This paper presents a Bayesian modeling approach to quantify the level of disinfection
achieved from raw infectivity data. Drinking water disinfection experiments often use
animal tests and a logistic regression data analysis to evaluate the performance of a
disinfectant. The proposed Bayesian method addresses statistical problems in the
commonly used logistic regression approach and uses more realistic probabilistic
assumptions about the data to predict disinfectant dose and pathogen response
relationships. The new methods are compared to the commonly used methods using one
published data set for UV light disinfection of Cryptosporidium. The Bayesian approach
yielded comparable log inactivation estimates to those from the conventional methods.
However, the Bayesian method estimated UV dose required to achieve 3 log inactivation
significantly different (smaller) from the same using conventional regression modeling
approach based on the estimated log-inactivation as the known response variable values. Includes 6 references, table, figures.
| Edition : | Vol. - No. |
| File Size : | 1
file
, 310 KB |
| Note : | This product is unavailable in Ukraine, Russia, Belarus |
| Number of Pages : | 12 |
| Published : | 11/15/2004 |
