Environmental surveillance for SARS-CoV-2 for outbreak detection in hospital: A single centre prospective study
Bibliographic record
Abstract
ABSTRACT Identifying COVID-19 outbreaks in hospitals at an early stage requires active surveillance. Our objective was to assess whether floor swabs correlated with COVID-19 outbreak status in hospital. We swabbed the floors of an inpatient ward at Mount Sinai Hospital for 32 weeks, from October 31, 2022 to June 15, 2023 and RT-qPCR analysis provided a quantification cycle of detection for each positive swab. 182 swabs were processed for SARS CoV-2, of which 98.4% were positive. Two COVID-19 outbreaks were declared during the study period. The median viral copy number was 210 (IQR, 49 to 1018) during non-outbreak periods and 653 (IQR, 300 to 1754) during outbreak periods. Analyzing the number of viral copies of SARS-CoV-2, instead of percentage positivity, gave a clearer view of changes in outbreak status over time, thereby illustrating the benefits of this approach to monitor pathogen load in hospital settings.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".