Investigating the Presence of SARS-CoV-2 on the Surfaces, Fomites, and in Indoor Air of a Referral COVID-19 Hospital, Shiraz, Iran
Bibliographic record
Abstract
Background: Coronavirus disease (COVID-19) is an immensely transmissible viral infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). This study aimed to assess the presence of SARS-CoV-2 in the indoor air, on the surfaces, and on the fomites of a COVID-19 referral hospital in Shiraz, Iran.Methods: In this cross-sectional study, indoor air sampling was conducted utilizing a standard midget impinger containing 15 ml of viral transfer medium (VTM) equipped with a sampling pump with a flow rate of 10 L min-1 for 60 minutes. Surfaces and fomites were sampled using sterile polyester swabs. The realtime reverse transcription-polymerase chain reaction (rRT-PCR) was utilized to detect SARS-CoV-2.Results: The RNA of SARS-CoV-2 was detected in about 41.2% indoor air and 32% swab samples. Four out of the six (66.7%) indoor air samples up to a distance of 2 meters from the patient’s bed in intensive care units (ICU-1, ICU-3), accident and emergency (A&E-2), and negative pressure rooms were positive for SARS-CoV-2 RNA. All air samples within 2 to 5 meters of the patient’s bed were negative.Conclusion: This study’s results did not support the airborne SARS-CoV-2 transmission; However, it showed contamination of surfaces and fomites in the studied hospital’s wards.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".