Wastewater surveillance for earlier detection of seniors congregate living COVID-19 outbreaks in Peterborough, Ontario
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has disproportionately affected seniors living in congregate living settings. The evolving surveillance context has led to novel use of wastewater surveillance to monitor levels of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in these settings. This study presents a pilot of upstream congregate living wastewater surveillance of SARS-CoV-2 for the detection of COVID-19 outbreaks and the effects of early public health interventions. We monitored localized wastewater SARS-CoV-2 levels from four congregate living settings March 15, 2021 to October 1, 2022 and correlated these levels with suspected and confirmed COVID-19 outbreaks determined by other methods. We identified five wastewater signals that correlated with confirmed outbreaks and three wastewater signals that did not correlate with subsequent outbreaks. In the five confirmed outbreaks, the wastewater signal was detected 2-10 days (median, five days) prior to confirmation of the outbreak by case testing. This pilot demonstrates upstream sampling for SARS-CoV-2 in wastewater may effectively detect outbreaks prior to their detection through symptomatic case testing and could support a balanced approach to outbreak response in congregate living settings, leading to increased wellbeing of these residents.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".