OPTIMAL VACCINATION STRATEGIES FOR AN INFLUENZA EPIDEMIC MODEL
Why this work is in the frame
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Bibliographic record
Abstract
We present an optimal control model for influenza vaccination strategies in an open population. The model is based on an extended Kermack–McKendrick model with the vaccination rate being a measurable function. The objective of this optimal control model is to describe the vaccination strategies so that the total cost arising from vaccination and infections is minimized. We show that the optimal control is a non-singular bang-bang control which has a finite number of switchings. A scheme for the solution of the optimal control problem is formulated using the shooting method. We also carry out numerical simulations to illustrate the general results and to examine the effects of parameters on the optimal vaccination strategy. The simulations show that the ratio of the per capita treatment cost and per capita vaccination cost has a significant effect on the optimal strategy, while the vaccination rate of the newly recruited class turns out to have less effect.
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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) | 0.001 | 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 | 0.000 |
| 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