Viral cultures for assessing airborne infectiousness of SARS-CoV-2: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: There is uncertainty about the quantification, viability and infectivity of SARS-CoV-2 in air samples. Our objective was to systematically review the evidence for air sample virus infectiousness with high-level confirmatory studies. METHODS: We conducted literature searches in LitCovid, medRxiv, PubMed, the WHO Covid-19 databases, and Google Scholar. We included studies that assessed viral infectiousness in the air using viral culture or serial qRT-PCR with or without genomic sequencing. Our primary outcome was the proportion of culture-positive air samples of SARS-CoV-2. Secondary outcomes explored the relationship between infectiousness and Cycle threshold (Ct). We used published methods for assessing quality, and R software for meta-analysis. RESULTS: = 52.3%; p = 0.001). The data were not sufficient to compute a threshold for infectivity, or to explore the relationship between distance and infectiousness. CONCLUSIONS: The proportion of positive SARS-CoV-2 viral cultures following positive RNA samples in the air is low, suggesting that while viral RNA may be present, the likelihood of detecting culturable, infectious viruses is substantially lower. Our findings underscore the need for standardized guidelines to assess and report the infectivity and potential for transmissibility of airborne viruses, including the consistent reporting of Ct values and methods to mitigate bias.
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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 | 0.030 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.028 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".