspective. However, the risk of unintended consequences is high. In this study, we aimed to assess how unrecognised complexity and system adaptation can lead to policy failure, and how these dynamics may impact zoonotic spillover risk and food system outcomes. METHODOLOGY: This study focused on three countries: China, the Democratic Republic of the Congo (DRC) and the Philippines. We combined evidence from a rapid literature review with key informant interviews to develop causal loop diagrams (CLDs), a form of systems map representing causal theory about system factors and interconnections. We analysed these CLDs using the 'fixes that fail' (FTF) systems archetype, a conceptual tool used to understand and communicate how system adaptation can lead to policy failure. In each country, we situated the FTF in the wider system of disease ecology and food system factors to highlight how zoonotic risk and food system outcomes may be impacted. RESULTS: We interviewed 104 participants and reviewed 303 documents. In each country, we identified a case of a policy with the potential to become an FTF: wildlife farming in China, the establishment of a new national park in the DRC, and international conservation agenda-setting in the Philippines. In each country, we highlighted context-specific impacts of the FTF on zoonotic spillover risk and key food system outcomes. CONCLUSION: Our use of systems thinking highlights how system adaptation may undermine prevention policy aims, with a range of unintended consequences for food systems and human, animal and environmental health. A broader application of systems-informed policy design and evaluation could help identify instruments approporiate for the disruption of system traps and improve policy success. A One Health approach may also increase success by supporting collaboration, communication and trust among actors to imporove collective policy action.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".