Public Health Benefits from Livestock Rift Valley Fever Control: A Simulation of Two Epidemics in Kenya
Why this work is in the frame
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Bibliographic record
Abstract
In controlling Rift Valley fever, public health sector optimises health benefits by considering cost-effective control options. We modelled cost-effectiveness of livestock RVF control from a public health perspective in Kenya. Analysis was limited to pastoral and agro-pastoral system high-risk areas, for a 10-year period incorporating two epidemics: 2006/2007 and a hypothetical one in 2014/2015. Four integrated strategies (baseline and alternatives), combined from three vaccination and two surveillance options, were compared. Baseline strategy included annual vaccination of 1.2-11% animals plus passive surveillance and monitoring of nine sentinel herds. Compared to the baseline, two alternatives assumed improved vaccination coverage. A herd dynamic RVF animal simulation model produced number of animals infected under each strategy. A second mathematical model implemented in R estimated number people who would be infected by the infected animals. The 2006/2007 RVF epidemic resulted in 3974 undiscounted, unweighted disability adjusted life years (DALYs). Improving vaccination coverage to 41-51% (2012) and 27-33% (2014) 3 years before the hypothetical 2014/2015 outbreak can avert close to 1200 DALYs. Improved vaccinations showed cost-effectiveness (CE) values of US$ 43-53 per DALY averted. The baseline practice is not cost-effective to the public health sector.
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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 | 0.000 |
| 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 it