Impact of One-Health framework on vaccination cost-effectiveness: A case study of rabies in Ethiopia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.003 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it