A General Framework For The Analysis Of Uncertainty And Variability In Risk Assessment

In recent years, there has been a trend toward the use of probabilistic methods for the analysis of uncertainty and variability in risk assessment. By developing a plausible distribution of risk, it is possible to obtain a more complete characterization of risk than is provided by either ''best estimates'' or ''upper bounds'' on risk. In this article, we propose a general framework for the evaluation of uncertainty and variability in risk assessment. Within this framework, the contributions made by individual variables affecting risk to overall uncertainty and variability can be identified. First-order approximations are developed which avoid the need to resort to Monte Carlo simulation for evaluating uncertainty and variability. A practical application based on a multiplicative risk model for a population of individuals ingesting contaminated fish is presented to illustrate the application of the proposed methods.