Social Determinants of a Potential Spillover of Bat-Borne Viruses to Humans in Ghana
Bibliographic record
Abstract
<p class="1Body">Bats are well-recognized reservoirs of a number of zoonotic viruses including henipavirus. The straw-coloured fruit bat (<em>Eidolon helvum</em>) and the Gambian epauletted fruit bat (<em>Epomophorus gambianus)</em> can in found in many parts of Ghana, raising concerns about the possibility of a spillover of henipavirus from bats to humans. However the context-specific socio-economic factors that may increase points of contact between bats and humans have still not been adequately identified. Using a number of participatory methods, this in-depth investigation sought to understand the behavioural and socio-economic factors that could facilitate henipavirus spillover to humans in Ghana. Direct exposure included people coming into contact with fresh bat meat through eating, hunting and processing bat meat. Indirect exposure included sitting, selling under bat roosts as well as exposure to bat faeces through contaminated water. Gender was most strongly associated with exposure, compared to age and education. Perceptions of disease risk from bats were generally low among respondents. The study highlights the complexities of sustainably managing a potential henipavirus spillover into humans in Ghana. It recommends the establishment of a multidisciplinary team made up of ecologists, social scientists, legal, veterinary and public health experts to manage such a spillover. The paper also recommends continuous education to encourage behavioural changes in people and to develop sustainable and relevant zoonoses prevention practices especially among identified groups at risk.</p>
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".