Evaluation of a West Nile virus risk-assessment tool used at a local health unit
Bibliographic record
Abstract
In Ontario, public health units collect surveillance data on vector-borne diseases (VBD) to determine emerging trends and develop VBD management strategies. Risk-assessment tools that are simple and easily applied can provide public health practitioners with objective evaluations of the risk of West Nile virus (WNV) activity in their jurisdiction. This study was conducted to evaluate an existing WNV risk-assessment tool used by a public health unit in southern Ontario. The purpose of this study was to: (i) describe the trends for WNV in mosquito and human cases in the Region of Peel, Ontario, Canada, and (ii) investigate the ability of the risk-assessment tool to predict positive human cases and positive mosquito traps in the following weeks. Data were collected from 2011 to 2016 and analysed using simple descriptive statistics and Fisher’s exact tests. This study found the tool includes variables that are not significant in predicting WNV activity in the following weeks. The current tool should be revised to remove variables that are not significant in predicting risk and add additional variables that have been shown to be effective predictors in other studies, such as rainfall and human WNV cases in the previous year.
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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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".