Large-scale data integration and harmonization to accurately predict sites facing future health-based drinking water crises

大规模数据整合和协调，以准确预测未来面临健康饮用水危机的地点

• 批准号: 10253600
• 负责人: Nathan L Tintle
• 金额: $ 25.66万
• 依托单位: SUPERIOR STATISTICAL RESEARCH, LLC
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10253600
• 关键词: Acute; Address; Algorithms; American; Area; Biological Monitoring; Chemicals; Cities; Coal; Communities; Community Surveys; Complex; Cost Savings; Data; Data Analyses; Data Set; Databases; Ensure; Exposure to; Filtration; Focus Groups; Future; Gastroenteritis; Goals; Government; Health; Human; Individual; International; Iowa; Lead; Lead levels; Link; Location; Machine Learning; Methods; Michigan; Modeling; Monitor; Municipalities; Negligence; Pathway interactions; Performance; Persons; Phase; Pollution; Price; Provider; Public Health; ROC Curve; Recording of previous events; Records; Research; Safety; Sampling; Serinus; Site; Surveys; System; Techniques; Technology; Testing; Translating; Trust; Underserved Population; Vulnerable Populations; Water; advocacy organizations; base; commercialization; data harmonization; data integration; drinking water; economic impact; health disparity; high risk; improved; inner city; innovation; large scale data; member; pollutant; predictive modeling; research and development; rural area; statistics; water quality; water sampling; water testing; willingness to pay; Acute; Address; Algorithms; American; Area; Biological Monitoring; Chemicals; Cities; Coal; Communities; Community Surveys; Complex; Cost Savings; Data; Data Analyses; Data Set; Databases; Ensure; Exposure to; Filtration; Focus Groups; Future; Gastroenteritis; Goals; Government; Health; Human; Individual; International; Iowa; Lead; Lead levels; Link; Location; Machine Learning; Methods; Michigan; Modeling; Monitor; Municipalities; Negligence; Pathway interactions; Performance; Persons; Phase; Pollution; Price; Provider; Public Health; ROC Curve; Recording of previous events; Records; Research; Safety; Sampling; Serinus; Site; Surveys; System; Techniques; Technology; Testing; Translating; Trust; Underserved Population; Vulnerable Populations; Water; advocacy organizations; base; commercialization; data harmonization; data integration; drinking water; economic impact; health disparity; high risk; improved; inner city; innovation; large scale data; member; pollutant; predictive modeling; research and development; rural area; statistics; water quality; water sampling; water testing; willingness to pay

Project summary: Up to 45 million people per year in the U.S. are directly impacted by health-based drinking water problems. This leads to at least 16 million cases of acute gastroenteritis directly linked to pollution at community water systems, with tens of millions more directly impacted by chemical and organic pollutants. Impacts are further exacerbated in locations dealing with water scarcity, in under-served populations, and within other vulnerable populations already suffering from health disparities. Many of these water problems are the direct result of managerial negligence, inconsistent monitoring, and a lack of the ability to anticipate where problems may arise next. While the reasons for drinking water problems are complex, if we could anticipate where health-based drinking water problems were to occur in the future, it could have an immediate and positive impact on tens of millions of Americans annually. Interestingly, extensive data about water quality and the performance of municipal water systems already exists in large, disparate databases. These databases are largely ignored and, when used, are typically used only anecdotally and retroactively. Preliminary evidence suggests that these existing databases, which contain histories of administrative violations and sub-threshold water-quality results, can be mined to accurately predict future drinking water crises. The Superior Statistical Research R&D team is an internationally recognized group of water experts with cross-cutting expertise in statistics/data analysis/modelling/computing, water-quality monitoring of biological and chemical contaminants, and the ability to clearly and compellingly translate water-quality and health information to actionable steps for individuals, organizations and communities. In this Phase I project, we will show that it is possible to predict water-related, health-based problem areas utilizing already collected, historical data on water quality and municipal water system performance. We will begin by harmonizing the disparate water quality and municipal water system performance in two different states (Michigan and Iowa). We will then utilize machine-learning techniques to predict health-based violation histories and will evaluate our methods by comparing predicted violations to actual health-based violations in the previous 5 years. Finally, we will identify at least 10 municipalities determined by our algorithm to be at the highest risk for future health- based water problems and will do systematic sampling to confirm our model-based predictions. We will then demonstrate how making these predictions can be leveraged to profitability by exploring how our model-based predictions can be presented to customers in an economical, usable form. Proof of our concept and profitability models in two states (Phase I) will set us up for widespread (multi-state) database harmonization and improvement of the proposed machine-learning/modelling effort in Phase II. With multi-state harmonized datasets, identification of key data gaps in particular states/areas, and proven financial models, our technology will ultimately lead to dramatic reductions in the number of health-based drinking water problems annually.

项目摘要：美国每年最多4500万人受到健康饮酒的影响 水问题。这导致至少1600万例急性胃炎直接与污染有关 社区供水系统，数以千计的直接受到化学和有机污染物的影响。 在处理水短缺，服务不足的人群和 在其他已经遭受健康差异的脆弱人群中。这些水问题中有许多是 管理疏忽，监控不一致以及缺乏预期在哪里的直接结果 接下来可能会出现问题。虽然饮用水问题的原因很复杂，但如果我们能预料到 如果将来发生基于健康的饮用水问题，它可能会立即发生 每年对数千万美国人的积极影响。有趣的是，有关水的大量数据 质量和市政供水系统的性能已经存在于大型，不同的数据库中。这些 数据库在很大程度上被忽略，并且使用时通常仅在轶事和追溯上使用。 初步证据表明，这些现有数据库，其中包含行政历史 可以开采违规和阈值下水质结果，以准确预测未来的饮用水 危机。高级统计研究研发团队是一群国际认可的水专家 凭借统计/数据分析/建模/计算方面的跨记录专业知识，水质监测 生物学和化学污染物，以及清晰而令人难以地翻译水品质和的能力 对个人，组织和社区的可行步骤的健康信息。在这个阶段的项目中， 我们将表明，可以预测利用已经收集的水相关的，基于健康的问题领域， 关于水质和市政水系统性能的历史数据。我们将首先协调 在两个不同州（密歇根州和爱荷华州）的不同水质和市政供水系统的表现。 然后，我们将利用机器学习技术来预测基于健康的违规历史，并将评估我们的 方法通过将预测的违规与过去5年的实际基于健康违规的行为进行比较。最后， 我们将确定至少10个由我们的算法确定的市政当局是未来健康的最高风险 基于水问题，并将进行系统抽样以确认我们的基于模型的预测。然后我们会 通过探索我们的模型如何利用如何利用这些预测来利用盈利能力 可以以经济的可用形式向客户提供预测。我们的概念和盈利能力的证明 在两个状态（第一阶段）的模型将使我们为广泛的（多状态）数据库协调和 在II阶段提出的机器学习/建模工作的改进。与多国家协调 数据集，特定州/地区的关键数据差距的识别以及经过验证的财务模型，我们的技术 最终将导致每年基于健康的饮用水问题的数量大大减少。