Efficacy of microbial sampling recommendations and practices in sub-Saharan Africa
Bibliographic record
Abstract
Current guidelines for testing drinking water quality recommend that the sampling rate, which is the number of samples tested for fecal indicator bacteria (FIB) per year, increases as the population served by the drinking water system increases. However, in low-resource settings, prevalence of contamination tends to be higher, potentially requiring higher sampling rates and different statistical methods not addressed by current sampling recommendations. We analyzed 27,930 tests for FIB collected from 351 piped water systems in eight countries in sub-Saharan Africa to assess current sampling rates, observed contamination prevalences, and the ability of monitoring agencies to complete two common objectives of sampling programs: determine regulatory compliance and detect a change over time. Although FIB were never detected in samples from 75% of piped water systems, only 14% were sampled often enough to conclude with 90% confidence that the true contamination prevalence met an example guideline (≤5% chance of any sample positive for FIB). Similarly, after observing a ten percentage point increase in contaminated samples, 43% of PWS would still require more than a year before their monitoring agency could be confident that contamination had actually increased. We conclude that current sampling practices in these settings may provide insufficient information because they collect too few samples. We also conclude that current guidelines could be improved by specifying how to increase sampling after contamination has been detected. Our results suggest that future recommendations should explicitly consider the regulatory limit and desired confidence in results, and adapt when FIB is detected.
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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 | 0.088 | 0.218 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".