Quantitative microbial risk assessment for <i>Escherichia Coli</i> O157: H7 via drinking water in the Gaza Strip, Palestine
Bibliographic record
Abstract
Introduction: Microbial contamination of drinking water, particularly by pathogens such as Escherichia coli O157: H7, is a significant public health concern worldwide, especially in regions with limited access to clean water like the Gaza Strip. However, few studies have quantified the disease burden associated with E. coli O157: H7 contamination in such challenging water management contexts. Objective: This study aimed to conduct a comprehensive Quantitative Microbial Risk Assessment to estimate the annual infection risk and disease burden attributed to E. coli O157: H7 in Gaza’s drinking water. Methods: Applying the typical four steps of the Quantitative Microbial Risk Assessment technique—hazard identification, exposure assessment, dose-response analysis, and risk characterization—the study assessed the microbial risk associated with E. coli O157: H7 contamination in Gaza’s drinking water supply. A total of 1317 water samples from various sources across Gaza were collected and analyzed for the presence of E. coli O157: H7. Using Microsoft ExcelTM and @RISKTM software, a Quantitative Microbial Risk Assessment model was constructed to quantify the risk of infection associated with E. coli O157: H7 contamination. Monte Carlo simulation techniques were employed to assess uncertainty surrounding input variables and generate probabilistic estimates of infection risk and disease burden. Results: Analysis of the water samples revealed the presence of E. coli O157: H7 in 6.9% of samples, with mean, standard deviation, and maximum values of 1.97, 9.74, and 112 MPN/100 ml, respectively. The risk model estimated a median infection risk of 3.21 × 10-01 per person per year and a median disease burden of 3.21 × 10-01 Disability-Adjusted Life Years per person per year, significantly exceeding acceptable thresholds set by the WHO. Conclusion: These findings emphasize the urgent need for proactive strategies to mitigate public health risks associated with waterborne pathogens in Gaza.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".