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This paper presents a Bayesian modeling approach to quantify the level of disinfectionachieved from raw infectivity data. Drinking water disinfection experiments often useanimal tests and a logistic regression data analysis to evaluate the performance of adisinfectant. The proposed Bayesian method addresses statistical problems in thecommonly used logistic regression approach and uses more realistic probabilisticassumptions about the data to predict disinfectant dose and pathogen responserelationships. The new methods are compared to the commonly used methods using onepublished data set for UV light disinfection of Cryptosporidium. The Bayesian approachyielded comparable log inactivation estimates to those from the conventional methods.However, the Bayesian method estimated UV dose required to achieve 3 log inactivationsignificantly different (smaller) from the same using conventional regression modelingapproach based on the estimated log-inactivation as the known response variable values. Includes 6 references, table, figures.

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Edition: Vol. - No. Published: 11/15/2004 Number of Pages: 12File Size: 1 file , 310 KB