Demonstrating the Benefits of Predictive Bayesian Dose –Response Relationships Using Six Exposure Studies of Cryptosporidium parvum

This study used a dose–response relation ship incorporating six separate data sets forCryptosporidium parvum. A Pareto II distribution with known priors was applied to one of the six data sets to calibrate the model, while the others were used for subsequent updating. While epidemiological principles indicate that local variations, host susceptibility, and organism strain virulence may vary, the six data sets all appear to be well characterized using the Bayesian approach. The adaptable model was applied to an existing data set forCampylobacter jejuni for model validation purposes, which yielded results that demonstrate the ability to analyze a dose –response function with limited data using and update those relationships with new data. An analysis of the goodness of fit compared to the beta‐Poisson methods also demonstrated correlation between the predictive Bayesian model and the data.

Source: Risk Analysis - August 20, 2020 Category: International Medicine & Public Health Authors: Frederick Bloetscher, Daniel Meeroff, Sharon C. Long, Jeanine D. Dudle Tags: Original Research Article Source Type: research