Role of monkeys in the sylvatic cycle of chikungunya virus in Senegal
Bibliographic record
Abstract
Abstract Athropod-borne viruses (arboviruses) pose the greatest risk of spillover into humans of any class of pathogens. Such spillover may occur as a one-step jump from a reservoir host species into humans or as a two-step jump from the reservoir to a different amplification host species and thence to humans. Despite the widespread havoc wreaked by emerging arboviruses, little is known about their transmission dynamics in reservoir and amplification hosts. Here we used serosurveillance and mathematical modeling to elucidate the role of monkeys in the sylvatic, enzootic cycle of chikungunya virus (CHIKV). Over three years, 219 African green monkeys, 78 patas monkeys, and 440 Guinea baboons were captured in the region surrounding Kedougou, Senegal. The age of each animal was determined by anthropometry and dentition, and exposure to CHIKV was determined by detection of neutralizing antibodies. We estimate age-specific CHIKV seroprevalence, force of infection (FoI), and basic reproductive number ( R 0 ) in each species. Among the different species, CHIKV Fol ranged from 0.13 to 1.12 (95% CI, 0.81–2.28) and R 0 ranged from 1.5 (95% CI, 1.3–1.9) to 6.6 (95% CI, 5.1–10.4). CHIKV infection of infant monkeys was detected even when the virus was not detected in a concurrent survey of primatophilic mosquitoes and when population seropositivity, and therefore immunity, was too high for monkeys themselves to support continuous CHIKV transmission. We therefore conclude that monkeys in this region serve primarily as amplification rather than reservoir hosts of CHIKV. Additional efforts are needed to identify other vertebrate hosts capable of supporting continuous circulation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".