Proof-of-concept evaluation at Cox’s Bazar of the Safe Water Optimization Tool: water quality modelling for safe water supply in humanitarian emergencies
Bibliographic record
Abstract
INTRODUCTION: Waterborne diseases are leading concerns in emergencies. Humanitarian guidelines stipulate universal water chlorination targets, but these fail to reliably protect water as postdistribution chlorine decay can leave water vulnerable to pathogenic recontamination. The Safe Water Optimization Tool (SWOT) models chlorine decay to generate context-specific chlorination targets that ensure water remains protected up to point-of-consumption. The SWOT has not been tested in an active humanitarian response, so we conducted a proof-of-concept evaluation at a Cox's Bazar refugee settlement to validate its modelling and assess its efficacy and effectiveness. METHODS: We trained the SWOT using data collected from July to September 2019 and evaluated using data from October to December 2019 (n=2221). We validated the SWOT's modelling by comparing performance using training and testing data sets. We assessed efficacy using binary logistic regression comparing household free residual chlorine (FRC) when the SWOT target was delivered at tapstands versus the status quo target, and effectiveness using interrupted time series analysis of the proportion of households with protective FRC before and after SWOT implementation. RESULTS: decreased from 0.50 to 0.23 between training and testing data sets, indicating periodic retraining is required. The SWOT's machine-learning model predicted a 1%-9% probability of household FRC<0.2 mg/L at 15 hours, close to the observed 12% and in line with the observed 7% risk during baseline and endline, respectively. Households that collected water meeting the SWOT target were more likely to have sufficient protection after 15 hours compared with the status quo target (90% vs 35%, p<0.01), demonstrating the SWOT's efficacy. The SWOT target was not fully implemented at tapstands, so we did not observe change in household FRC during endline. CONCLUSION: The SWOT can generate context-specific chlorination targets that protect water against pathogenic recontamination. Improving feedback between monitoring and treatment would help system operators unlock the SWOT's full water safety potential.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".