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Estimating virus occurrence using Bayesian modeling in multiple drinking water systems of the United States

Overview of attention for article published in Science of the Total Environment, April 2018
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Mentioned by

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2 tweeters
facebook
1 Facebook page

Citations

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1 Dimensions

Readers on

mendeley
23 Mendeley
Title
Estimating virus occurrence using Bayesian modeling in multiple drinking water systems of the United States
Published in
Science of the Total Environment, April 2018
DOI 10.1016/j.scitotenv.2017.10.267
Pubmed ID
Authors

Eunice A. Varughese, Nichole E. Brinkman, Emily M. Anneken, Jennifer L. Cashdollar, G. Shay Fout, Edward T. Furlong, Dana W. Kolpin, Susan T. Glassmeyer, Scott P. Keely

Abstract

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.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 22%
Student > Bachelor 4 17%
Student > Master 4 17%
Student > Doctoral Student 3 13%
Unspecified 3 13%
Other 4 17%
Readers by discipline Count As %
Environmental Science 6 26%
Engineering 5 22%
Unspecified 4 17%
Agricultural and Biological Sciences 2 9%
Chemistry 2 9%
Other 4 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 December 2017.
All research outputs
#7,343,358
of 12,231,187 outputs
Outputs from Science of the Total Environment
#5,137
of 8,359 outputs
Outputs of similar age
#175,266
of 340,221 outputs
Outputs of similar age from Science of the Total Environment
#282
of 612 outputs
Altmetric has tracked 12,231,187 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,359 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.0. This one is in the 36th percentile – i.e., 36% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 340,221 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 612 others from the same source and published within six weeks on either side of this one. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.