Tick-borne viruses and their risk to public health in the Caribbean: Spotlight on bats as reservoirs in Cuba
Bibliographic record
Abstract
In recent decades, tick-borne diseases (TBDs) have surged and expanded globally due to factors like changes in human activities, land use patterns, and climate change, and it have been associated with the emergence of zoonotic diseases. Cuba faces the impact of ticks on human health and the economy. Although Cuba has studied TBDs extensively for the past 50 years, focus on tick-borne viral pathogens affecting humans remains scant. Despite TBDs not currently being a major health concern in Cuba, factors like inadequate clinician awareness, climate conditions, global tick emergence, and evidence of zoonotic pathogens in ticks underscore the importance of enhanced TBD surveillance in the country. Here we revised the available information on ticks as vectors of pathogenic viruses to humans, spotlighting bats as potential reservoirs of tick-borne viruses (TBVs). Ticks on bats have gained interest as potential reservoirs of pathogenic viruses to humans in Cuba and worldwide. Understanding their role in maintaining viruses and their potential transmission to humans is crucial for the implementation of surveillance and control programs to reduce the risk of tick-borne viral diseases and public health management.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".