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Estimation of concentration ratio of indicator to pathogen-related gene in environmental water based on left-censored data

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/62573

Title: Estimation of concentration ratio of indicator to pathogen-related gene in environmental water based on left-censored data
Authors: Kato, Tsuyoshi Browse this author →KAKEN DB
Kobayashi, Ayano Browse this author
Ito, Toshihiro Browse this author
Miura, Takayuki Browse this author
Ishii, Satoshi Browse this author
Okabe, Satoshi Browse this author →KAKEN DB
Sano, Daisuke Browse this author →KAKEN DB
Keywords: analytical quantification limit
Bacteroides
Bayesian estimation
indicator micro-organisms
left-censored data
pathogens
Issue Date: Feb-2016
Publisher: IWA Publishing
Journal Title: Journal of water and health
Volume: 14
Issue: 1
Start Page: 14
End Page: 25
Publisher DOI: 10.2166/wh.2015.029
PMID: 26837826
Abstract: A stochastic model for estimating the ratio between a fecal indicator and a pathogen based on left-censored data, which includes a substantially high number of non-detects, was constructed. River water samples were taken for 16 months at six points in a river watershed, and conventional fecal indicators (total coliforms and general Escherichia coli), genetic markers (Bacteroides spp.), and virulence genes (eaeA of enteropathogenic E. coli and ciaB of Campylobacter jejuni) were quantified. The quantification of general E. coli failed to predict the presence of the virulence gene from enteropathogenic E. coli, different from what happened with genetic markers (Total Bac and Human Bac). A Bayesian model that was adapted to left-censored data with a varying analytical quantification limit was applied to the quantitative data, and the posterior predictive distributions of the concentration ratio were predicted. When the sample size was 144, simulations conducted in this study suggested that 39 detects were enough to accurately estimate the distribution of the concentration ratio, when combined with a dataset with a positive rate higher than 99%. To evaluate the level of accuracy in the estimation, it is desirable to perform a simulation using an artificially generated left-censored dataset that has the identical number of non-detects as the actual data.
Rights: ©IWA Publishing 2016. The definitive peer-reviewed and edited version of this article is published in Journal of water and health 14(1) 14-25 2016 DOI: 10.2166/wh.2015.029 and is available at www.iwapublishing.com.
Type: article (author version)
URI: http://hdl.handle.net/2115/62573
Appears in Collections:工学院・工学研究院 (Graduate School of Engineering / Faculty of Engineering) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 佐野 大輔

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