Public Health Benefits from Livestock Rift Valley Fever Control: A Simulation of Two Epidemics in Kenya
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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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".