Influenza virus RNA recovered from droplets and droplet nuclei emitted by adults in an acute care setting
Bibliographic record
Abstract
Transmission in hospital settings of seasonal influenza viruses and novel agents such as the Middle East respiratory syndrome coronavirus (MERS-CoV) is well-described but poorly understood. The characterization of potentially infectious bio-aerosols in the healthcare setting remains an important yet ill-defined factor in the transmission of respiratory viruses. Empiric data describing the distribution of bio-aerosols enable discernment of potential exposure risk to respiratory viruses. We sought to determine the distribution of influenza virus RNA emitted into the air by participants with laboratory-confirmed influenza, and whether these emissions had the potential to reach healthcare workers' breathing zones. Two-stage cyclone bio-aerosol samplers from the Centers for Disease Control and Prevention - National Institute for Occupational Safety and Health were placed 0.5-1.0 m (near field) and 2.1-2.5 m (far field) from infected patient participants, as well as in the corridor immediately outside their rooms. In addition, healthcare worker participants providing care to infected participants were recruited to wear a polytetrafluoroethylene (PTFE) filter cassette in their breathing zones. Viral RNA was detected from the air emitted by 37.5% of the 16 participants infected with influenza virus and distributed both in near and far fields and in all tested particle sizes (<1 µm, 1-4 µm, and >4 µm). Viral RNA was recovered in droplet nuclei and beyond 1 m from naturally-infected participants in the healthcare setting and from the breathing zone of one healthcare worker. There was no correlation between patient participant nasal viral load and recovery of viral RNA from the air, and we did not identify any significant association between RNA detection from the air and patient demographics or clinical presentation. A more substantial study is required to identify patient determinants of virus emission into the air and delineate implications for evidence-based policy for prevention and control.
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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 | 0.000 | 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".