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

项目总结 迈阿密大学(UM)在佛罗里达州的迈阿密有三个主要校区，地理上分布在 目前最糟糕的新冠肺炎温床之一。UM已经部署了一个精心设计的人类监视测试，跟踪 以及跟踪(3T)系统，以监控学生身体、教职员工。这个3T系统包括一家大医院 这是UM的一部分，用于治疗新冠肺炎患者。为了加强这一新冠肺炎监控系统，UM 部署了一项试点废水监测计划，以从以下建筑物群中检测SARS-CoV-2 校园里。威尔·康奈尔医学公司(WCM)位于纽约州纽约市，直到最近，该地区还拥有 最严重的新冠肺炎爆发。WCM成立了SARS-CoV-2国际联盟 环境监测，包括在纽约市和全球与MetaSUB财团合作，该财团正在创建 世界污水的超基因组和超转录图谱。基于UM和WCM的这项工作， 该提案旨在通过以下方式开发、实施和展示有效的、可预测的废水监控 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)将开发和实施数据标准和 信息学基础架构并执行集成分析，以使所有数据、结果和模型可供 社区，从而为国家SARS-COV-2 RADX-RAD废水检测做出了重要贡献 财团。我们的目标将通过三个目标来实现。目标1：数据标准化，重点是 制定和实施数据标准和质量标准，并建立业务基础设施 管理SARS-CoV-2废水监测数据集和元数据。目标2：废水 定性，重点是优化废水监测协议和废水参数 采样、样本浓缩和病毒检测技术。目标3：与人类健康相结合 监测，重点是后转录分析和废水量化数据的整合 随着社区和医院新冠肺炎的流行，开发预测模型来检测当地和社区 新冠肺炎的水平传播。所有数据都将在中实现可查找、可访问、可互操作和可重复使用(公平) 与DCC密切合作，并将在收集和管理过程中注意道德问题 监督和数据管理，包括努力确保研究的严密性和可重复性。结果是 根据该计划，将开发和部署实验和信息学基础设施和运营，作为 国家RADX-rad SARS-CoV-2废水监测网络，并将提供概念验证 实施污水传染病监测早发现本地化新冠肺炎 疫情爆发。

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.