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Use of Bayesian Modeling to Assess Occurrence of Viral Pathogens in Multiple US Drinking Water Systems

Metadata Updated: November 12, 2020

Drinking water treatment plants rely on purification of contaminated source waters to provide communities with potable water. One group of possible contaminants are enteric viruses. Measurement of viral quantities in environmental water systems are often performed using polymerase chain reaction (PCR) or quantitative PCR (qPCR). However, true values may be underestimated due to challenges involved in a multi-step viral concentration process and due to PCR inhibition. In this study, water samples were concentrated from 25 drinking water treatment plants (DWTPs) across the US to study the occurrence of enteric viruses in source water and removal after treatment. The five different types of viruses studied were adenovirus, norovirus GI, norovirus GII, enterovirus, and polyomavirus. Quantitative PCR was performed on all samples to determine presence or absence of these viruses in each sample. Ten DWTPs showed presence of one or more viruses in source water, with four DWTPs having treated drinking water testing positive. Furthermore, PCR inhibition was assessed for each sample using an exogenous amplification control, which indicated that all of the DWTP samples, including source and treated water samples, had some level of inhibition, confirming that inhibition plays an important role in PCR-based assessments of environmental samples. PCR inhibition measurements, viral recovery, and other assessments were incorporated into a Bayesian model to more accurately determine viral load in both source and treated water. Results of the Bayesian model indicated that viruses are present in source water and treated water. By using a Bayesian framework that incorporates inhibition, as well as many other parameters that affect viral detection, this study offers an approach for more accurately estimating the occurrence of viral pathogens in environmental waters.

This dataset is associated with the following publication: Varughese, E., N. Brinkman, E. Anneken, J. Cashdollar, S. Fout, E. Furlong, D. Kolpin, S. Glassmeyer, and S. Keely. Estimating virus occurrence using Bayesian modeling in multiple drinking water systems of the United States. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 619620: 1330-1339, (2018).

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Public: This dataset is intended for public access and use. License: See this page for license information.

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Metadata Created Date November 12, 2020
Metadata Updated Date November 12, 2020

Metadata Source

Harvested from EPA ScienceHub

Additional Metadata

Resource Type Dataset
Metadata Created Date November 12, 2020
Metadata Updated Date November 12, 2020
Publisher U.S. EPA Office of Research and Development (ORD)
Data Last Modified 2017-10-26
Public Access Level public
Bureau Code 020:00
Schema Version
Harvest Object Id 0c3b27ab-8788-4754-b6ab-341881b96128
Harvest Source Id 04b59eaf-ae53-4066-93db-80f2ed0df446
Harvest Source Title EPA ScienceHub
Program Code 020:000
Publisher Hierarchy U.S. Government > U.S. Environmental Protection Agency > U.S. EPA Office of Research and Development (ORD)
Related Documents
Source Datajson Identifier True
Source Hash ea1841bf606b464811d53ae83a46397446450d97
Source Schema Version 1.1

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