Estimating indoor airborne concentrations of SARS-CoV-2 RNA using quantitative filter forensics
Bibliographic record
Abstract
This investigation used portable air cleaners (PACs) and quantitative filter forensics (QFF) to assess week-long average airborne concentrations of SARS-CoV-2 RNA in homes occupied with COVID-symptomatic individuals, classrooms, and dining locations throughout Toronto, Canada. PACs were deployed for one week each, and dust from filters was collected via vacuuming. SARS-CoV-2 RNA from filter dust was quantified using reverse transcription-polymerase chain reaction (RT-PCR). RNA quantities and PAC metadata were used to estimate airborne concentrations of SARS-CoV-2 RNA. The highest concentrations of RNA were found in isolation rooms (median concentration=8.68 RNA copies/m3). Classrooms had lower concentrations during summer than in fall and winter (median concentration of sampling weeks when RNA was present = 0.02 RNA copies/m3 and 0.11 RNA copies/m3, respectively), which may be attributed to differences in classroom occupancy among other factors. Limitations include unknown recovery efficiency of RNA from filters and the dynamics of concentrations due to the temporal averaging provided by QFF. Our results are consistent with previous research highlighting the effectiveness of isolation in preventing distribution of high concentrations of SARS-CoV-2 RNA throughout a home. Overall, QFF is a beneficial tool for environmental sampling of respiratory airborne virus RNA such as SARS-CoV-2 RNA, that can be implemented for long-term sampling in mixed-occupancy environments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".