Frequentist and Bayesian approaches for food allergen risk assessment: risk outcome and uncertainty comparisons

Sophie Birot, Amélie Crépet, Benjamin C. Remington, Charlotte B. Madsen, Astrid G. Kruizinga, Joseph L. Baumert, Per B. Brockhoff

Research output: Contribution to journalArticlepeer-review

Abstract

Peer-reviewed probabilistic methods already predict the probability of an allergic reaction resulting from an accidental exposure to food allergens, however, the methods calculate it in different ways. The available methods utilize the same three major input parameters in the risk model: the risk is estimated from the amount of food consumed, the concentration of allergen in the contaminated product and the distribution of thresholds among allergic persons. However, consensus is lacking about the optimal method to estimate the risk of allergic reaction and the associated uncertainty. This study aims to compare estimation of the risk of allergic reaction and associated uncertainty using different methods and suggest improvements. Four cases were developed based on the previous publications and the risk estimations were compared. The risk estimation was found to agree within 0.5% with the different simulation cases. Finally, an uncertainty analysis method is also presented in order to evaluate the uncertainty propagation from the input parameters to the risk.

Original languageEnglish (US)
Article number18206
JournalScientific reports
Volume9
Issue number1
DOIs
StatePublished - Dec 1 2019

ASJC Scopus subject areas

  • General

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