EAGER: A Wastewater-Based-Epidemiology System for Early Detection of Viral Outbreaks in Detroit, MI

由水传播病原体引起的传染病暴发对健康造成广泛危害，特别是在人口密集的城市地区和弱势群体中。 病毒是这些病原体中最危险的一种，因为它们具有变异能力，在水和废物中具有很高的存活能力。 传统上，疾病检测和管理是通过分析来自寻求医疗的受影响个体的临床样本来完成的。 这种检测可能很慢，在采取有效的公共卫生措施之前就出现了流行病。 这个早期概念探索性研究资助（EAGER）项目的目标是确定病毒和其他生物标志物的废水采样是否能有效预测与水有关的疾病爆发。 该研究将为通过在社区层面对废水进行智能监测来早期发现和预测疾病爆发的变革性新范式提供基础。 这种方法有可能比仅基于临床诊断的方法快得多，后者本质上限于对爆发进行事后分析。 该项目将为工程专业的学生提供机会，以发展研究能力，在解决重要的公共卫生问题的研究目标将通过整合病毒载量和代谢生物标志物的测量，从社区废水样本与公共卫生监测疾病。 来自社区污水处理设施的数据将用于描述污水管道网络内病毒和其他生物标志物的时空分布特征。 来自县公共卫生部门的数据将使用流行病学模型进行分析，以预测疾病的临床病例。 这些预测，连同高维度原始数据沿着，将被用于创建一个非线性学习模型，以研究变量之间的复杂关系，并确定重要的解释变量，以便提高在疾病爆发达到关键阶段之前预测疾病爆发的能力。

Infectious disease outbreaks due to waterborne pathogens present a widespread health hazard, particularly in densely populated urban areas and among vulnerable populations. Viruses are among the most hazardous of these pathogens because of their ability to mutate and their high survivability in water and waste. Traditionally, disease detection and management has been accomplished through analysis of clinical samples from affected individuals who have sought medical treatment. Such detection may be slow, allowing an epidemic to emerge before effective public health measures can be taken. The objective of this EArly-concept Grant for Exploratory Research (EAGER) project is to determine whether wastewater sampling for viral and other biomarkers provides effective prediction for water-related disease outbreaks. The research will provide the basis for a transformative new paradigm for early detection and prediction of disease outbreaks through smart monitoring of wastewater at a community level. This approach has the potential to be much faster than an approach based on clinical diagnostics alone, which is inherently limited to an after analysis of an outbreak. The project will provide opportunities for engineering students to develop research capabilities in addressing important public health issuesThe research objective will be accomplished by integrating viral load and metabolic biomarker measurements from community wastewater samples with public health monitoring of disease. Data from a community wastewater treatment facility will be used to characterize the spatiotemporal distribution of viruses and other biomarkers within the sewer pipe network. Data from the county public health departments will be analyzed using epidemiological modeling to predict clinical cases of disease. These predictions, along with the high-dimensional raw data, will be used to create a nonlinear learning model to investigate the complex relationships among variables and to identify significant explanatory variables in order to improve the ability to predict disease outbreaks before they reach a critical stage.