Development of a Quantitative Microbial Risk Assessment for Private Drinking Water Wells in Rural and Remote Ontario
Bibliographic record
Abstract
The province of Ontario comprises the largest groundwater reliant population in Canada serving approximately 1.6 million individuals. Private well users are responsible for ensuring the potability of their own private drinking water source through protective actions (i.e., water treatment, well maintenance, and regular water quality testing). Infections associated with private drinking water wells are rarely documented given their sporadic nature, thus the human health effects remain relatively unknown, representing a significant gap in water safety management and public health action. In the absence of regulation and limited surveillance, quantitative microbial risk assessment (QMRA) represents the most feasible and robust approach to estimating the human health burden attributable to private wells. The current thesis sought to quantify the risk of waterborne acute gastrointestinal illness (AGI) attributed to Giardia, shiga-toxin producing E. coli and norovirus from private drinking water wells in Ontario using simulation-based hydrogeologically- and socio-cognitively- (i.e., Clusters) delineated QMRA. The effects of various geographical and societal factors on E. coli detection rates informed delineation of QMRA models. The current study employed a large groundwater quality dataset (>700,000 samples), permitting spatio-temporally-specific exposure distributions for model inputs, including private well water consumption and annual contamination duration. Developed models were used to explore the effect of varying socio-cognitive scenarios on baseline simulations. Consumption of contaminated private well water in Ontario is estimated to cause 4,648 AGI cases annually, with 3,420 and 1,228 AGI cases predicted to occur among well users located in consolidated and unconsolidated aquifers, respectively. These estimates would increase by 1,596 cases per year in the (unlikely) event of total treatment failure. Further, socio-cognitive-specific QMRA findings suggest significant differences in the level of exposure among Cluster sub-groups of private well users. Private well users within Cluster 3 are characterised by higher levels of exposure and annual illness than Clusters 1 and 2. An efficacious public health intervention would be predicted to decrease AGI cases among well users within Cluster 3 by approximately 91% (5,345 to 536 cases). Developed models provide a transferable tool for public health authorities and governments globally to preserve and protect private well water for current and future well users.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".