West Nile virus illness in Ontario, Canada: 2017
Bibliographic record
Abstract
BACKGROUND: In Canada, the annual incidence rates of West Nile virus (WNV) illness have fluctuated over the last 15 years. Ontario is one of the provinces in Canada most affected by WNV and, as a result, has implemented robust mosquito and human surveillance programs. OBJECTIVE: To summarize and discuss the epidemiology of WNV illness in Ontario, Canada in 2017, with comparisons to previous years. METHODS: Case data were obtained from the provincial integrated Public Health Information System. Provincial and public health unit (PHU)-specific incidence rates by year were calculated using population data extracted from intelliHEALTH Ontario. RESULTS: In 2017, the incidence of WNV illness in Ontario was 1.1 cases per 100,000 population, with 158 confirmed and probable cases reported by 27 of the province's 36 PHUs. This is the highest rate since 2013, but less than the rate in 2012 (2.0 cases per 100,000 population). Incidence rates in 2017 were highest in Windsor-Essex County and in PHUs in eastern Ontario. While the seasonality is consistent with previous years, the number of cases reported between July and September 2017 was above expected. Most cases were in older age groups (median: 58 years old) and males (59.5% of provincial total); cases with severe outcomes (neurological complications, hospitalizations, deaths) were also disproportionately in older males. CONCLUSION: WNV illness continues to be an ongoing burden in Ontario. The increase in the number of cases reported in 2017, and the increased number of PHUs reporting cases, suggests changing and expanding risk levels in Ontario. Continued mosquito and human surveillance, increased awareness of preventive measures, and early recognition and treatment are needed to mitigate the impact of WNV infections.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".