Efficacy of Disinfectants for Monkeypox Virus Inactivation on High Touch Surface Materials in Low-Resource Settings
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Disinfection efficacy tests were conducted on surface carriers inoculated with the monkeypox virus (MPXV) by applying six disinfectant solutions (and three controls) on six surfaces common in low-resource settings: four nonporous surfaces (stainless steel, glass, plastic, and latex) and two porous surfaces (ceramic and wood). Disinfectants were wiped on carriers in triplicate, with a 1 min contact time: 0.05 and 0.5% sodium hypochlorite, 70% ethanol, two quaternary ammonium compound (QAC)-based disinfectants, and 1.4% hydrogen peroxide. MPXV was then quantified, and log 10 removal values were calculated. Sodium hypochlorite (0.05 and 0.5%) and ethanol (70%) removed MPXV to below detection level, ≥ 99.97% reduction for nonporous surfaces, and ≥99.40% for wood, QAC-based disinfectants were efficacious on nonporous surfaces (≥99.97% inactivation) but had diminished efficacy on wood, a porous surface, and 1.4% H 2 O 2 had limited efficacy across all tested surfaces. Results varied by disinfectant type and surface type. Based on our results, we recommend using 0.05% sodium hypochlorite or 70% ethanol with 1 min contact time to inactive MPXV on clean nonporous and porous surfaces. As MPXV is evolving, future research with additional disinfectants, application methods, and environmental conditions and research to understand adsorption, disinfection efficacy, and transmission risk on porous surfaces are needed to develop practical disinfection recommendations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".