SARS-CoV-2 and Risk to Food Safety
Bibliographic record
Abstract
The COVID-19 pandemic has brought about a consideration of our understanding of transmission of the causal agent, SARS-CoV-2 to humans and its potential effect on food safety and food security. The main routes of transmission are reported to be person-to-person, by respiratory droplets and to a lesser degree, by fomites. Concerns have been raised on the possibility of transmission via food and food packaging and whether the virus poses a risk to food safety. The current contribution provides an exposé of updated literature and reports applicable to various components of food safety and its linkage to SARS-CoV-2. The article focuses on SARS-CoV-2 survival in food, on food contact materials and food packaging, and its categorization as a foodborne vs. respiratory virus, the possibility of fecal-oral transmission and the likelihood of infection via the gastro-intestinal system. The survival and inactivation of SARS-CoV-2 in food through thermal and non-thermal inactivation methods as well as the survival and inactivation on inanimate surfaces and effective disinfection of food contact surfaces, are discussed. Ultimately, the article endeavors to add to the body of knowledge pertaining to the role of SARS-CoV-2 in food safety and thereby contribute to an agile and robust fraternity that is equipped to absorb and weather the ongoing effects of the pandemic on the food sector.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".