A Risk Assessment Model to Evaluate the Role of Fecal Contamination in Recreational Water on the Incidence of Cryptosporidiosis at the Community Level in Ontario
Bibliographic record
Abstract
A quantitative microbial risk assessment model was developed to simulate the role of recreational water contact in the transmission of cryptosporidiosis in a model Ontario community. Stochastic simulations were based on plausible modes of contamination of a pool (literature derived), river (site-specific), and recreational lakes (literature derived). The highest estimated risks of infection were derived from the (highly contaminated) recreational lake scenario, considered the upper end for risk of infection for both children (10 infections per 1,000 swims [5 per thousand: two infections per 1,000 swims; 95 per thousand: three infections per 100 swims]) and adults (four infections per 1,000 swims [5 per thousand: four infections per 1,000 swims; 95 per thousand: one infection per 100 swims]). Simulating the likely Cryptosporidium oocyst concentration in a lane pool that a child would be exposed to following a diarrheal fecal release event resulted in the third highest mean risk of infection (four infections per 10,000 swims [5 per thousand: three infections per 100,000; 95 per thousand: 10 infections per 10,000 swims]). The findings from this study illustrate the need for systematic and standardized research to quantify Cryptosporidium oocyst levels in Canadian public pools and recreational beaches. There is also a need to capture the swimming practices of the Canadian public, including most common forms and frequency measures. The study findings suggest that swimming in natural swim environments and in pools following a recent fecal contamination event pose significant public health risks. When considering these risks relative to other modes of cryptosporidiosis transmission, they are significant.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".