Modeling the U.S. national distribution of waterborne pathogen concentrations with application to Cryptosporidium parvum

Ciprian M. Crainiceanu, Jery R. Stedinger, David Ruppert, Christopher T. Behr

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)SWC21-SWC215
JournalWater Resources Research
Volume39
Issue number9
DOIs
StatePublished - Sep 2003
Externally publishedYes

Keywords

  • Bayesian analysis
  • Cryptosporidium parvum
  • Generalized linear mixed model
  • Markov Chain Monte Carlo
  • Waterborne pathogens

ASJC Scopus subject areas

  • Water Science and Technology

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