Using physical contact heterogeneity and frequency to characterize dynamics of human exposure to nonhuman primate bodily fluids in central Africa
Bibliographic record
Abstract
Emerging infectious diseases of zoonotic origin constitute a recurrent threat to global health. Nonhuman primates (NHPs) occupy an important place in zoonotic spillovers (pathogenic transmissions from animals to humans), serving as reservoirs or amplifiers of multiple neglected tropical diseases, including viral hemorrhagic fevers and arboviruses, parasites and bacteria, as well as retroviruses (simian foamy virus, PTLV) that are pathogenic in human beings. Hunting and butchering studies in Africa characterize at-risk human social groups, but overlook critical factors of contact heterogeneity and frequency, NHP species differences, and meat processing practices. In southeastern Cameroon, a region with a history of zoonotic emergence and high risk of future spillovers, we conducted a novel mixed-method field study of human physical exposure to multiple NHP species, incorporating participant-based and ecological methodologies, and qualitative interviews (n = 25). We find frequent physical contact across adult human populations, greater physical contact with monkeys than apes, especially for meat handling practices, and positive correlation of human exposure with NHP species abundance and proximity to human settlement. These fine-grained results encourage reconsideration of the likely dynamics of human-NHP contact in past and future NTD emergence events. Multidisciplinary social science and ecological approaches should be mobilized to generate more effective human and animal surveillance and risk communications around neglected tropical diseases. At a moment when the WHO has included "Disease X", a presumably zoonotic pathogen with pandemic potential, on its list of blueprint priority diseases as, new field-based tools for investigating zoonotic disease emergence, both known and unknown, are of critical importance.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".