Risk of adenovirus and <i>Cryptosporidium</i> ingestion to sanitation workers in a municipal scale non-sewered sanitation process: a case study from Kigali, Rwanda
Bibliographic record
Abstract
Abstract Sanitation workers provide essential services that protect public health, often at the cost of their own health and safety. In this study, we evaluate occupational exposure to fecal pathogens at each stage in a non-sewered sanitation process. Bulk fecal waste samples were collected during waste collection and waste processing tasks and analyzed for Cryptosporidium, adenovirus, E. coli, and total coliforms using quantitative polymerase chain reaction and culture methods. Structured observations of worker hand-to-mouth behavior were conducted, and worker hand- and glove-rinse samples were collected and analyzed for E. coli and total coliforms. A Monte Carlo simulation was used to model the dose of pathogen ingested and the risk of disease across two waste collection and processing tasks. The model results show that the probability of disease was highest from exposure to adenovirus during collection. Our analysis highlights that pathogen-to-indicator ratios are useful for predicting the risk to adenovirus which has a high detection rate. On the other hand, the use of pathogen-to-indicator ratios to predict Cryptosporidium concentration is fraught due to variable detection rates and concentration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".