Optimizing syndromic health surveillance in free ranging great apes: The case of Gombe National Park
Bibliographic record
Abstract
Abstract Syndromic surveillance is an incipient approach to early wildlife disease detection. Consequently, systematic assessments are needed for methodology validation in wildlife populations. We evaluated the sensitivity of a syndromic surveillance protocol for respiratory disease detection among chimpanzees in Gombe National Park, Tanzania. Empirical health, behavioural, and demographic data were integrated with an agent‐based, network model to simulate disease transmission and surveillance. Surveillance sensitivity was estimated as 66% (95% CI: 63.1, 68.8%) and 59.5% (95% CI: 56.5%, 62.4%) for two monitoring methods (weekly count and prevalence thresholds respectively), but differences among calendar quarters in outbreak size and surveillance sensitivity suggest seasonal effects. We determined that a weekly detection threshold of ≥2 chimpanzees with clinical respiratory disease leading to outbreak response protocols (enhanced observation and biological sampling) is an optimal algorithm for outbreak detection in this population. Synthesis and applications. This is the first quantitative assessment of syndromic surveillance in wildlife, providing a model approach to detecting disease emergence. Coupling syndromic surveillance with targeted diagnostic sampling in the midst of suspected outbreaks will provide a powerful system for detecting disease transmission and understanding population impacts.
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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 | 0.009 | 0.029 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".