Predicting COVID-19 cases across a large university campus using built environment surveillance
Bibliographic record
Abstract
Background: Environmental surveillance of SARS-CoV-2 via wastewater has become an invaluable tool for population-level surveillance. Built environment sampling may provide complementary spatially refined detection for viral surveillance in congregate settings such as universities. Methods: We conducted a prospective environmental surveillance study at the University of Ottawa between September 2021 and April 2022. Floor surface samples were collected twice weekly from six university buildings and analyzed for the presence of SARS-CoV-2 using quantitative PCR. A Poisson regression was used to model the campus-wide COVID-19 cases predicted from the fraction of floor swabs positive for SARS-CoV-2 RNA, building CO2 levels, Wi-Fi usage, and SARS-CoV-2 RNA levels in city wastewater. Building-level cases were modelled using viral copies detected in floor samples as a predictor. Results: Over the 32-week study period, we collected 554 floor swabs at six university buildings. Overall, 13% of swabs were PCR positive for SARS-CoV-2, with positivity ranging between 4.8% and 32.7% among buildings. Both floor swab positivity (Spearman r = 0.74, 95% CI 0.53 to 0.87) and city wastewater signal (Spearman r = 0.50, 95% CI 0.18 to 0.73) positively correlated with on-campus COVID-19 cases. In addition, built environment detection was a predictor of cases linked to individual university buildings. Conclusions: Detection of SARS-CoV-2 RNA on floors and viral RNA levels in city-wide wastewater were strongly associated with the incidence of COVID-19 cases on a university campus. These data suggest a potential role for institutional built environment sampling, used together with wastewater surveillance, for predicting COVID-19 cases at both campus-wide and building-level scales.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".