Common and Potential Emerging Foodborne Viruses: A Comprehensive Review
Bibliographic record
Abstract
Human viruses and viruses from animals can cause illnesses in humans after the consumption of contaminated food or water. Contamination may occur during preparation by infected food handlers, during food production because of unsuitably controlled working conditions, or following the consumption of animal-based foods contaminated by a zoonotic virus. This review discussed the recent information available on the general and clinical characteristics of viruses, viral foodborne outbreaks and control strategies to prevent the viral contamination of food products and water. Viruses are responsible for the greatest number of illnesses from outbreaks caused by food, and risk assessment experts regard them as a high food safety priority. This concern is well founded, since a significant increase in viral foodborne outbreaks has occurred over the past 20 years. Norovirus, hepatitis A and E viruses, rotavirus, astrovirus, adenovirus, and sapovirus are the major common viruses associated with water or foodborne illness outbreaks. It is also suspected that many human viruses including Aichi virus, Nipah virus, tick-borne encephalitis virus, H5N1 avian influenza viruses, and coronaviruses (SARS-CoV-1, SARS-CoV-2 and MERS-CoV) also have the potential to be transmitted via food products. It is evident that the adoption of strict hygienic food processing measures from farm to table is required to prevent viruses from contaminating our food.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".