City-wide Built Environment SARS-CoV-2 Detection for COVID-19 Surveillance
Bibliographic record
Abstract
ABSTRACT Built environment surveillance has shown promise for monitoring COVID-19 burden at granular geographic scales, but its utility for surveillance across larger areas and populations is unknown. Our study aims to evaluate the role of built environment detection of SARS-CoV-2 for the surveillance of COVID-19 across broad geographies and populations. We conducted a prospective city-wide sampling study to examine the relationship between SARS-CoV-2 on floors and COVID-19 burden. We used non-parametric correlation analyses and linear models to evaluate associations between SARS-CoV-2 signals and COVID-19 outcomes, across multiple locations/populations over time. Sampling sites included schools, libraries, and emergency departments in the capital city of Ottawa, Canada, from October 2022 to March 2023. Floor sampling was performed across spaces, and outcomes were evaluated at aggregate levels. Detection (presence/absence) and quantification (viral load) of SARS-CoV-2 was determined by reverse-transcriptase polymerase chain reaction conducted on floor swabs collected weekly at study locations. The main outcomes, and measures of COVID-19 burden, were (1) weekly regional wastewater signal and (2) weekly admitted COVID-19 patient census from hospitals. We collected 1,863 built environment floor samples over the 6-month study period, with an overall swab positivity for SARS-CoV-2 of 45% (95%CI 43%-48%). We found a strong correlation between overall built environmental swab viral load and hospital COVID-19 census (Spearman’s r =0.64, p =0.0017), but no correlation between regional wastewater and hospital COVID-19 census (Spearman’s r =-0.15, p=0.5). We found a strong correlation (Spearman’s r =0.76, p =9x10 -5 ) between hospital-specific swab viral copy number and hospital-specific COVID-19 census, which is a likely driver of the overall association between swab load and census. Built environment surveillance of SARS-CoV-2 from hospitals was strongly correlated with hospital burden with improved delineation of hospital COVID-19 cases compared to regional wastewater. These findings support the use of built environment surveillance for quantification of infectious burden amongst institutionalized groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".