Detection of COVID-19 Outbreaks in Long-Term Care Homes Using Built Environment Testing for SARS-CoV-2: A Multicentre Prospective Study
Bibliographic record
Abstract
ABSTRACT Background Environmental surveillance of SARS-CoV-2 via wastewater has become an invaluable tool for population-level surveillance of COVID-19. Built environment sampling may provide a more spatially refined approach for surveillance of COVID-19 in congregate living settings and other high risk settings (e.g., schools, daycares). Methods We conducted a prospective study in 10 long-term care homes (LTCHs) across three cities in Ontario, Canada between September 2021 and May 2022. Floor surfaces were sampled weekly at multiple locations (range 10 to 24 swabs per building) within each building and analyzed for the presence of SARS-CoV-2 using RT-qPCR. The exposure variable was detection of SARS-CoV-2 on floors. The primary outcome was the presence of a COVID-19 outbreak in the week that floor sampling was performed. Results Over the 9-month study period, we collected 3848 swabs at 10 long-term care homes. During the study period, 19 COVID-19 outbreaks occurred with 103 cumulative weeks under outbreak. During outbreak periods, the proportion of floor swabs positive for SARS-CoV-2 was 50% (95% CI: 47-53) with a median quantification cycle of 37.3 (IQR 35.2-38.7). During non-outbreak periods the proportion of floor swabs positive was 18% (95% CI:17-20) with a median quantification cycle of 38.0 (IQR 36.4-39.1). Using the proportion of positive floor swabs for SARS-CoV-2 to predict COVID-19 outbreak status in a given week, the area under the receiver operating curve (AUROC) was 0.85 (95% CI: 0.78-0.92). Using thresholds of ≥10%, ≥30%, and ≥50% of floor swabs positive for SARS-CoV-2 yielded positive predictive values for outbreak of 0.57 (0.49-0.66), 0.73 (0.63-0.81), and 0.73 (0.6-0.83) respectively and negative predictive values of 0.94 (0.87-0.97), 0.85 (0.78-0.9), and 0.75 (0.68-0.81) respectively. Among 8 LTCHs with an outbreak and swabs performed in the antecedent week, 5 had positive floor swabs exceeding 10% at least five days prior to outbreak identification. For 3 of these 5 LTCHs, positivity of floor swabs exceeded 10% more than 10 days before the outbreak being identified. Conclusions Detection of SARS-CoV-2 on floors is strongly associated with COVID-19 outbreaks in LTCHs. These data suggest a potential role for floor sampling in improving early outbreak identification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".