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Modeling the U.S. national distribution of waterborne pathogen concentrations with application to Cryptosporidium parvum
Author(s) -
Crainiceanu Ciprian M.,
Stedinger Jery R.,
Ruppert David,
Behr Christopher T.
Publication year - 2003
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2002wr001664
Subject(s) - markov chain monte carlo , cryptosporidium parvum , cryptosporidium , markov chain , bayesian inference , pathogen , inference , statistics , environmental science , statistical inference , water quality , random effects model , bayesian probability , mathematics , biology , ecology , computer science , microbiology and biotechnology , meta analysis , medicine , artificial intelligence , feces
This paper provides a general statistical methodology for modeling environmental pathogen concentrations in natural waters. A hierarchical model of pathogen concentrations captures site and regional random effects as well as random laboratory recovery rates. Recovery rates were modeled by a generalized linear mixed model. Two classes of pathogen concentration models are differentiated according to their ultimate purpose: water quality prediction or health risk analysis. A fully Bayesian analysis using Markov chain Monte Carlo (MCMC) simulation is used for statistical inference. The applicability of this methodology is illustrated by the analysis of a national survey of Cryptosporidium parvum concentrations, in which 93% of the observations were zero counts.