Changes in norovirus genotype diversity in gastroenteritis outbreaks in Alberta, Canada: 2012–2018
Bibliographic record
Abstract
BACKGROUND: The emergence of norovirus genotype GII.4 variants has been associated with gastroenteritis pandemics worldwide, prompting molecular surveillance for early detection of novel strains. In this study, we aimed to analyze the outbreak activity of norovirus and characterize the norovirus strains circulating in Alberta between July 2012 and February 2018. METHODS: Stool samples from gastroenteritis outbreaks in Alberta were tested for norovirus at the Provincial Laboratory for Public Health using a multiplex real time-RT PCR assay. The ORF1 and ORF2-genotypes of norovirus positive samples were assigned based on phylogenetic analyses of partial polymerase and capsid sequences, respectively. RESULTS: A total of 530 norovirus outbreaks were identified. During July 2012 and June 2017 there was a gradual decrease in the annual number of GII.4 outbreaks, however, outbreak numbers increased from June 2017-February 2018. Four novel strains emerged: GII.17 Kawasaki in July 2014-June 2015, GII.P16/GII.4 Sydney in July 2015-June 2016, GII.P16/GII.2 and GII.P4 New Orleans/GII.4 Sydney in July 2016-June 2017. GII.Pe/GII.4 Sydney was the single predominant strain responsible for the majority (over 50%) of all norovirus outbreaks up to June 2015. Between June 2017 and February 2018, GII.P16/GII.4 Sydney was the leading strain causing 63% of all norovirus outbreaks. CONCLUSIONS: GII.4 stands as the predominant capsid genotype causing a large majority of the norovirus outbreaks in early 2018. An increase in genotype diversity was observed in the last years, characterized by a high circulation of non-GII.4 strains and GII.4 recombinants.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".