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

PROJECT SUMMARYThe University of Miami (UM), with three primary campuses in Miami, Florida, is geographically spread withinone of the worst current COVID-19 hotbeds.

UM has deployed an elaborate human surveillance testing, trackingand tracing (3T) system to monitor the student body, faculty, and staff. This 3T system includes a major hospitalthat is part of UM and that treats COVID-19 patients.

To augment this COVID-19 monitoring system, UM hasdeployed a pilot wastewater surveillance program for detecting SARS-CoV-2 from clusters of buildings oncampus.

Weill Cornell Medicine (WCM) is located in New York City, NY, an area that until recently had one ofthe worst outbreaks of COVID-19.

WCM has established an international consortium for SARS-CoV-2environmental surveillance, including in NYC and globally with the MetaSUB Consortium, which is creatingmetagenomic 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 byoptimizing sampling, concentration, and detection strategies.

Working closely with the RADx-rad DataCoordination Center (DCC), this application (SF-RAD) will develop and implement data standards andinformatics infrastructure and perform integrative analyses to make all data, results, and models available to thecommunity, thus providing a critical contribution to the national SARS-COV-2 RADx-rad Wastewater DetectionConsortium.

Our objectives will be addressed through three aims.

Aim 1: Data Standardization, focuses ondeveloping and implementing data standards and quality metrics, and establishing the operational infrastructureto manage SARS-CoV-2 wastewater-based surveillance datasets and metadata.

Aim 2: WastewaterCharacterization, focuses on optimizing wastewater surveillance protocols and parameters for wastewatersampling, sample concentration, and viral detection technologies.

Aim 3: Integration with Human HealthSurveillance, focuses on metatranscriptomic analyses and on the integration of wastewater quantification datawith community and hospital COVID-19 prevalence, to develop predictive models to detect local and communitylevel spread of COVID-19.

All data will be made Findable, Accessible, Interoperable and Reusable (FAIR) inclose collaboration with the DCC, and will be collected and managed with attention to ethical issues insurveillance and data management, including efforts to ensure research rigor and reproducibility.

The resultsfrom this proposal will develop and deploy experimental and informatics infrastructure and operations as part ofthe national RADx-rad SARS-CoV-2 wastewater surveillance network and will provide a proof-of-conceptimplementation to use wastewater for infectious disease surveillance for early detection of localized COVID-19outbreaks.