Efficiency of coronavirus inactivation on environmental surfaces: A comparison study of two available disinfectants
Bibliographic record
Abstract
Background: There are many coronaviruses of significant medical and veterinary concern, all of which are the result of spillover from another species. Disinfection of healthcare and veterinary environments is an important factor in limiting the transmission of coronaviruses. Disinfection agents for coronaviruses use bleach, quaternary compounds, hydrogen peroxide, and sodium hydroxide. Product labels list contact times that range from 10-30 minutes for total inactivation. Decon7 is a combination disinfectant that is currently used in the food and agriculture, medical facilities, and other industries. While Decon7 has been shown to inactivate a variety of pathogens and disrupt biofilms, its effectiveness and rate of coronavirus inactivation has not been evaluated.Objective: This project sought to evaluate Decon7’s effectiveness and rate of coronavirus inactivation.Methods: This study evaluated the disinfection efficacy of Decon7 (diluted at 1:4) and bleach (diluted at 1:10) after 3 coronaviruses (SARS-CoV-2, HCoV OC43, and HCoV NL63) were inoculated onto up to sixteen environmental surface materials.Results: A 1:4 dilution of Decon7 inactivated all coronaviruses on all surfaces with 1 minute contact time. A 1:10 dilution of bleach was not effective in inactivating coronaviruses with a contact time of 1 minute on all surfaces.Conclusions: New technologies and chemistries may offer more efficient inactivation of pathogens on environmental surfaces. These disinfection methods and materials, which require less than 10 minutes contact time, may improve the efficacy of cleaning and disinfecting surfaces in the built environment.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".