Distribution of West Nile virus cases in horses reveals different spatiotemporal patterns in eastern and western Canada
Bibliographic record
Abstract
OBJECTIVE: West Nile virus (WNV) became notifiable in horses in 2003 in Canada and has been reported every year since. The objective of this study was to describe the spatiotemporal distribution of WNV in horses between 2003 and 2020 in Canada. ANIMALS: The 848 symptomatic and laboratory-confirmed WNV cases in horses reported to the Canadian Food Inspection Agency between 2003 and 2020. METHODS: Canada was divided into eastern and western regions for analysis. For each case, location and date of notification were captured. Triennial maps were made to describe the spatiotemporal distribution and expansion of reported cases. The association between year and latitude of cases was investigated with simple linear regressions, and space-time clusters were detected with a permutation scan test. RESULTS: Most of the western region showed an extended distribution of WNV cases from 2003 to 2005 and a high recurrence of cases at the census division level. In the eastern region, the expansion of cases was gradual, with new infected census divisions mostly contiguous to previous ones. There was no association between year and latitude of cases. Six spatiotemporal clusters were detected. CLINICAL RELEVANCE: This study confirmed the endemicity of WNV in parts of both regions with local peaks in risk varying in time. Prevention and control efforts should focus on previously infected areas based on the spatiotemporal regional distribution patterns. Incursions of WNV to new areas should also be anticipated. These findings could also contribute to enhancing monitoring and prevention of WNV infections in an integrated surveillance system.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".