Viral load of SARS-CoV-2 in droplets and bioaerosols directly captured during breathing, speaking and coughing
Bibliographic record
Abstract
Determining the viral load and infectivity of SARS-CoV-2 in macroscopic respiratory droplets, bioaerosols, and other bodily fluids and secretions is important for identifying transmission modes, assessing risks and informing public health guidelines. Here we show that viral load of SARS-CoV-2 Ribonucleic Acid (RNA) in participants' naso-pharyngeal (NP) swabs positively correlated with RNA viral load they emitted in both droplets >10 [Formula: see text] and bioaerosols <10 [Formula: see text] directly captured during the combined expiratory activities of breathing, speaking and coughing using a standardized protocol, although the NP swabs had [Formula: see text] 10[Formula: see text] more RNA on average. By identifying highly-infectious individuals (maximum of 18,000 PFU/mL in NP), we retrieved higher numbers of SARS-CoV-2 RNA gene copies in bioaerosol samples (maximum of 4.8[Formula: see text] gene copies/mL and minimum cycle threshold of 26.2) relative to other studies. However, all attempts to identify infectious virus in size-segregated droplets and bioaerosols were negative by plaque assay (0 of 58). This outcome is partly attributed to the insufficient amount of viral material in each sample (as indicated by SARS-CoV-2 gene copies) or may indicate no infectious virus was present in such samples, although other possible factors are identified.
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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 | 0.000 |
| 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) | 0.001 | 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".