WildHealthNet: Supporting the development of sustainable wildlife health surveillance networks in Southeast Asia
Bibliographic record
Abstract
Wildlife and wildlife interfaces with people and livestock are essential surveillance targets to monitor emergent or endemic pathogens or new threats affecting wildlife, livestock, and human health. However, limitations of previous investments in scope and duration have resulted in a neglect of wildlife health surveillance (WHS) systems at national and global scales, particularly in lower and middle income countries (LMICs). Building on decades of wildlife health activities in LMICs, we demonstrate the implementation of a locally-driven multi-pronged One Health approach to establishing WHS in Cambodia, Lao PDR and Viet Nam under the WildHealthNet initiative. WildHealthNet utilizes existing local capacity in the animal, public health, and environmental sectors for event based or targeted surveillance and disease detection. To scale up surveillance systems to the national level, WildHealthNet relies on iterative field implementation and policy development, capacity bridging, improving data collection and management systems, and implementing context specific responses to wildlife health intelligence. National WHS systems piloted in Cambodia, Lao PDR, and Viet Nam engaged protected area rangers, wildlife rescue centers, community members, and livestock and human health sector staff and laboratories. Surveillance activities detected outbreaks of H5N1 highly pathogenic avian influenza in wild birds, African swine fever in wild boar (Sus scrofa), Lumpy skin disease in banteng (Bos javanicus), and other endemic zoonotic pathogens identified as surveillance priorities by local stakeholders. In Cambodia and Lao PDR, national plans for wildlife disease surveillance are being signed into legislation. Cross-sectoral and trans-disciplinary approaches are needed to implement effective WHS systems. Long-term commitment, and paralleled implementation and policy development are key to sustainable WHS networks. WildHealthNet offers a roadmap to aid in the development of locally-relevant and locally-led WHS systems that support the global objectives of the World Organization for Animal Health's Wildlife Health Framework and other international agendas.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".