Investigating the spatial risk distribution of West Nile virus disease in birds and humans in southern Ontario from 2002 to 2005
Bibliographic record
Abstract
BACKGROUND: The West Nile virus (WNv) became a veterinary public health concern in southern Ontario in 2001 and has continued to threaten public health. Wild bird mortality has been shown to be an indicator for tracking the geographic distribution of the WNv. The purpose of this study was to investigate the latent risk distribution of WNv disease among dead birds and humans in southern Ontario and to compare the spatial risk patterns for the period 2002-2005. The relationship between the mortality fraction in birds and incidence rate in humans was also investigated. METHODS: Choropleth maps were created to investigate the spatial variation in bird and human WNv risk for the public health units of southern Ontario. The data were smoothed by empirical Bayesian estimation before being mapped. Isopleth risk maps for both the bird and human data were created to identify high risk areas and to investigate the potential relationship between the WNv mortality fraction in birds and incidence rates in humans. This was carried out by the geostatistical prediction method of kriging. A Poisson regression analysis was used to model regional human WNv case counts as a function of the spatial coordinates in the east and north direction and the regional bird mortality fractions. The presence of disease clustering and the location of disease clusters were investigated by the spatial scan test. RESULTS: The isopleth risk maps exhibited high risk areas that were relatively constant from year to year. There was an overlap in the bird and human high risk areas, which occurred in the central-west and south-west areas of southern Ontario. The annual WNv cause-specific mortality fractions in birds for 2002 to 2005 were 31.9, 22.0, 19.2 and 25.2 positive birds per 100 birds tested, respectively. The annual human WNv incidence rates for 2002 to 2005 were 2.21, 0.76, 0.13 and 2.10 human cases per 100,000 population, respectively. The relative risk of human WNv disease was 0.72 times lower for a public health unit that was 100 km north of another public health unit. The relative risk of human WNv disease increased by the factor 1.44 with every 10 positive birds per 100 tested. The scan statistic detected disease cluster in the bird and human data. The human clusters were not significant, when the analysis was conditioned on the bird data. CONCLUSION: The study indicates a significant relationship between the spatial pattern of WNv risk in humans and birds.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".