Exploring the Cost-Benefit of Vaccines for Infectious Diseases in Slaughter Pig Production
Bibliographic record
Abstract
Modern pig production is facing a number of diseases where control strategies, if available, are voluntary.An example could be the adoption of a vaccination program against Swine Influenza.Although such a vaccine currently is unavailable in Denmark, an economic analysis of application under typical Danish slaughter pig production conditions serves well to illustrate the conceptual aspects of such a decision problem.The efficacy of a vaccine can be established under experimental conditions, while the value of a vaccination program needs modeling of the specific conditions of the production system in which the vaccine should operate.A model where the control strategy for a Swine Influenza-like disease and the delivery policy are optimized simultaneously is developed in Toft et al. (2001).The optimal policy will consist of decisions at multiple time scales.The optimal delivery policy at the finishing time for a batch of pigs will be contingent upon the chosen vaccination policy adopted at the beginning of the current fattening period.Using this model we explore the cost-benefit relationship of vaccines with efficacy less than 1 applied to Danish conditions.We adopt 2 different interpretations of imperfect efficacy taken from Halloran et al. (1992).A leaky vaccine that offer reduced susceptibility to all pigs, and an all-or-nothing vaccine where a fraction of pigs are offered complete immunity while the remaining pigs are left fully susceptible to the disease.Using these 2 efficacy interpretations we explore the optimal policy of delivering pigs for slaughter and controlling disease.The problem serves as an illustration of the potential shortcomings of the rather crude efficacy measure usually adopted for vaccines, as well as an introduction to the possibilities of model based decision support.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".