dbGaP Study Accession: phs002527

NIH Institute/Center: NLM

RADx Data Program: RADx-rad

DOI: 10.60773/6e21-bq61

Release Date: 11/07/2022

Study Description: COVID-19 is expected to become one of the largest mass casualty events in the history of the United States (U.S.). Assessment of the true burden of disease in the population is needed for the prevention and mitigation of this and future viral disease outbreaks. Testing of new cases (via swabs/saliva) and those previously exposed (via serum) has limited reach in the population. However, an alternative approach relying on the analysis of community wastewater can screen up to 70% of the U.S. population on a weekly basis at < 0.01% of the cost of clinical screening of individuals. As a population-wide infectious disease surveillance tool, wastewater-based epidemiology (WBE) can be used to complement current surveillance methods to better understand disease burden and how these burdens differ across communities. The goal of this RADx-rad supplement was to implement and evaluate a near real-time WBE framework for SARS-CoV-2 by (i) assessing in near real-time community spread of the new coronavirus, (ii) significantly increasing the fraction of the U.S. population screened, the frequency at which this testing is being completed (weekly) and the geospatial resolution of screening (from city-wide to neighborhood-specific), (iii) comparing novel coronavirus levels in wastewater with the burdens of infection, disease, and mortality reported by local health systems, (iv) harvesting high throughput sequence (HTS) data on SARS-CoV-2 variants across the U.S., (v) optimizing pipelines for HTS analysis, and (vi) immediately sharing any new knowledge gained with the RADx-rad Data Coordinating Center (DCC), research community, and the general public via an expansion of the online dashboard that was pioneered by the team in collaboration with the City of Tempe, AZ. Previously developed, peer-reviewed strategies were leveraged for population-wide virus monitoring via reverse transcription real-time polymerase chain reaction (RT-qPCR), HTS, sequence analysis, and data communication originally developed for the parent award to quickly provide a data stream and scientific resource for managing the COVID-19 epidemic in the U.S. In Aim 1, a wastewater-based epidemiology (WBE) bioinformatics framework for SARS-CoV-2 was developed at the national, city, and intra-sewershed or neighborhood-level to produce RT-qPCR and SARS-CoV-2 RNA-seq data for studying the distribution of viral levels and genetic polymorphisms in the community. In Aim 2, a WBE bioinformatics framework for translating SARS-CoV-2 data from RT-qPCR and high-throughput sequencing into information for monitoring population health was be evaluated. Successful completion of this biomedical informatics project provided the U.S. with an early warning system for SARS-CoV-2 detection and a tracking aid for public health epidemiologists seeking to reduce morbidity and mortality from infectious diseases like COVID-19 in the U.S.

Updated Date: 06/17/2023

Principal Investigator: Scotch, Matthew

Has Data Files: Yes

Study Domain: Wastewater Surveillance; Next Generation Sequencing (NGS); Screening Testing

Data Collection Method: Wastewater Sampling

Keywords: Genomics; Bioinformatics; Wastewater-Based Epidemiological Monitoring; Viral Genetics; Epidemiologic and Clinical Phenotypes; Cluster/Outbreaks; COVID-19 Screening

Study Design: Observational

Multi-Center Study: FALSE

Data Types: Other; Immunological; Environmental (Physical)

Data Types, Other: SARS-CoV-2 virus sequences from wastewater samples

Study Start Date: 07/01/2020

Study End Date: 05/31/2023

Species: Non-Human Data

Estimated Cohort Size: 0

Study Population Focus: N/A

Publication URL: https://pubmed.ncbi.nlm.nih.gov/36203558/; https://pubmed.ncbi.nlm.nih.gov/36287087/; https://pubmed.ncbi.nlm.nih.gov/36493788/; https://pubmed.ncbi.nlm.nih.gov/37098909/; https://pubmed.ncbi.nlm.nih.gov/36043869/; https://pubmed.ncbi.nlm.nih.gov/35972248/; https://pubmed.ncbi.nlm.nih.gov/35714764/; https://pubmed.ncbi.nlm.nih.gov/35475464/; https://pubmed.ncbi.nlm.nih.gov/34607084/; https://pubmed.ncbi.nlm.nih.gov/34578384/; https://pubmed.ncbi.nlm.nih.gov/34388979/; https://pubmed.ncbi.nlm.nih.gov/33501452/; https://pubmed.ncbi.nlm.nih.gov/33430521/; https://pubmed.ncbi.nlm.nih.gov/37098909/; https://pubmed.ncbi.nlm.nih.gov/36493788/; https://pubmed.ncbi.nlm.nih.gov/36287087/; https://pubmed.ncbi.nlm.nih.gov/36203558/; https://pubmed.ncbi.nlm.nih.gov/36168937/; https://pubmed.ncbi.nlm.nih.gov/36043869/; https://pubmed.ncbi.nlm.nih.gov/35972248/; https://pubmed.ncbi.nlm.nih.gov/35714764/; https://pubmed.ncbi.nlm.nih.gov/36168937/; https://pubmed.ncbi.nlm.nih.gov/35475464/

Acknowledgement Statement: This study was supported through funding, 3U01LM013129-04S1, for the National Library of Medicine (NLM) as part of the RADx-rad program. The work was also partially supported by the Intramural Research Program of the National Library of Medicine, National Institutes of Health. Approved users should acknowledge the provision of data access by dbGaP for accession phs002527.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: PA-18-484

NIH Grant or Contract Number(s): 3U01LM013129-04S1

Consent/Data Use Limitations: General Research Use