The Omicron variant significantly increases viral load emissions in healthcare settings: implication for healthcare workers
Bibliographic record
Abstract
BACKGROUND: The SARS-CoV-2 Omicron variant is transmitted via contaminated droplets and aerosols, raising concerns in healthcare settings where poor ventilation and high patient density can increase airborne viral load. AIM: This study aimed to assess real-world exposure of healthcare workers to COVID-19-positive patients isolated in designated hospital areas, using continuous 24-h air sampling. METHODS: Air sampling was conducted inside 10 hospital rooms hosting a succession of 38 patients who tested positive for SARS-CoV-2. Sampling was performed using 37-mm cassettes placed near the patients' heads. The Omicron variant in the air was detected by RT-qPCR, with results expressed as emission rates based on air changes per hour for each room and correlated with the onset of patients' symptoms. FINDINGS: genomes/h per patient. Expectoration was the sole symptom significantly affecting emission rates, with patient suffering from it exhibiting values three times higher than patients without. Additionally, the room accounted for half of the variance in emission rates, suggesting that the number of patients and the room's prior usage are key determinants of viral particle exposure. CONCLUSION: Our findings indicate that healthcare workers face significant exposure when providing care in rooms with positive patients, even when mechanically ventilated. Greater attention should be given to treating and managing these spaces to reduce the potential for viral transmission toward healthcare workers.
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 | 0.001 | 0.001 |
| 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".