COVID-19 pandemic lesson learned- critical parameters and research needs for UVC inactivation of viral aerosols
Bibliographic record
Abstract
The COVID-19 pandemic highlighted public awareness of airborne disease transmission in indoor settings and emphasized the need for reliable air disinfection technologies. This increased awareness will carry in the post-pandemic era along with the ever-emerging SARS-CoV variants, necessitating effective and well-defined protocols, methods, and devices for air disinfection. Ultraviolet (UV)-based air disinfection demonstrated promising results in inactivating viral bioaerosols. However, the reported data diversity on the required UVC doses has hindered determining the best UVC practices and led to confusion among the public and regulators. This article reviews available information on critical parameters influencing the efficacy of a UVC air disinfection system and, consequently, the required dose including the system's components as well as operational and environmental factors. There is a consensus in the literature that the interrelation of humidity and air temperature has a significant impact on the UVC susceptibility, which translate to changing the UVC efficacy of commercialized devices in indoor settings under varying conditions. Sampling and aerosolization techniques reported to have major influence on the result interpretation and it is recommended to use several sampling methods simultaneously to generate comparable and conclusive data. We also considered the safety concerns and the potential safe alternative of UVC, far-UVC. Finally, the gaps in each critical parameter and the future research needs of the field are represented. This paper is the first step to consolidating literature towards developing a standard validation protocol for UVC air disinfection devices which is determined as the one of the research needs.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 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".