Development of a Quantitative Microbial Risk Assessment Model for Foodborne E. coli O157:H7 Infection: The Risk of Consuming Lettuce
Bibliographic record
Abstract
The current study used a probabilistic Quantitative Microbial Risk Assessment (QMRA) framework to describe the change of E. coli O157:H7 concentration in lettuce through a foodborne pathway, to develop a predictive model for risk estimation for E. coli O157:H7 infection associated with lettuce. The model consisted of a series of pathogen-associated events including initial contamination, growth during cooling, cold storage and distribution, disinfection (chlorine, gaseous chlorine dioxide and gamma irradiation), and dose response after consumption. A modified Baranyi growth model was proposed which described the initial physiological state of E. coli O157:H7 as a function of the initial temperature. The modified Baranyi growth model was used to predict \nE. coli O157:H7 growth under realistic time-temperature profiles, accounting for the time dynamics of temperature fluctuation. The risk assessment model was constructed in an Excel spreadsheet and Monte Carlo uncertainty analysis was simulated using Crystal Ball. The results in the current study showed that temperature control was the key measure for minimizing the risk of E. coli O157:H7 infection associated with lettuce. Disinfecting contaminated lettuce using the hypothetical methods examined in the study had limited effectiveness in risk reduction. Temperature abuse occurring before or after the hypothetical disinfections significantly diminished the disinfection effect and contributed to increased risk. Of all simulated scenarios, the lowest risk was associated with adequate temperature control and irradiation (44 infections per 1000 consumptions [95%: 94 infection per 1,000 consumption; 5%: 5 infections per 1,000 consumption]). The model can be used to explore the public health impact of other potential strategies that can be adopted to minimize the risk of E. coli O157:H7, while taking into account the possible amplification of pathogen through the food chain.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".