Benefit-cost analysis of raccoon rabies control in Ontario, Canada
Bibliographic record
Abstract
Zoonotic diseases, particularly those originating in wildlife, pose significant public health and economic risks. Rabies, a viral zoonosis with near-100% case fatality in humans, is a prime example of such a threat, especially in regions like North America where wildlife-such as raccoons-serve as key reservoirs. This study assesses the economic efficiency of Ontario, Canada's raccoon rabies control program, which combines oral rabies vaccination (ORV), trap-vaccinate-release (TVR), and surveillance strategies. Using a spatial agent-based epidemiological model, the study estimates the benefits and costs of intervention compared to a no-intervention scenario over the period 2015-2025. Benefits were quantified as avoided public health costs, including post-exposure prophylaxis (PEP), animal testing (AT), and human exposure investigations (INVT), and converted to 2023 CAD. Results show that the intervention prevented significant economic losses, with benefit-cost ratios ranging from 1.5 to 14.16 depending on assumed rates of intervention necessity, confirming the program's cost-effectiveness. This analysis not only supports continued investment in wildlife rabies control but also provides a scalable economic framework for other zoonotic disease management programs utilizing a One Health approach.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".