COVID-19: Research Directions for Non-Clinical Aerosol-Generating Facilities in the Built Environment
Bibliographic record
Abstract
Physical contact and respiratory droplet transmission have been widely regarded as the main routes of COVID-19 infection. However, mounting evidence has unveiled the risk of aerosol transmission of the virus. Whereas caution has been taken to avoid this risk in association with clinical facilities, facilities such as spa pools and Jacuzzis, which are characterized by bubble-aerosol generation, high bather loads, and limited turnover rates, may promote aerosol transmission. Focusing on these non-clinical facilities in the built environment, a review study was undertaken. First, the typical water disinfection and ventilation-aided operations for the facilities were illustrated. Second, cross comparisons were made between the applicable standards and guidelines of the World Health Organization and countries including Australia, Canada, China, the United Kingdom, and the United States. The similarities and differences in their water quality specifications, ventilation requirements, and air quality enhancement measures were identified; there were no specific regulations for preventing aerosol transmission at those aerosol-generating facilities. Third, a qualitative review of research publications revealed the emergence of studies on potential air-borne transmission of COVID-19, but research on built facilities posing high risks of aerosol transmission remains scant. This study’s results inform key directions for future research on abating aerosol transmission of COVID-19: the development of bespoke personal protective equipment and engineering and management controls on water quality, ventilation, and air quality.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".