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 probabilisti c methods for the analysis of uncertainty and variability in risk asse ssment. By developing a plausible distribution of risk, it is possible to obtain a more complete characterization of risk than is provided b y either ''best estimates'' or ''upper bounds'' on risk. In this artic le, we propose a general framework for the evaluation of uncertainty a nd variability in risk assessment. Within this framework, the contribu tions made by individual variables affecting risk to overall uncertain ty and variability can be identified. First-order approximations are d eveloped 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 ingesti ng contaminated fish is presented to illustrate the application of the proposed methods.