Norovirus trends in British Columbia from 2021 to 2022: the relationship between wastewater surveillance and clinical outbreak data during the COVID-19 pandemic
Bibliographic record
Abstract
Norovirus causes frequent global outbreaks and significant financial and operational burdens on healthcare systems. Albeit, norovirus is a non-notifiable disease in many jurisdictions. Surveillance is focused on community outbreaks rather than routine monitoring, making assessment of community transmission difficult. Wastewater based epidemiology (WBE) can identify disease surges and confirm circulation of new variants of SARS-CoV-2, and could be applied to norovirus. This study sought to identify appropriate normalization techniques for norovirus in wastewater, evaluate the relationship between wastewater and outbreak data, and assess norovirus trends in 2021 and 2022 in British Columbia, Canada. A total of 1093 influent wastewater samples, from five municipal wastewater plants, were collected between January 2021 and November 2022. Samples were tested using qRT-PCR for norovirus genogroups I and II. Clinical outbreak data from 2021 to 2022 were significantly correlated to normalized norovirus levels in wastewater. During the first six months of 2022, the number of norovirus outbreaks and the concentrations of norovirus in wastewater were significantly higher than the same timeframe in 2021 (p = 0.016 and p < 0.0001 respectively). Easing COVID-19 countermeasures in 2022 may explain higher norovirus levels and outbreaks that year. WBE is useful for monitoring norovirus within the community and addresses gaps in clinical disease reporting.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".