Exploring the Differences in the Response of SARS-CoV-2 Delta and Omicron to Ultraviolet Radiation
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide One method that can help slow the spread of coronaviruses is disinfection with UV light. The Delta and Omicron variants of the COVID-19 virus (SARS-CoV-2) have come to dominate the later stages of the pandemic due to their higher rates of transmission. In this work, it is shown that a 17% higher UV 254 dose is required for the disinfection of Delta and Omicron variants when compared to the ancestral strain of SARS-CoV-2. The UV 254 disinfection rate constants for SARS-CoV-2 and the Delta and Omicron variants were found to be 1.4 ± 0.3, 1.1 ± 0.2, and 1.1 ± 0.2 cm 2 /mJ, respectively. The rate constants of Delta and Omicron were statistically different from the ancestral strain of SARS-CoV-2 at the 95% confidence level based on at least three replicate experiments. It is suggested that the reason for this difference is the absence of repeating uracil (U) bases in the genome of the two variants. The UV 254 sensitivity of repeating pyrimidine bases is well-established. There are 2.6 and 3.7% fewer uracil triplets (UUU) in the Delta and Omicron variants, respectively, when compared to SARS-CoV-2. This difference in UV 254 sensitivity is relevant to a range of UV disinfection applications including upper-room disinfection, air handling equipment, aircraft sanitization, and others.
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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".