Elicitation for food microbial risk assessment: a probabilistic approach extending Risk Ranger proposal (original) (raw)

Quantitative estimation of uncertainty in human risk analysis

Journal of Hazardous Materials, 2007

This paper is aimed to candidate the use of an ISO standard procedure (Guide to the Expression of Uncertainty in Measurement, GUM) for quantitative evaluation of uncertainty in Human Risk estimation under chronic exposure to a hazardous chemical compound. Risk was evaluated by using the usual methodologies: the deterministic reasonable maximum exposure (RME) and the statistical Monte Carlo method; in both cases the procedures to evaluate the uncertainty on risk values are detailed.

Applications of Probabilistic Risk Assessments: The Selection of Appropriate Tools1

Risk Analysis, 1991

Probabilistic risk assessment (PRA) is an important methodology for assessing the risks of complex technologies. This paper discusses the strengths and weaknesses of PRA. Its application is explored in three different settings: adversarial policy processes, regulatory/licensing procedures, and plant safety audits. It is concluded that PRA is a valuable tool for auditing safety precautions of existing or planned technologies, especially when it is carried out as an interactive process involving designers and plant personnel who are familiar with actual, everyday operations. PRA has not proven to be as well-suited in providing absolute risk estimates in public-policy debates concerning the acceptability of a technology, or for the licensing and regulatory procedures. The reasons for this are discussed.

A comprehensive risk assessment system for probabilistic problems

Computational Ecology and Software, 2023

http://www.iaees.org/publications/journals/ces/articles/2023-13(2)/2-Zhang-Abstract.asp In present study we provided a comprehensive risk assessment system. The system consists of four representative methods, namely probability-deterministic assessment, probability-interval assessment, probability-ranking assessment, and probability-ranking with optimal mix strategy. Among them, the probability-interval assessment was proposed by us. In the risk assessment, there are multiple available states, but only one of them can occur in nature. The occurrence probability of each state is the determined value and interval respectively for the first two methods; and for the latter two methods it is the probability difference of each pair of adjacent states. Known the benefit matrix, plans ranking can be derived from the first three methods according to the expected benefit; the fourth method can be used to obtain the optimal mix proportion of plans. The Matlab full code of the assessment system was given for further application and improvement.

On the Treatment of Uncertainty and Variability in Making Decisions About Risk

Risk Analysis, 2013

Much attention has been paid to the treatment of dependence and to the characterization of uncertainty and variability (including the issue of dependence among inputs) in performing risk assessments to avoid misleading results. However, with relatively little progress in communicating about the effects and implications of dependence, the effort involved in performing relatively sophisticated risk analyses (e.g., two-dimensional Monte Carlo analyses that separate variability from uncertainty) may be largely wasted, if the implications of those analyses are not clearly understood by decisionmakers. This article emphasizes that epistemic uncertainty can introduce dependence among related risks (e.g., risks to different individuals, or at different facilities), and illustrates the potential importance of such dependence in the context of two important types of decisions-evaluations of risk acceptability for a single technology, and comparisons of the risks for two or more technologies. We also present some preliminary ideas on how to communicate the effects of dependence to decisionmakers in a clear and easily comprehensible manner, and suggest future research directions in this area.

On the use of confidence levels in risk management

Journal of Hazardous Materials, 1985

A framework for incorporating uncertainty in risk management is developed and applied to two aspects of decision making: meeting standards or safety goals, and cost-benefit criteria. The framework is applied to several case studies including toxic chemicals in water, failure of civil engineering structures and nuclear power plants. The framework proposes that decisions be based on a level of confidence, in addition to comparing best estimate or point values with standards and goals.

A new scientific framework for quantitative risk assessments

International Journal of Business Continuity and Risk Management, 2009

Many analysts consider Quantitative Risk Assessment (QRA) to be an application of statistics and founded on the natural science paradigm. However, if the goal is the accurate estimation of some true underlying risk parameters, QRA fails as a scientific method. The alternative is to consider QRA to be a method for describing uncertainties. The purpose of this paper is to present and discuss a new scientific framework founded on such a perspective. The framework is based on knowledge-based (subjective) probabilities to express uncertainties about unknown quantities, as well as on the qualitative assessment of uncertainties extending beyond the probabilistic analysis. Risk is viewed as the combination of events/consequences and associated uncertainties. Critical methodological issues, such as model uncertainty, are clarified. Several examples are included to motivate and explain the basic ideas of the framework.

What risk assessments can tell us about setting criteria

Food Control, 2011

The Food Safety Objective (FSO) paradigm has been developed as a risk-based approach to microbial food safety. To be operational, this paradigm requires that an Acceptable Level of Protection (ALOP) and a FSO be quantitatively defined. It then becomes the industry's task to produce foods that achieve the FSO. A two-dimensional risk assessment, which separates variation and uncertainty, can help design a process or validate an existing process. If the initially proposed or existing process parameters do not meet the FSO, the sensitivity analysis can show which parameters have high uncertainty or variability that can be better controlled or how much a parameter must change for the process to achieve the FSO.

risk analysis

Identification and qualitative comparison of sensitivity analysis methods that have been used across various disciplines, and that merit consideration for application to food-safety risk assessment models, are presented in this article. Sensitivity analysis can help in identifying critical control points, prioritizing additional data collection or research, and verifying and validating a model. Ten sensitivity analysis methods, including four mathematical methods, five statistical methods, and one graphical method, are identified. The selected methods are compared on the basis of their applicability to different types of models, computational issues such as initial data requirement and complexity of their application, representation of the sensitivity, and the specific uses of these methods. Applications of these methods are illustrated with examples from various fields. No one method is clearly best for food-safety risk models. In general, use of two or more methods, preferably with dissimilar theoretical foundations, may be needed to increase confidence in the ranking of key inputs.