Surveillance for One Health and high consequence veterinary pathogens (Brucellosis, Coxiellosis and Foot and Mouth Disease) in Southeast Asia: Lao PDR and Cambodia in focus and the importance of international partnerships
Bibliographic record
Abstract
Animal disease surveillance in limited-resource countries is challenging but critical in providing epidemiological information to inform disease prevention and control programmes. Despite multiple international agencies and partnerships supporting Lao PDR and Cambodia’s animal disease surveillance activities over many years, cost-effectiveness and sustainability remain significant constraints. Here we describe the development and implementation of national abattoir-based surveillance networks in Laos and central Cambodia consisting of an information exchange platform and sample collection and submission systems. The networks enhanced the national surveillance capacity and provided snapshot information of seroprevalence for selected One Health and high consequence veterinary pathogens, including Q fever, brucellosis, and Foot and Mouth Disease (FMD). Despite abattoir survey data revealing that the seroprevalence of Q fever and brucellosis was generally low, the true impact on public health for these diseases remains unclear due to low levels of awareness and diagnostic capacity. FMD antibodies derived from natural infection rather than vaccination were noted in greater than 40% of the animal sampled in both countries, which suggests significant underreporting of outbreak events. Such networks will continue to be refined to improve their cost-effectiveness and sustainability, including the introduction of a simple online application for reporting animal disease outbreaks as well as expanding to other relevant One Health pathogens and species.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".