Decontamination of common healthcare facility surfaces contaminated with SARS-CoV-2 using peracetic acid dry fogging
Bibliographic record
Abstract
Background The SARS-CoV-2 pandemic has highlighted the urgent need for safe and effective surface decontamination methods, particularly in healthcare settings. Aim To evaluate the effectiveness of peracetic acid (PAA) dry fogging in decontaminating healthcare facility surfaces experimentally contaminated with SARS-CoV-2. Methods Nine materials (stainless steel, latex painted wood, unsealed hardwood, melamine countertop, vinyl flooring, clear plastic, faux leather, computer keyboard button, and smartphone touch screen) were surface contaminated with >10 6 median tissue culture infectious dose (TCID 50 ) of SARS-CoV-2, and allowed to dry before exposing to PAA dry fogging. Findings When fumigated with PAA dry fog for 1 h, no infectious SARS-CoV-2 virus was recovered from any of the experimentally inoculated surface types. By contrast, high titres of infectious virus were recovered from corresponding untreated drying controls of the same materials. Conclusion Standard surface decontamination processes, including sprays and wipes, are laborious and frequently cannot completely decontaminate sensitive electronic equipment. The ease of use, low cost, and overall effectiveness of a PAA dry fogging suggest that it should be considered for decontaminating healthcare settings, particularly intensive care units where severely ill SARS-CoV-2 patients are cared for.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".