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

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：美国每年有多达 4500 万人受到健康饮酒的直接影响 水问题。这导致至少 1600 万例急性胃肠炎病例与污染直接相关。 社区供水系统，数以千万计的人直接受到化学和有机污染物的影响。 在缺水地区、服务不足的人群中，影响进一步加剧 其他已经遭受健康差距的弱势群体。其中许多水问题是 管理疏忽、监控不一致以及缺乏预测能力的直接结果 接下来可能会出现问题。虽然造成饮用水问题的原因很复杂，但如果我们能够预见到 如果将来出现基于健康的饮用水问题，可能会立即得到解决 每年对数千万美国人产生积极影响。有趣的是，有关水的大量数据 市政供水系统的质量和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视，并且在使用时通常仅作为轶事和追溯使用。 初步证据表明，这些现有数据库包含行政管理的历史。 可以挖掘违规和亚阈值水质结果，以准确预测未来的饮用水 危机。卓越的统计研究研发团队是国际公认的水专家团队 具有统计/数据分析/建模/计算、水质监测等方面的跨领域专业知识 生物和化学污染物，以及清晰、令人信服地转化水质和 为个人、组织和社区提供健康信息以采取可行的步骤。在这个一期项目中， 我们将证明可以利用已经收集的数据来预测与水相关的、基于健康的问题领域， 水质和市政供水系统性能的历史数据。我们将从协调 两个不同州（密歇根州和爱荷华州）的水质和市政供水系统性能不同。 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，并评估我们的行为 通过将过去 5 年预测的违规行为与实际的基于健康的违规行为进行比较的方法。最后， 我们将根据我们的算法确定至少 10 个未来健康风险最高的城市 - 基于水问题，并将进行系统采样以确认我们基于模型的预测。我们随后将 通过探索我们基于模型的方法，展示如何利用这些预测来实现盈利 预测可以以经济、可用的形式呈现给客户。我们的理念和盈利能力的证明 两个州的模型（第一阶段）将为我们建立广泛的（多州）数据库协调和 改进第二阶段拟议的机器学习/建模工作。与多州协调 数据集、识别特定州/地区的关键数据差距以及经过验证的金融模型，我们的技术 最终将导致每年健康饮用水问题的数量大幅减少。