Development of a high number, high coverage dog rabies vaccination programme in Sri Lanka.
Bibliographic record
Abstract
Abstract Background: Rabies is estimated to cause 59,000 deaths and economic losses of 8.6 billion USD every year. Despite several years of rabies surveillance and awareness programmes, increased availability of post-exposure prophylaxis vaccinations and dog population control, the disease still remains prevalent in Sri Lanka. This study reports the development and roll-out of a high number, high coverage canine rabies vaccination campaign in Sri Lanka, providing estimates for the vaccination coverage achieved, analysing the local dog demographics, and identifying barriers of attendance to static vaccination clinics. Methods: A mass dog vaccination campaign was undertaken in Negombo, Sri Lanka. The campaign was composed of static point and door-to-door vaccination stages, with a final survey of vaccination coverage. A large volume of data on the distribution, health, and signalment of vaccinated dogs was collected through a mobile phone application. A logistic regression model was developed to investigate which socio-spatial and dog-related factors influenced attendance of owners to static vaccination points. Results: The campaign vaccinated over 7,800 dogs achieving a vaccination coverage of 75.8%. A dog:human ratio of 1:17 was estimated. Most dogs were owned, and the dog population was mostly male, adult, and non-sterilized. Unawareness, unavailability and handling problems were the most common reasons given by owners to explain failure to attend a static vaccination point. The regression analysis showed that increasing distance to a static point, in addition to young age and poor health of the dog, were associated with a decrease in the likelihood of attendance to a static vaccination points. Conclusion: This study demonstrates the feasibility of high number, high coverage vaccination campaigns in Sri Lanka. The information on dog ecology and barriers of attendance to a static point vaccination clinic will facilitate development of future vaccination campaigns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".