Human interactions with bats and bat coronaviruses in rural Côte d'Ivoire
Bibliographic record
Abstract
Bats are presumed reservoirs of diverse α- and β- coronaviruses (CoVs) and understanding the diversity of bat-CoVs and the role bats play in CoV transmission is highly relevant in the context of the current COVID pandemic. We sampled bats in Côte d'Ivoire (2016–2018) living at ecotones between anthropogenic and wild habitats in the Marahoué National Park, a recently encroached protected area, to detect and characterize the CoVs circulating in bats and humans. A total of 314 bats were captured, mostly during the rainy season (78%), and CoV RNA was detected in three of the bats (0.96%). A CoV RNA sequence similar to Chaerephon bat coronavirus/Kenya/KY22/2006 (BtKY22) was found in a Chaerephon cf. pumilus and a Mops sp. fecal swab, while a CoV RNA sequence similar to the two almost identical Kenya bat coronaviruses BtKY55 and BtKY56 (BtKY55/56) was detected in an Epomops buettikoferi oral swab. Phylogenetic analyses indicated differences in the degree of evolutionary host-virus co-speciation for BtKY22 and BtKY55/56. To assess potential for human exposure to these viruses, we conducted human syndromic and community-based surveillance in clinics and high-risk communities. We collected data on participant characteristics, livelihoods, animal contact, and high-risk behaviors that may be associated with exposure to zoonotic diseases. We then collected biological samples for viral testing from 401 people. PCR testing of these biological samples revealed no evidence of CoV infection among the enrolled individuals. We identified higher levels of exposure to bats in people working in crop production and in hunting, trapping and fishing. Finally, we used the ‘Spillover’ risk-ranking tool to assess the potential for viral spillover and concluded that, while there is no evidence to suggest imminent risk of spillover for these CoVs, their host range and other traits suggest caution and vigilance are warranted in people with high exposure risk.
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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 | 0.000 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".