Detection of Covid-19 Outbreaks Using Built Environment Testing for SARS-CoV-2
Bibliographic record
Abstract
BACKGROUND: Environmental surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) through wastewater has become a useful tool for population-level surveillance. Built environment sampling may provide a more spatially refined approach for surveillance in congregate living settings. METHODS: We conducted a prospective study in 10 long-term care homes (LTCHs) between September 2021 and November 2022. Floor surfaces were sampled weekly at multiple locations within each building and analyzed for the presence of SARS-CoV-2 using quantitative reverse transcriptase polymerase chain reaction. The primary outcome was the presence of a coronavirus disease 2019 (Covid-19) outbreak in the week that floor sampling was performed. RESULTS: Over the 14-month study period, we collected 4895 swabs at 10 LTCHs. During the study period, 23 Covid-19 outbreaks occurred with 119 cumulative weeks under outbreak. During outbreak periods, the proportion of floor swabs that were positive for SARS-CoV-2 was 54.3% (95% confidence interval [CI], 52 to 56.6), and during non-outbreak periods it was 22.3% (95% CI, 20.9 to 23.8). Using the proportion of floor swabs positive for SARS-CoV-2 to predict Covid-19 outbreak status in a given week, the area under the receiver-operating characteristic curve was 0.84 (95% CI, 0.78 to 0.9). Among 10 LTCHs with an outbreak and swabs performed in the prior week, eight had positive floor swabs exceeding 10% at least 5 days before outbreak identification. For seven of these eight LTCHs, positivity of floor swabs exceeded 10% more than 10 days before the outbreak was 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.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".