A Modular Bayesian Salmonella Source Attribution Model for Sparse Data.

ABSTRACT: Several statistical models for salmonella source attribution have been presented in the literature. However, these models have often been found to be sensitive to the model parameterization, as well as the specifics of the data set used. The Bayesian salmonella source attribution model presented here was developed to be generally applicable with small and sparse annual data sets obtained over several years. The full Bayesian model was modularized into three parts (an exposure model, a subtype distribution model, and an epidemiological model) in order to separately estimate unknown parameters in each module. The proposed model takes advantage of the consumption and overall salmonella prevalence of the studied sources, as well as bacteria typing results from adjacent years. The latter were u

SUBMITTER: Mikkela A 

PROVIDER: S-EPMC6849795 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature