The burden of brucellosis in donkeys and its implications for public health and animal welfare: A systematic review and meta-analysis
Bibliographic record
Abstract
Background and Aim: Brucellosis is a globally significant zoonotic disease affecting a wide range of wild and domestic animals, with implications for human and animal health. Despite donkeys' crucial roles in agriculture, transportation, and livelihoods, there is limited research on the burden of brucellosis in this species. This study systematically reviews the prevalence and role of donkeys as reservoirs for Brucella spp., providing insights into their public health implications. Materials and Methods: Using the PRISMA guidelines, a systematic search of PubMed, Scopus, and Google Scholar was conducted for studies published from 1990 to May 2024. Out of 1159 retrieved articles, 20 met the inclusion criteria. Data on study design, location, diagnostic methods, and brucellosis prevalence were extracted and analyzed using R statistical software. Pooled prevalence and heterogeneity were calculated, and the Newcastle-Ottawa Scale was employed to assess study quality. Results: The pooled prevalence of brucellosis in 6785 donkeys across 20 studies was 10.23% (range: 0%-63.7%), with the highest prevalence reported in Asia (26.80%). While 15% of studies suggested that donkeys act as reservoirs for Brucella spp., direct evidence linking donkeys to disease transmission remains scarce. The disease's impact on donkey reproduction, including abortion and infertility, is underexplored, highlighting a significant research gap. Conclusion: Brucellosis in donkeys represents a notable zoonotic and occupational risk. The limited data from East Africa, despite its high donkey population, emphasize the need for comprehensive epidemiological studies. Findings underscore the importance of targeted interventions, including biosecurity, public education, and enhanced diagnostic approaches, to mitigate brucellosis' impact on donkey health and its broader public health implications.
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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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.038 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".