Surveillance Opportunities and the Need for Intersectoral Collaboration on Rabies in Sri Lanka
Bibliographic record
Abstract
Sri Lanka is progressing towards its goal of eliminating human rabies. This goal rests on programs designed to limit canine rabies, which in turn requires a combination of targeted dog rabies control and a better understanding of the movement of the virus between domestic animals, people, and wildlife. Coordinated and integrated surveillance of the disease between human and animal health sectors underpins successful rabies elimination. Our objective was to review surveillance data from 2005 to 2014 to assemble the first multispecies synthesis of rabies information in Sri Lanka and, in doing so, assess needs and opportunities for a One Health approach to rabies surveillance in the country. Our descriptive epidemiological findings were consistent with other studies showing a decline in human cases, endemic and unchanging numbers of dog cases, a relationship between human density and the occurrence of human and animal cases, and significant gaps in understanding trends in rabies incidences in livestock and wildlife. Assessing the trends in the data from the three government organizations responsible for rabies surveillance was difficult due to lack of information on animal population sizes, unquantified sampling biases due to inequities in access to diagnostic capacities, regulatory and administrative barriers, and a continued reliance on clinical means to establish a diagnosis. The information required for a comprehensive rabies control programme was not standardized or consistent, was not in one place, showed significant gaps in completeness, and was not amenable to routine and rapid analysis. Achieving rabies elimination in Sri Lanka would benefit from harmonization of diagnostic and information management standards across animal and human health sectors as well as equitable access to diagnostic capacity for all regions and species.
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 | 0.002 | 0.000 |
| 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 | 0.000 |
| 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".