Demonstrating the Benefits of Predictive Bayesian Dose–Response Relationships Using Six Exposure Studies of Cryptosporidium parvum. Issue 11 (21st August 2020)

• Record Type: Journal Article
• Authors: Bloetscher, Frederick; Meeroff, Daniel; Long, Sharon C.; Dudle, Jeanine D.
• Is Part Of: Risk analysis. Volume 40:Issue 11(2020)
• Page Start: 2442
• Page End: 2461
• Publication Date: 2020-08-21
• Subjects: Campylobacter jejuni -- Cryptosporidium -- dose–response functions -- predictive Bayesian -- risk assessment; Technology -- Risk assessment -- Periodicals; 658.403
• DOI: 10.1111/risa.13552
• Languages: English
• ISSNs: 0272-4332

Abstract

Abstract: A conventional dose–response function can be refitted as additional data become available. A predictive dose–response function in contrast does not require a curve‐fitting step, only additional data and presents the unconditional probabilities of illness, reflecting the level of information it contains. In contrast, the predictive Bayesian dose–response function becomes progressively less conservative as more information is included. This investigation evaluated the potential for using predictive Bayesian methods to develop a dose–response for human infection that improves on existing models, to show how predictive Bayesian statistical methods can utilize additional data, and expand the Bayesian methods for a broad audience including those concerned about an oversimplification of dose–response curve use in quantitative microbial risk assessment (QMRA). This study used a dose–response relationship incorporating six separate data sets for Cryptosporidium 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 for Campylobacter 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.