Determinants of Vaccination Coverage and Consequences for Rabies Control in Bali, Indonesia
Bibliographic record
Abstract
Maintaining high vaccination coverage is key to successful rabies control, but mass dog vaccination can be challenging and population turnover erodes coverage. Declines in rabies incidence following successive island-wide vaccination campaigns in Bali suggests that prospects for controlling and ultimately eliminating rabies are good. Rabies, however, has continued to circulate at low levels. In the push to eliminate rabies from Bali, high coverage needs to be maintained across all areas of the island. We carried out door-to-door questionnaire surveys (n=10,352 dog-owning households) and photographic mark-recapture surveys (536 line transects, 2,597 observations of free-roaming dogs) in 2011-2012 to estimate dog population sizes and assess rabies vaccination coverage and dog demographic characteristics in Bali, Indonesia. The median number of dogs per subvillage unit (banjar) was 43 (range 0-307) for owned dogs estimated from the door-to-door survey and 17 (range 0-83) for unconfined dogs (including both owned and unowned) from transects. Vaccination coverage of owned dogs was significantly higher in adults (91.4%) compared to juveniles (<1yr, 43.9%), likely due to insufficient targeting of pups and from puppies born subsequent to vaccination campaigns. Juveniles had a 10 to 70 times greater risk of not being vaccinated in urban, suburban and rural areas (combined Odds Ratios/OR 9.9-71.1, 95% CI 8.6-96.0). Free-roaming owned dogs were also 2-3 times more likely to be not vaccinated compared to those confined (combined ORs 1.9-3.6, 95% CI 1.4-5.4), with more dogs being confined in urban (71.2%) than in suburban (16.1%) and rural areas (8.0%). Vaccination coverage estimates from transects were also much lower (30.9%) than household surveys (83.6%), possibly due to loss of collars used to identify the vaccination status of free-roaming dogs, but these unconfined dogs may also include dogs that were unowned or more difficult to vaccinate. Overall, coverage levels were high in the owned dog population, but for future campaigns in Bali to have the highest chance of eliminating rabies, concerted effort should be made to vaccinate free-roaming dogs particularly in suburban and rural areas, with advertising to ensure owners vaccinate pups. Long-lasting, cheap and quick methods are needed to mark vaccinated animals and reassure communities of
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".