Surface and air contamination with SARS-CoV-2 from hospitalized COVID-19 patients in Toronto, Canada
Bibliographic record
Abstract
Summary Background The aim of this prospective cohort study was to determine the burden of SARS-CoV-2 in air and on surfaces in rooms of patients hospitalized with COVID-19, and to identify patient characteristics associated with SARS-CoV-2 environmental contamination. Methods Nasopharyngeal swabs, surface, and air samples were collected from the rooms of 78 inpatients with COVID-19 at six acute care hospitals in Toronto from March to May 2020. Samples were tested for SARS-CoV-2 viral RNA and cultured to determine potential infectivity. Whole viral genomes were sequenced from nasopharyngeal and surface samples. Association between patient factors and detection of SARS-CoV-2 RNA in surface samples were investigated using a mixed-effects logistic regression model. Findings SARS-CoV-2 RNA was detected from surfaces (125/474 samples; 42/78 patients) and air (3/146 samples; 3/45 patients) in COVID-19 patient rooms; 17% (6/36) of surface samples from three patients yielded viable virus. Viral sequences from nasopharyngeal and surface samples clustered by patient. Multivariable analysis indicated hypoxia at admission, a PCR-positive nasopharyngeal swab with a cycle threshold of ≤30 on or after surface sampling date, higher Charlson co-morbidity score, and shorter time from onset of illness to sample date were significantly associated with detection of SARS-CoV-2 RNA in surface samples. Interpretation The infrequent recovery of infectious SARS-CoV-2 virus from the environment suggests that the risk to healthcare workers from air and near-patient surfaces in acute care hospital wards is likely limited. Surface contamination was greater when patients were earlier in their course of illness and in those with hypoxia, multiple co-morbidities, and higher SARS-CoV-2 RNA concentration in NP swabs. Our results suggest that air and surfaces may pose limited risk a few days after admission to acute care hospitals.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".