Assessment of Effectiveness of Control Strategies Against Simulated Outbreaks of Highly Pathogenic Avian Influenza in Ontario, Canada
Bibliographic record
Abstract
The North American Animal Disease Spread Model (NAADSM) is a stochastic model framework developed to simulate the spread of highly contagious diseases of livestock and poultry, such as foot-and-mouth disease and highly pathogenic avian influenza (HPAI). The objective of this study was to make recommendations on the most effective HPAI control policy for Canada, specifically, on the effect of different speeds of detection, effectiveness of movement restrictions and stamping-out and ring-culling strategies on the magnitude of an HPAI outbreak. In addition, the effect of introduction of infection in a range of multiple farms simultaneously was also evaluated. A total of 21 060 scenarios, defined as different combinations of parameters for various epidemiological conditions and control measures, were created to simulate the number of poultry flocks that would become infected as a result of an incursion of HPAI. Each scenario was parameterized in NAADSM and replicated 1000 times, generating the median number of flocks infected at the end of the simulated outbreak for each scenario. Negative binomial regression analysis was used to model significant explanatory variables of the median number of flocks infected at the end of each simulated outbreak for each of the 21 060 scenarios. The final model included the following explanatory variables: number and type initially infected flock(s), density of flocks within the county where the initially infected flock(s) was located, probability of transmission through indirect contact, subclinical spread of the infection, speed of detection and a two-way interaction between intensity of bird destruction strategy and movement restriction effectiveness to reduce transmission through direct and indirect contacts. The modelling results suggested that stamping out of the detected infected flocks, without ring culling, in combination with effective 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 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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".