dbGaP Study Accession: phs002525

NIH Institute/Center: NIDA

RADx Data Program: RADx-rad

DOI: 10.60773/tca1-nb16

Release Date: 01/26/2022

Study Description: The University of Miami (UM), with three primary campuses in Miami, Florida, was geographically spread within one of the worst COVID-19 hotbeds at the time of study. UM deployed an elaborate human surveillance testing, tracking, and tracing (3T) system to monitor the student body, faculty, and staff. The 3T system included a major hospital that is part of UM and that treats COVID-19 patients. To augment the COVID-19 monitoring system, UM 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 (at the time of study) had one of the worst outbreaks of COVID-19. WCM established an international consortium for SARS-CoV-2 environmental surveillance, including in NYC and globally with the MetaSUB Consortium, which created metagenomic and metatranscriptomic maps of the world's sewage. Based on this work at both UM and WCM, the aim of the project was 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), work under application SF-RAD included the development and implementation of data standards and informatics infrastructure and performing integrative analyses that made 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 objectives were addressed through three aims. Aim 1: Data Standardization, focused 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, focused on optimizing wastewater surveillance protocols and parameters for wastewater sampling, sample concentration, and viral detection technologies. Aim 3: Integration with Human Health Surveillance, focused 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. The results from this project developed and deployed experimental and informatics infrastructure and operations as part of the national RADx-rad SARS-CoV-2 wastewater surveillance network and provided a proof-of-concept implementation to use wastewater for infectious disease surveillance for early detection of localized COVID-19 outbreaks.

Updated Date: 10/30/2023

Principal Investigator: Solo-Gabriele, Helena

Has Data Files: Yes

Study Domain: Community Outreach Programs; Wastewater Surveillance

Data Collection Method: Wastewater Sampling

Keywords: Cluster/Outbreaks; Data Standardization; Fecal Coliform; Pepper Mild Mottle Virus (PMMoV); Predictive Modeling; Transcription; Virological Detection Technology; Volcano Second Generation (V2G)-qPCR; Water Quality; β-2 Microglobulin (B2M)

Study Design: Longitudinal Cohort

Multi-Center Study: No

Data Types: Clinical; Electronic Medical Records; Environmental; Genomic; Genotyping; Immunological; Metagenomic; Questionnaire or Survey; Sequencing; Supporting Documents

Study Start Date: 01/01/2021

Study End Date: 12/31/2022

Species: Human; Non-Human

Estimated Cohort Size: 30000

Study Population Focus: Adults

Publication URL: https://pubmed.ncbi.nlm.nih.gov/33795001/; https://pubmed.ncbi.nlm.nih.gov/34039416/; https://pubmed.ncbi.nlm.nih.gov/34043940/; https://pubmed.ncbi.nlm.nih.gov/34375259/; https://pubmed.ncbi.nlm.nih.gov/34504351/; https://pubmed.ncbi.nlm.nih.gov/35132260/; https://pubmed.ncbi.nlm.nih.gov/35136383/; https://pubmed.ncbi.nlm.nih.gov/35136384/; https://pubmed.ncbi.nlm.nih.gov/35233546/; https://pubmed.ncbi.nlm.nih.gov/35313580/; https://pubmed.ncbi.nlm.nih.gov/35864089/; https://pubmed.ncbi.nlm.nih.gov/35999570/; https://pubmed.ncbi.nlm.nih.gov/35999570/; https://pubmed.ncbi.nlm.nih.gov/36202365/; https://pubmed.ncbi.nlm.nih.gov/36202365/; https://pubmed.ncbi.nlm.nih.gov/36292799/; https://pubmed.ncbi.nlm.nih.gov/36292799/; https://pubmed.ncbi.nlm.nih.gov/36398131/; https://pubmed.ncbi.nlm.nih.gov/36398131/; https://pubmed.ncbi.nlm.nih.gov/36623667/; https://pubmed.ncbi.nlm.nih.gov/36623667/; https://pubmed.ncbi.nlm.nih.gov/37160974/; https://pubmed.ncbi.nlm.nih.gov/37160974/`; https://pubmed.ncbi.nlm.nih.gov/37216988/; https://pubmed.ncbi.nlm.nih.gov/37216988/; https://pubmed.ncbi.nlm.nih.gov/37406055/; https://pubmed.ncbi.nlm.nih.gov/37502918/; https://pubmed.ncbi.nlm.nih.gov/37502918/; https://pubmed.ncbi.nlm.nih.gov/37601294/; https://pubmed.ncbi.nlm.nih.gov/37601294/; https://pubmed.ncbi.nlm.nih.gov/37622131/

Acknowledgement Statement: This study was supported through funding, 4U01DA053941-02, for the National Institute on Drug Abuse (NIDA) as part of the RADx-rad program. Approved users should acknowledge the provision of data access by dbGaP for accession phs002525.v1.p1, and the NIH RADx Data Hub. Approved users should also acknowledge the specific version(s) of the dataset(s) obtained from the NIH RADx Data Hub.

Funding Opportunity Announcement (FOA) Number: RFA-OD-20-015

NIH Grant or Contract Number(s): 4U01DA053941-02

Consent/Data Use Limitations: General Research Use