SARS-CoV-2 transmission modes: Why and how contamination occurs around shared meals and drinks?
Bibliographic record
Abstract
In spite of prevention measures enacted all over the world to control the COVID-19 pandemic outbreak, including mask wearing, social distancing, hand hygiene, vaccination, and other precautions, the SARS-CoV-2 virus continues to spread globally at an unabated rate of about 1 million cases per day. The specificities of superspreading events as well as evidence of human-to-human, human-to-animal and animal-to-human transmission, indoors or outdoors, raise questions about a possibly neglected viral transmission route. In addition to inhaled aerosols, which are already recognized as key contributors to transmission, the oral route represents a strong candidate, in particular when meals and drinks are shared. In this review, we intend to discuss that significant quantities of virus dispersed by large droplets during discussions at festive gatherings could explain group contamination either directly or indirectly after deposition on surfaces, food, drinks, cutlery, and several other soiled vectors. We suggest that hand hygiene and sanitary practices around objects brought to the mouth and food also need to be taken into account in order to curb transmission.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".