Development and Proof-of-Concept Implementation of the South Florida Miami RADx-rad SARS-CoV-2 Wastewater-Based Surveillance Infrastructure

项目摘要 迈阿密大学（UM）在佛罗里达的迈阿密有三个主要校区，地理分布在 是目前最严重的COVID-19温床之一。UM已经部署了一个精心设计的人类监视测试，跟踪 和跟踪（3 T）系统，以监测学生，教师和工作人员。这个3 T系统包括一家大型医院， 这是UM的一部分，用于治疗COVID-19患者。为了加强COVID-19监测系统，澳大已 部署了一个试点废水监测计划，以检测建筑群中的SARS-CoV-2， 校园威尔康奈尔医学（WCM）位于纽约州纽约市，该地区直到最近才成为 最严重的COVID-19疫情。WCM成立了SARS-CoV-2国际联盟 环境监测，包括在纽约市和全球范围内与Metaprology财团，这是创造 世界污水的宏基因组和宏转录组地图。基于UM和WCM的这项工作， 本提案旨在通过以下方式开发、实施和演示有效的预测性废水监测： 优化采样、浓缩和检测策略。与RADx-rad数据密切合作 协调中心（DCC），该应用程序（SF-RAD）将制定和实施数据标准， 信息基础设施，并进行综合分析，使所有数据，结果和模型提供给 社区，从而为国家SARS-COV-2 RADx-rad废水检测提供了重要贡献 财团我们将通过三个目标来实现我们的目标。目标1：数据标准化，重点是 制定和实施数据标准和质量指标，并建立运营基础设施 管理SARS-CoV-2废水监测数据集和元数据。目标2：废水 表征，侧重于优化废水监测协议和废水参数 采样、样品浓缩和病毒检测技术。目标3：与人类健康相结合 监测，侧重于元转录分析和废水量化数据的整合 与社区和医院COVID-19的患病率，开发预测模型，以检测当地和社区 COVID-19的水平传播。所有数据将在以下环境中实现可查找、可解释、可互操作和可重用（FAIR）： 与DCC密切合作，并将在收集和管理时注意道德问题， 监督和数据管理，包括努力确保研究的严谨性和可重复性。结果 从这个建议将开发和部署实验和信息基础设施和业务的一部分， 国家RADx-rad SARS-CoV-2废水监测网络，并将提供概念验证 实施使用废水进行传染病监测，以早期检测局部COVID-19 爆发

PROJECT SUMMARY The University of Miami (UM), with three primary campuses in Miami, Florida, is geographically spread within one of the worst current COVID-19 hotbeds. UM has deployed an elaborate human surveillance testing, tracking and tracing (3T) system to monitor the student body, faculty, and staff. This 3T system includes a major hospital that is part of UM and that treats COVID-19 patients. To augment this COVID-19 monitoring system, UM has deployed a pilot wastewater surveillance program for detecting SARS-CoV-2 from clusters of buildings on campus. Weill Cornell Medicine (WCM) is located in New York City, NY, an area that until recently had one of the worst outbreaks of COVID-19. WCM has established an international consortium for SARS-CoV-2 environmental surveillance, including in NYC and globally with the MetaSUB Consortium, which is creating metagenomic and metatranscriptomic maps of the world’s sewage. Based on this work at both UM and WCM, this proposal aims to develop, implement, and demonstrate effective and predictive wastewater surveillance by optimizing sampling, concentration, and detection strategies. Working closely with the RADx-rad Data Coordination Center (DCC), this application (SF-RAD) will develop and implement data standards and informatics infrastructure and perform integrative analyses to make all data, results, and models available to the community, thus providing a critical contribution to the national SARS-COV-2 RADx-rad Wastewater Detection Consortium. Our objectives will be addressed through three aims. Aim 1: Data Standardization, focuses on developing and implementing data standards and quality metrics, and establishing the operational infrastructure to manage SARS-CoV-2 wastewater-based surveillance datasets and metadata. Aim 2: Wastewater Characterization, focuses on optimizing wastewater surveillance protocols and parameters for wastewater sampling, sample concentration, and viral detection technologies. Aim 3: Integration with Human Health Surveillance, focuses on metatranscriptomic analyses and on the integration of wastewater quantification data with community and hospital COVID-19 prevalence, to develop predictive models to detect local and community level spread of COVID-19. All data will be made Findable, Accessible, Interoperable and Reusable (FAIR) in close collaboration with the DCC, and will be collected and managed with attention to ethical issues in surveillance and data management, including efforts to ensure research rigor and reproducibility. The results from this proposal will develop and deploy experimental and informatics infrastructure and operations as part of the national RADx-rad SARS-CoV-2 wastewater surveillance network and will provide a proof-of-concept implementation to use wastewater for infectious disease surveillance for early detection of localized COVID-19 outbreaks.