Integrated economic -epidemic modeling of avian influenza mitigation options: A case study of an outbreak in Texas
Bibliographic record
Abstract
Recent World Animal Health Organization (OIE) reports on Avian Influenza (AI) outbreaks in Asia, Europe and Canada suggest that there is a nonzero probability that an\noutbreak may occur anywhere in the world, including the US. To help evaluate possible policy in the face of such an event, this dissertation does an economic evaluation of the implications of using two mitigation strategies: one corresponding to the currently response strategy; and the other an OIE recommended one utilizing vaccination. To do this, the dissertation develops and uses an integrated economic-epidemic model. In this\neffort, I first estimate the cost of an AI outbreak under a deterministic disease spread assumption where a new vaccination strategy and the current strategy are compared. Subsequently, I introduce risk in the model and construct 95 percent confidence intervals for\nthe outbreak costs, and I rank the outcomes of the alternative strategies using stochastic dominance criteria. In addition, during both phases, I develop and estimate the breakeven probability for an event where ex-ante fixed costs of vaccine stockpiling are\njustified by the reduction in disease event damages.\n\nResults under deterministic disease spread assumption suggest that the vaccination strategy lowers the cost of outbreaks as opposed to the current strategy. This\nhappens because vaccination reduces the number of culled and quarantined flocks. The study is conducted in three locations, yielding the finding that the costs of an outbreak vary depending on the densities of poultry flocks. I also find that when consumer\ndemand shifts due to the outbreak, the costs are much larger. Finally, I find that ex-ante\nvaccine stockpiling is justified for all the sub-regions if the probability of outbreak\nexceeds 0.07.\nThe stochastic disease spread assumption results also show that the vaccination\nstrategy dominates in first degree stochastic dominance sense. Consistent with stochastic\ndominance results, the 95 percent confidence intervals have narrower ranges under the\nvaccination strategy than without it. Finally, the distribution of the breakeven probability\nfor vaccine stocking has a mode of 0.07 and that the probability is accurate with 82 percent\nlikelihood. However, the threshold varies with the disease transmission parameters and\ncould reach up to 0.32.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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) | 0.002 | 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".