Indoor air sampling strategies by active bioaerosol samplers: a case study to detect SARS-CoV-2 in hospital settings
Bibliographic record
Abstract
This study demonstrates the applicability of air sampling for the detection of SARS-CoV-2 in a hospital by means of active bioaerosol samplers following a specifically designed air sampling strategy based on digital mapping of the architectural layout of the ward to minimize disruptions of health care activities and reducing operator risks. Prior to the experimental study, some model tests were conducted using the air sampler with a tunable flow rate to determine the most suitable real time polymerase chain reaction (RT-PCR) based detection method. Preliminary results showed the need to perform intensive extraction protocols combined with Real-time reverse transcription PCR (rRT-PCR), rather than conventional, to enhance sensitivity. The experimental study was conducted within the general medicine ward of Spedali Civili Hospital in Brescia during the winter of 2021/2022, a period marked by a high prevalence of COVID-19 cases using three active air sampling devices: Coriolis Compact, Coriolis Micro®, and BioSpot GEM®. Environmental parameters, such as room size, occupancy, ventilation rates, and activities performed during sampling, and patients’ conditions were documented to contextualize the findings. The virus was detected in a few rooms with concentrations ranging from 1171 to 2225 copies/m 3 . These findings support the integration of routinary air sampling as tools for control and assessment of transmission risks, not only for SARS-CoV-2 but generalized to all airborne pathogens, supporting patient management and infection control in health care settings.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".