Early detection of localized COVID-19 outbreaks has been facilitated through measurements of the RNA of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in wastewater from domestic sanitary sewer systems (a.k.a. wastewater). Through our study (SF-RAD), we will generate, standardize, integrate, compare and make available to the RADx-rad Data Coordination Center (DCC), SARS-CoV-2 human surveillance, and correlated wastewater quantification data under various sampling, processing, detection, and analysis approaches. We hypothesize that sensitivity (enhanced detection and recovery) can be improved by optimizing sampling and concentration strategies and that specificity (viral strain identification for epidemiologic tracking) can be improved by optimizing wastewater detection methods.

Overall Objectives

The three primary University of Miami (UM) campuses in Miami, Florida, are 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, including a major hospital that treats COVID-19 patients. UM has also initiated a pilot wastewater monitoring program to quantify the RNA of SARS-CoV-2 in effluents from building clusters on its three campuses. Weill Cornell Medicine (WCM) has established an international consortium for SARS-CoV-2 environmental surveillance (MetaSUB), including developing a metagenomic map of wastewater from cities around the world. 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 DCC, SF-RAD will develop and implement an 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.

The project will be carried out via the following Specific Aims:

Aim 1: Data Standardization

In collaboration with the RADx-rad DCC, develop and implement data standards and quality metrics, and establish the operational informatics infrastructure to manage SARS-CoV-2 wastewater-based surveillance datasets and metadata.  Data collection and management will be guided by the FAIR principle (findable, accessible, interoperable, reusable) and ethical considerations in surveillance and data management, including efforts to ensure research rigor and reproducibility. We will leverage our experience and existing informatics tools and infrastructure with major NIH research consortia, including our experience as a BD2K LINCS Data Coordination and Integration Center and IDG Resource Dissemination and Outreach Center and our affiliation with NIST (Rapid Diagnostics Testing Consortium and Coronavirus Standards Working Group). Read more . . .

Aim 2: Wastewater Characterization

Optimize wastewater surveillance experimental protocols and parameters for wastewater sampling (grab samples versus 24-hour composite samples, evaluation of geographic scales), sample concentration techniques (ultrafiltration vs. electronegative filtration), viral detection technologies (qRT-PCR, qLAMP, FA, and RNA-seq), and data processing and normalization methods. We will leverage our extensive experience and technical expertise, a coordinated array of core facilities (including shared resources that offer support for behavioral and community-based research, genomics, biorepository, molecular therapeutics, biostatistics, and bioinformatics), and available 3T surveillance data, to perform high-quality reproducible sample collection and quantification to optimize the experimental process. Read more . . .

Aim 3: Integration with Human Health Surveillance

Perform environmental metatranscriptomic analyses, integrate and analyze SARS-CoV-2 wastewater quantification data with COVID-19 prevalence, and develop predictive models to detect local and community level spread of SARS-CoV-2 and other viruses. As wastewater samples subjected to all three detection methodologies will be collected downstream from the UM hospital that treats COVID-19 patients, previously collected and biobanked nasal swab media from patients residing in this hospital will be analyzed using the same methodologies. Similarly, we have access to biobanked human specimens from the University of Miami community that is linked to buildings on campus serviced by sewers used for wastewater sampling. We hypothesize that the SARS-CoV-2 levels in wastewater will reflect the number of cases in the community and hospital, and, that the metatranscriptomics of the populations will be reflected in the sewage samples. Read more . . .

Results

The results from this proposal will develop and deploy experimental and informatics infrastructure and operations and will provide a proof of concept implementation to use wastewater for infectious disease surveillance. This system will contribute to the early detection of localized COVID-19 outbreaks and will be developed as part of the national RADx-rad SARS-CoV-2 wastewater surveillance network. As such, all data will be made FAIR in close collaboration with the DCC, the overall consortium, and NIH, as part of the cooperative agreement of this project.