Risk Analysis
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Structured Expert Elicitation About <i>Listeria monocytogenes</i> Cross‐Contamination in the Environment of Retail Deli Operations in the United States Listeria monocytogenes is among the foodborne pathogens with the highest death toll in the United States. Ready‐to‐eat foods contaminated at retail are an important source of infection. Environmental sites in retail deli operations can be contaminated. However, commonly contaminated sites are unlikely to come into direct contact with food and the public health relevance of environmental contamination has remained unclear. To identify environmental sites that may pose a considerable cross‐contamination risk, to elucidate potential transmission pathways, and to identify knowledge gaps, we performed a structured expert elicitation of 41 experts from state regulatory agencies and the food retail industry with practical experience in retail deli operations. Following the “Delphi” method, the elicitation was performed in three consecutive steps: questionnaire, review and discussion of results, second questionnaire. Hands and gloves were identified as important potential contamination sources. However, bacterial transfers to and from hands or gloves represented a major data gap. Experts agreed about transfer probabilities from cutting boards, scales, deli cases, and deli preparation sinks to product, and about transfer probabilities from floor drains, walk‐in cooler floors, and knife racks to food contact surfaces. Comparison of experts' opinions to observational data revealed a tendency among experts with certain demographic characteristics and professional opinions to overestimate prevalence. Experts’ votes clearly clustered into separate groups not defined by place of employment, even though industry experts may have been somewhat overrepresented in one cluster. Overall, our study demonstrates the value and caveats of expert elicitation to identify data gaps and prioritize research efforts.
Risk Analysis - Tập 32 Số 7 - Trang 1139-1156 - 2012
Quantitative Risk Estimation for a <i>Legionella pneumophila</i> Infection Due to Whirlpool Use Quantitative microbiological risk assessment was used to quantify the risk associated with the exposure to Legionella pneumophila in a whirlpool. Conceptually, air bubbles ascend to the surface, intercepting Legionella from the traversed water. At the surface the bubble bursts into dominantly noninhalable jet drops and inhalable film drops. Assuming that film drops carry half of the intercepted Legionella , a total of four (95% interval: 1–9) and 4.5×104 (4.4×104 – 4.7×104 ) cfu/min were estimated to be aerosolized for concentrations of 1 and 1,000 legionellas per liter, respectively. Using a dose‐response model for guinea pigs to represent humans, infection risks for active whirlpool use with 100 cfu/L water for 15 minutes were 0.29 (∼0.11–0.48) for susceptible males and 0.22 (∼0.06–0.42) for susceptible females. A L. pneumophila concentration of ≥1,000 cfu/L water was estimated to nearly always cause an infection (mean: 0.95; 95% interval: 0.9–∼1). Estimated infection risks were time‐dependent, ranging from 0.02 (0–0.11) for 1‐minute exposures to 0.93 (0.86–0.97) for 2‐hour exposures when the L. pneumophila concentration was 100 cfu/L water. Pool water in Dutch bathing establishments should contain <100 cfu Legionella /L water. This study suggests that stricter provisions might be required to assure adequate public health protection.
Risk Analysis - Tập 33 Số 7 - Trang 1228-1236 - 2013
Treatment of Uncertainty in Performance Assessments for Complex Systems When viewed at a high level, performance assessments (PAs) for complex systems involve two types of uncertainty: stochastic uncertainty, which arises because the system under study can behave in many different ways, and subjective uncertainty, which arises from a lack of knowledge about quantities required within the computational implementation of the PA. Stochastic uncertainty is typically incorporated into a PA with an experimental design based on importance sampling and leads to the final results of the PA being expressed as a complementary cumulative distribution function (CCDF). Subjective uncertainty is usually treated with Monte Carlo techniques and leads to a distribution of CCDFs. This presentation discusses the use of the Kaplan/Garrick ordered triple representation for risk in maintaining a distinction between stochastic and subjective uncertainty in PAs for complex systems. The topics discussed include (1) the definition of scenarios and the calculation of scenario probabilities and consequences, (2) the separation of subjective and stochastic uncertainties, (3) the construction of CCDFs required in comparisons with regulatory standards (e.g., 40 CFR Part 191, Subpart B for the disposal of radioactive waste), and (4) the performance of uncertainty and sensitivity studies. Results obtained in a preliminary PA for the Waste Isolation Pilot Plant, an uncertainty and sensitivity analysis of the MACCS reactor accident consequence analysis model, and the NUREG‐1150 probabilistic risk assessments are used for illustration.
