Rabies epidemiology, prevention and control in Nigeria: Scoping progress towards elimination
Bibliographic record
Abstract
BACKGROUND: Human rabies remains a significant public health problem in Africa with outbreaks reported in most countries. In Nigeria-the most populous country in Africa-rabies causes a significant public health burden partly due to perennial obstacles to implementing a national prevention and control program. METHODS: We conducted a scoping review using standard Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to identify and select published articles from Nigeria during 1978-2020 reporting on rabies virus infections (human, canine, livestock, and wildlife), canine bites, knowledge, attitudes and practices (KAP) surveys on rabies and canine ecology studies. We extracted information on study location, year and additional details of each study such as rabies prevalence, general characteristics of offending dogs, dog vaccination status and health-seeking behaviours. FINDINGS: Between 1978 and 2020, 90 published articles met our inclusion criteria. The prevalence of rabies virus antigen detection varied between 3% and 28%, with more studies in the north. Most bites were unprovoked from dog bite studies (36.4%-97%), by dogs with low vaccination rates (12-38%). A more significant proportion of biting dogs were owned (31-90%). Laboratory confirmation for biting was available for only a small proportion of studies (6%; n = 2/32). Of the dogs surveyed during ecology studies, indigenous dogs accounted for the majority (62-98%), used mostly for security purposes (52-98%), with the vaccination rate between 15% and 38% in most states. Studies conducted in areas distant from rabies diagnostic facilities accounted for more human rabies cases and fewer dog rabies cases. CONCLUSION: Significant improvements are necessary to achieve the elimination of human rabies mediated via dogs by 2030.
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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 | 0.029 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".