Epidemiology of norovirus and viral gastroenteritis in Ontario, Canada, 2009–2014
Bibliographic record
Abstract
BACKGROUND: Norovirus is the most common cause of acute gastroenteritis in Canada. The illness causes great morbidity and high societal costs. The objective of this article is to describe the epidemiology of norovirus in the province of Ontario, Canada from 2009 to 2014. METHODS: To assess activity of norovirus and viral gastroenteritis (VGE) in Ontario, three datasets were acquired from the provincial government: two traditional surveillance datasets (outbreak and laboratory) and syndromic surveillance data (telehealth), all spanning 2009-2014. All outbreaks, laboratory submissions and telehealth calls were first assessed for total VGE. Norovirus and norovirus-like illness totals were calculated as a proportion of VGE to estimate agent-specific activity levels. Affected institution types, sexes and age groups were also analyzed. RESULTS: Between 2009 and 2014, 41.5% of VGE outbreaks, 63.4% of VGE laboratory submissions and 36.6% of all acute gastroenteritis-related (not restricted to viral causes) telehealth calls were attributed to norovirus and norovirus-like illness in Ontario. The most commonly affected institution type was long-term care homes and the most commonly affected age groups were younger (younger than five years) and older (older than 65 years) individuals. Females were slightly more frequently affected than males. CONCLUSION: Norovirus and norovirus-like illnesses were the leading cause of VGE in Ontario between 2009 and 2014. They comprised the greatest percentage of VGE when compared with all other VGE-associated viruses. Additional work is needed to determine all component costs and necessary public health actions to reduce the burden of disease.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".