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

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.

[1]  J. Leite,et al.  Assessment of burden of virus agents in an urban sewage treatment plant in Rio de Janeiro, Brazil. , 2013, Journal of water and health.

[2]  M. Sobsey,et al.  Human viruses and viral indicators in marine water at two recreational beaches in Southern California, USA. , 2014, Journal of water and health.

[3]  P. Drechsel,et al.  Quantification of human norovirus GII, human adenovirus, and fecal indicator organisms in wastewater used for irrigation in Accra, Ghana. , 2013, Journal of water and health.

[4]  Daisuke Sano,et al.  Bayesian Modeling of Enteric Virus Density in Wastewater Using Left-Censored Data , 2013, Food and Environmental Virology.

[5]  S. Itoh.  Effect of the Ratio of Illness to Infection of Campylobacter on the Uncertainty of DALYs in Drinking Water , 2013 .

[6]  Michiel J W Jansen,et al.  Risk assessment of dietary exposure to pesticides using a Bayesian method. , 2005, Pest management science.

[7]  D. Sano,et al.  New tools for the study and direct surveillance of viral pathogens in water , 2008, Current Opinion in Biotechnology.

[8]  Robert J. Gilliom,et al.  Estimation of Distributional Parameters for Censored Trace Level Water Quality Data: 1. Estimation Techniques , 1986 .

[9]  K. Vairavamoorthy,et al.  Quantitative Microbial Risk Analysis to evaluate health effects of interventions in the urban water system of Accra, Ghana. , 2010, Journal of water and health.

[10]  Joan B. Rose,et al.  Microbial Source Tracking: Current Methodology and Future Directions , 2002, Applied and Environmental Microbiology.

[11]  P. Servais,et al.  Changes in Escherichia coli to Cryptosporidium ratios for various fecal pollution sources and drinking water intakes. , 2014, Water research.

[12]  S. Okabe,et al.  Quantification of host-specific Bacteroides–Prevotella 16S rRNA genetic markers for assessment of fecal pollution in freshwater , 2007, Applied Microbiology and Biotechnology.

[13]  G. Dougan,et al.  Molecular characterization of a carboxy-terminal eukaryotic-cell-binding domain of intimin from enteropathogenic Escherichia coli , 1995, Infection and immunity.

[14]  Valerie J. Harwood,et al.  Microbial Source Tracking , 2006 .

[15]  A. E. Greenberg,et al.  Standard methods for the examination of water and wastewater : supplement to the sixteenth edition , 1988 .

[16]  Use of a genetically-engineered Escherichia coli strain as a sample process control for quantification of the host-specific bacterial genetic markers , 2013, Applied Microbiology and Biotechnology.

[17]  D. Sano,et al.  Water quality monitoring and risk assessment by simultaneous multipathogen quantification. , 2014, Environmental science & technology.

[18]  S. Dorner,et al.  Are microbial indicators and pathogens correlated? A statistical analysis of 40 years of research. , 2011, Journal of water and health.

[19]  Fourth Edition.  Guidelines for Drinking-water Quality, Fourth Edition , 2011 .

[20]  M. Kitajima,et al.  Prevalence and Genetic Diversity of Aichi Viruses in Wastewater and River Water in Japan , 2011, Applied and Environmental Microbiology.

[21]  S. Dorner,et al.  Are microbial indicators and pathogens correlated ? , 2011 .

[22]  Hiroaki Tanaka,et al.  Estimating the safety of wastewater reclamation and reuse using enteric virus monitoring data , 1998 .

[23]  M. Konkel,et al.  Bacterial secreted proteins are required for the internalization of Campylobacter jejuni into cultured mammalian cells , 1999, Molecular microbiology.

[24]  Toshiro Yamada,et al.  Characteristics of Fecal Indicators in Channels of Johkasou Systems , 2014 .

[25]  A. Cohen,et al.  Simplified Estimators for the Normal Distribution When Samples Are Singly Censored or Truncated , 1959 .

[26]  Daisuke Sano,et al.  Chicken- and duck-associated Bacteroides–Prevotella genetic markers for detecting fecal contamination in environmental water , 2012, Applied Microbiology and Biotechnology.

[27]  C N Haas,et al.  Test of the validity of the Poisson assumption for analysis of most-probable-number results , 1988, Applied and environmental microbiology.

[28]  James D Englehardt,et al.  The Discrete Weibull Distribution: An Alternative for Correlated Counts with Confirmation for Microbial Counts in Water , 2011, Risk analysis : an official publication of the Society for Risk Analysis.

[29]  J. Rose,et al.  Detection and characterization of human pathogenic viruses circulating in community wastewater using multi target microarrays and polymerase chain reaction. , 2013, Journal of water and health.

[30]  Satoshi Ishii,et al.  Simultaneous Quantification of Multiple Food- and Waterborne Pathogens by Use of Microfluidic Quantitative PCR , 2013, Applied and Environmental Microbiology.

[31]  Dennis R Helsel,et al.  Fabricating data: how substituting values for nondetects can ruin results, and what can be done about it. , 2006, Chemosphere.

[32]  S. Takizawa,et al.  Assessment of groundwater pollution in Tokyo using PPCPs as sewage markers. , 2012, Environmental science & technology.

[33]  Daisuke Sano,et al.  Microfluidic Quantitative PCR for Simultaneous Quantification of Multiple Viruses in Environmental Water Samples , 2014, Applied and Environmental Microbiology.

[34]  P. Lens,et al.  Application of Quantitative Microbial Risk Assessment to analyze the public health risk from poor drinking water quality in a low income area in Accra, Ghana. , 2013, The Science of the total environment.

[35]  G. Brion,et al.  Multivariate Logistic Regression for Predicting Total Culturable Virus Presence at the Intake of a Potable-Water Treatment Plant: Novel Application of the Atypical Coliform/Total Coliform Ratio , 2007, Applied and Environmental Microbiology.