Risk Analysis - Tập 14 Số 4 - Trang 483-511 - 1994
Representation, Propagation, and Decision Issues in Risk Analysis Under Incomplete Probabilistic Information
Risk Analysis - Tập 30 Số 3 - Trang 361-368 - 2010
Propagation of Uncertainty in Risk Assessments: The Need to Distinguish Between Uncertainty Due to Lack of Knowledge and Uncertainty Due to Variability In quantitative uncertainty analysis, it is essential to define rigorously the endpoint or target of the assessment. Two distinctly different approaches using Monte Carlo methods are discussed: (1) the end point is a fixed but unknown value (e.g., the maximally exposed individual, the average individual, or a specific individual) or (2) the end point is an unknown distribution of values (e.g., the variability of exposures among unspecified individuals in the population). In the first case, values are sampled at random from distributions representing various “degrees of belief” about the unknown “fixed” values of the parameters to produce a distribution of model results. The distribution of model results represents a subjective confidence statement about the true but unknown assessment end point. The important input parameters are those that contribute most to the spread in the distribution of the model results. In the second case, Monte Carlo calculations are performed in two dimensions producing numerous alternative representations of the true but unknown distribution. These alternative distributions permit subject confidence statements to be made from two perspectives: (1) for the individual exposure occurring at a specified fractile of the distribution or (2) for the fractile of the distribution associated with a specified level of individual exposure. The relative importance of input parameters will depend on the fractile or exposure level of interest. The quantification of uncertainty for the simulation of a true but unknown distribution of values represents the state‐of‐the‐art in assessment modeling.
Risk Analysis - Tập 14 Số 5 - Trang 707-712 - 1994
On the Need for Restricting the Probabilistic Analysis in Risk Assessments to Variability
Risk Analysis - Tập 30 Số 3 - Trang 354-360 - 2010
A Combined Monte Carlo and Possibilistic Approach to Uncertainty Propagation in Event Tree Analysis In risk analysis, the treatment of the epistemic uncertainty associated to the probability of occurrence of an event is fundamental. Traditionally, probabilistic distributions have been used to characterize the epistemic uncertainty due to imprecise knowledge of the parameters in risk models. On the other hand, it has been argued that in certain instances such uncertainty may be best accounted for by fuzzy or possibilistic distributions. This seems the case in particular for parameters for which the information available is scarce and of qualitative nature. In practice, it is to be expected that a risk model contains some parameters affected by uncertainties that may be best represented by probability distributions and some other parameters that may be more properly described in terms of fuzzy or possibilistic distributions. In this article, a hybrid method that jointly propagates probabilistic and possibilistic uncertainties is considered and compared with pure probabilistic and pure fuzzy methods for uncertainty propagation. The analyses are carried out on a case study concerning the uncertainties in the probabilities of occurrence of accident sequences in an event tree analysis of a nuclear power plant.
Risk Analysis - Tập 28 Số 5 - Trang 1309-1326 - 2008
Integrating Household Risk Mitigation Behavior in Flood Risk Analysis: An Agent‐Based Model Approach Abstract Recent studies showed that climate change and socioeconomic trends are expected to increase flood risks in many regions. However, in these studies, human behavior is commonly assumed to be constant, which neglects interaction and feedback loops between human and environmental systems. This neglect of human adaptation leads to a misrepresentation of flood risk. This article presents an agent‐based model that incorporates human decision making in flood risk analysis. In particular, household investments in loss‐reducing measures are examined under three economic decision models: (1) expected utility theory, which is the traditional economic model of rational agents; (2) prospect theory, which takes account of bounded rationality; and (3) a prospect theory model, which accounts for changing risk perceptions and social interactions through a process of Bayesian updating. We show that neglecting human behavior in flood risk assessment studies can result in a considerable misestimation of future flood risk, which is in our case study an overestimation of a factor two. Furthermore, we show how behavior models can support flood risk analysis under different behavioral assumptions, illustrating the need to include the dynamic adaptive human behavior of, for instance, households, insurers, and governments. The method presented here provides a solid basis for exploring human behavior and the resulting flood risk with respect to low‐probability/high‐impact risks.
Risk Analysis - Tập 37 Số 10 - Trang 1977-1992 - 2017
An Agent‐Based Model of Evolving Community Flood Risk Abstract Although individual behavior plays a major role in community flood risk, traditional flood risk models generally do not capture information on how community policies and individual decisions impact the evolution of flood risk over time. The purpose of this study is to improve the understanding of the temporal aspects of flood risk through a combined analysis of the behavioral, engineering, and physical hazard aspects of flood risk. Additionally, the study aims to develop a new modeling approach for integrating behavior, policy, flood hazards, and engineering interventions. An agent‐based model (ABM) is used to analyze the influence of flood protection measures, individual behavior, and the occurrence of floods and near‐miss flood events on community flood risk. The ABM focuses on the following decisions and behaviors: dissemination of flood management information, installation of community flood protection, elevation of household mechanical equipment, and elevation of homes. The approach is place based, with a case study area in Fargo, North Dakota, but is focused on generalizable insights. Generally, community mitigation results in reduced future damage, and individual action, including mitigation and movement into and out of high‐risk areas, can have a significant influence on community flood risk. The results of this study provide useful insights into the interplay between individual and community actions and how it affects the evolution of flood risk. This study lends insight into priorities for future work, including the development of more in‐depth behavioral and decision rules at the individual and community level.
Risk Analysis - Tập 38 Số 6 - Trang 1258-1278 - 2018
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