Estimating the potential for disease spread in horses associated with an equestrian show in Ontario, Canada using an agent-based model
Bibliographic record
Abstract
Participation in equestrian shows provides opportunities for contact between horses, increasing the risk of disease introduction and spread within the population. The magnitude of a potential outbreak, and the impact of disease prevention and control strategies, can be estimated using simulation modeling. The objectives of this study were to (1) examine the potential spread of equine influenza in a network of horses associated with a 2-day equestrian show in Ontario, Canada; and (2) determine the effectiveness of several interventions during a simulated outbreak. A discrete-event, continuous-time, stochastic agent-based simulation model was constructed to represent horses associated with the show, including those in attendance at the show, and those that were not in attendance but co-boarded with attending horses at their home facilities. At the beginning of each simulation run, one random horse in attendance at the show was infected with equine influenza. In the absence of interventions, the median attack rate was 0.029 (IQR: 0.016-0.056; mean: 0.043; 95% CI: 0.040-0.044) and the average outbreak duration was 19.58 days (95% CI: 19.31-19.85). The most effective intervention was the implementation of either a 5-day or 14-day quarantine period, which both resulted in the same median attack rate of 0.0026 (IQR: 0.0013-0.0039), although the mean attack rates differed (mean: 0.0043, 95% CI: 0.0039-0.0046; and mean: 0.0029, 95% CI: 0.0028-0.0029; respectively). In instances where implementing either a 5-day or 14-day quarantine period would not be feasible, quarantine for shorter time periods was effective when combined with targeted increases in initial facility-level vaccine coverage. The combined implementation of a 2-day quarantine period and an increased vaccine coverage of 75% in facilities with four or more owners resulted in a median attack rate of 0.013 (IQR: 0.0052-0.026; mean: 0.022; 95% CI: 0.020-0.024). This study demonstrates a relative comparison of intervention effectiveness during a simulated outbreak of equine influenza in a population of horses associated with an equestrian show. The results have the potential to inform and improve the current strategies used to prevent the introduction and spread of disease within the equine population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".