Impact of One-Health framework on vaccination cost-effectiveness: A case study of rabies in Ethiopia
Bibliographic record
Abstract
Livestock losses due to rabies and health and the corresponding benefits of controlling the disease are not often considered when the cost-effectiveness of rabies control is evaluated. In this research, assessed the benefits of applying a One Health perspective that includes these losses to the case of canine rabies vaccination in Ethiopia. We constructed a dynamic epidemiological model of rabies transmission. The model was fit to district-specific data on human rabies exposures and canine demography for two districts with distinct agro-ecologies. The epidemiological model was coupled with human and livestock economic outcomes to predict the health and economic impacts under a range of vaccination scenarios. The model indicates that human exposures, human deaths, and rabies-related livestock losses would decrease monotonically with increasing vaccination coverage. In the rural district, all vaccination scenarios were found to be cost-saving compared to the status quo of no vaccination, as more money could be saved by preventing livestock losses than would be required to fund the vaccination campaigns. Vaccination coverages of 70% and 80% were identified as most likely to provide the greatest net health benefits at the WHO cost-effectiveness threshold over a period of 5 years, in urban and rural districts respectively. Shorter time frames led to recommendations for higher coverage in both districts, as did even a minor threat of rabies re-introduction. Exclusion of rabies-related livestock losses reduced the optimal vaccination coverage for the rural district to 50%. This study demonstrated the importance of including all economic consequences of zoonotic disease into control decisions. Analyses that include cattle and other rabies-susceptible livestock are likely better suited to many rural communities in Africa wishing to maximize the benefits of canine vaccination.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".