Analysis of simulated outbreak data and spatial analysis of highly pathogenic avian influenza for preparedness planning and policy
Bibliographic record
Abstract
The first objective of this research was to develop and evaluate an approach to analyze and communicate the results of a large number of simulated outbreaks of highly pathogenic avian influenza (HPAI) to decision-makers and policy-makers, using the North American Animal Disease Spread Model (NAADSM), and to make recommendations on the most effective HPAI control policy for Ontario, Canada, specifically, on the effect of stamping-out and ring-culling strategies on the magnitude of an HPAI outbreak. Negative binomial regression analysis was used to identify significant predictors of the number of farms infected for each scenario. Interaction plots were developed from the output of the negative binomial regression analysis, to facilitate communication of simulation results to policy-makers and to analyze the relationship between movement restrictions and destruction strategy. Negative binomial regression analysis was appropriate for handling the right-skewed count data of the simulated HPAI outbreaks in Ontario, while interaction plots were an appropriate visualization tool for communication to policy-makers. For policy development, the modeling results suggested that stamping-out of the infected/detected flocks, without ring-culling, in combination with movement restrictions on direct and indirect contacts, would be the most appropriate policy for Ontario.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".