Ali Syed Imran, De Santi Michael, Arnold Matt, Khan Usman T, Penney Tarra L, Ali Syed Saad, Fesselet Jean-François, Orbinski James
Dahdaleh Institute for Global Health Research, York University, Toronto, Ontario, Canada
Lassonde School of Engineering, York University, Toronto, Ontario, Canada.
BMJ Glob Health. 2025 Aug 20;10(8):e018631. doi: 10.1136/bmjgh-2024-018631.
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.
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.
The SWOT generated a context-specific FRC target of 0.85-1.05 mg/L for 15-hours protection. Validation of the SWOT's process-based model showed R 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.
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.
水源性疾病是紧急情况下的主要关注点。人道主义准则规定了普遍的水氯化目标,但由于分发后氯的衰减会使水容易受到病原体的再次污染,这些目标无法可靠地保护水。安全水优化工具(SWOT)对氯衰减进行建模,以生成特定环境下的氯化目标,确保水在到达消费点之前一直受到保护。SWOT尚未在实际的人道主义应对中进行测试,因此我们在科克斯巴扎尔难民营进行了概念验证评估,以验证其建模并评估其有效性和成效。
我们使用2019年7月至9月收集的数据对SWOT进行训练,并使用2019年10月至12月的数据(n = 2221)进行评估。我们通过比较训练数据集和测试数据集的性能来验证SWOT的建模。我们使用二元逻辑回归评估有效性,比较在取水口实现SWOT目标时家庭自由余氯(FRC)与现状目标,使用中断时间序列分析评估实施SWOT前后有保护性FRC的家庭比例的成效。
SWOT生成了特定环境下15小时保护的FRC目标为0.85 - 1.05毫克/升。对SWOT基于过程的模型的验证表明,训练数据集和测试数据集之间的R从0.50降至0.23,表明需要定期重新训练。SWOT的机器学习模型预测家庭FRC在15小时时<0.2毫克/升的概率为1% - 9%,接近观察到的12%,分别与基线和终线期间观察到的7%风险一致。与现状目标相比,收集符合SWOT目标的水的家庭在15小时后更有可能获得充分保护(90%对35%,p<0.01),证明了SWOT的有效性。SWOT目标在取水口未得到充分实施,因此我们在终线期间未观察到家庭FRC的变化。
SWOT可以生成特定环境下的氯化目标,保护水免受病原体的再次污染。改善监测与处理之间的反馈将有助于系统操作人员充分发挥SWOT的水安全潜力。