Human Coronaviruses Do Not Transfer Efficiently between Surfaces in the Absence of Organic Materials
Bibliographic record
Abstract
Human coronaviruses, including SARS-CoV-2, are known to spread mainly via close contact and respiratory droplets. However, other potential means of transmission may be present. Fomite-mediated transmission occurs when viruses are deposited onto a surface and then transfer to a subsequent individual. Surfaces can become contaminated directly from respiratory droplets or from a contaminated hand. Due to mask mandates in many countries around the world, the former is less likely. Hands can become contaminated if respiratory droplets are deposited on them (i.e., coughing or sneezing) or through contact with fecal material where human coronaviruses (HCoVs) can be shed. The focus of this paper is on whether human coronaviruses can transfer efficiently from contaminated hands to food or food contact surfaces. The surfaces chosen were: stainless steel, plastic, cucumber and apple. Transfer was first tested with cellular maintenance media and three viruses: two human coronaviruses, 229E and OC43, and murine norovirus-1, as a surrogate for human norovirus. There was no transfer for either of the human coronaviruses to any of the surfaces. Murine norovirus-1 did transfer to stainless steel, cucumber and apple, with transfer efficiencies of 9.19%, 5.95% and 0.329%, respectively. Human coronavirus OC43 transfer was then tested in the presence of fecal material, and transfer was observed for stainless steel (0.52%), cucumber (19.82%) and apple (15.51%) but not plastic. This study indicates that human coronaviruses do not transfer effectively from contaminated hands to contact surfaces without the presence of fecal material.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".