@article{mokhtari_jaykus_2009, title={Quantitative exposure model for the transmission of norovirus in retail food preparation}, volume={133}, ISSN={["1879-3460"]}, DOI={10.1016/j.ijfoodmicro.2009.04.021}, abstractNote={It is widely recognized that the human noroviruses (HuNoV) are responsible for a large proportion of the world's foodborne disease burden. These viruses are transmitted by human fecal contamination and frequently make their way into foods because of poor personal hygiene of infected food handlers. This paper describes a probabilistic exposure assessment which models the dynamics of the transmission of HuNoV in the retail food preparation environment. Key inputs included degree of fecal shedding, hand hygiene behaviors, efficacy of virus removal and/or inactivation, and transferability of virus between surfaces. The model has a temporal dimension allowing contamination to be estimated as a function of time over the simulation period. Sensitivity and what-if scenario analyses were applied to identify the most important model inputs and evaluate potential mitigation strategies. The key inputs affecting estimates of the number of infectious viruses present in contaminated food servings, given the current model structure and assumptions, were as follows: mass of feces on hands (m(FH)), concentration of virus in feces (nv(CF)), number of bathroom visits, degree of gloving compliance (p(WG)), hand-washing efficiency (HW(eff)), and hand-washing compliance (p(HW)). The model suggests that gloving and hand-washing compliance are most effective in controlling contamination of food products when practiced simultaneously. Moreover, the bathroom environment was identified as a major reservoir of HuNoV, even in the absence of an ill individual on site. This mathematical approach to modeling the transmission of gastrointestinal viruses should facilitate comparison of potential mitigations aimed at reducing the transmission of foodborne viruses.}, number={1-2}, journal={INTERNATIONAL JOURNAL OF FOOD MICROBIOLOGY}, author={Mokhtari, Amirhossein and Jaykus, Lee-Ann}, year={2009}, month={Jul}, pages={38–47} }
 @article{vicari_mokhtari_morales_jaykus_frey_slenning_cowen_2007, title={Second-order modeling of variability and uncertainty in microbial hazard characterization}, volume={70}, ISSN={["0362-028X"]}, DOI={10.4315/0362-028X-70.2.363}, abstractNote={This study describes an analytical framework that permits quantitative consideration of variability and uncertainty in microbial hazard characterization. Second-order modeling that used two-dimensional Monte Carlo simulation and stratification into homogeneous population subgroups was applied to integrate uncertainty and variability. Specifically, the bootstrap method was used to simulate sampling error due to the limited sample size in microbial dose-response modeling. A data set from human feeding trials with Campylobacter jejuni was fitted to the log-logistic dose-response model, and results from the analysis of FoodNet surveillance data provided further information on variability and uncertainty in Campylobacter susceptibility due to the effect of age. Results of our analyses indicate that uncertainty associated with dose-response modeling has a dominating influence on the analytical outcome. In contrast, inclusion of the age factor has a limited impact. While the advocacy of more closely modeling variability in hazard characterization is warranted, the characterization of key sources of uncertainties and their consistent propagation throughout a microbial risk assessment actually appear of greater importance.}, number={2}, journal={JOURNAL OF FOOD PROTECTION}, author={Vicari, Andrea S. and Mokhtari, Amirhossein and Morales, Roberta A. and Jaykus, Lee-Ann and Frey, H. Christopher and Slenning, Barrett D. and Cowen, Peter}, year={2007}, month={Feb}, pages={363–372} }
 @article{mokhtari_frey_jaykus_2006, title={Application of classification and regression trees for sensitivity analysis of the Escherichia coli O157 : H7 food safety process risk model}, volume={69}, ISSN={["1944-9097"]}, DOI={10.4315/0362-028X-69.3.609}, abstractNote={Microbial food safety process risk models are simplifications of the real world that help risk managers in their efforts to mitigate food safety risks. An important tool in these risk assessment endeavors is sensitivity analysis, a systematic method used to quantify the effect of changes in input variables on model outputs. In this study, a novel sensitivity analysis method called classification and regression trees was applied to safety risk assessment with the use of portions of the Slaughter Module and Preparation Module of the E. coli O157:H7 microbial food safety process risk as an example. Specifically, the classification and regression trees sensitivity analysis method was evaluated on the basis of its ability to address typical characteristics of microbial food safety process risk models such as nonlinearities, interaction, thresholds, and categorical inputs. Moreover, this method was evaluated with respect to identification of high exposure scenarios and corresponding key inputs and critical limits. The results from the classification and regression trees analysis applied to the Slaughter Module confirmed that the process of chilling carcasses is a critical control point. The method identified a cutoff value of a 2.2-log increase in the number of organisms during chilling as a critical value above which high levels of contamination would be expected. When classification and regression trees analysis was applied to the cooking effects part of the Preparation Module, cooking temperature was found to be the most sensitive input, with precooking treatment (i.e., raw product storage conditions) ranked second in importance. This case study demonstrates the capabilities of classification and regression trees analysis as an alternative to other statistically based sensitivity analysis methods, and one that can readily address specific characteristics that are common in microbial food safety process risk models.}, number={3}, journal={JOURNAL OF FOOD PROTECTION}, author={Mokhtari, A and Frey, HC and Jaykus, LA}, year={2006}, month={Mar}, pages={609–618} }
 @article{yang_mokhtari_jaykus_morales_cates_cowen_2006, title={Consumer phase risk assessment for Listeria monocytogenes in deli meats}, volume={26}, DOI={10.1111/j.1539-6924.2006.00071.x}, number={1}, journal={Risk Analysis}, author={Yang, H. and Mokhtari, A. and Jaykus, L. A. and Morales, R. A. and Cates, S. C. and Cowen, P.}, year={2006}, pages={89–103} }
 @article{mokhtari_moore_yang_jaykus_morales_cates_cowen_2006, title={Consumer-phase Salmonella enterica serovar Enteritidis risk assessment for egg-containing food products}, volume={26}, ISSN={["1539-6924"]}, DOI={10.1111/j.1539-6924.2006.00759.x}, abstractNote={We describe a one‐dimensional probabilistic model of the role of domestic food handling behaviors on salmonellosis risk associated with the consumption of eggs and egg‐containing foods. Six categories of egg‐containing foods were defined based on the amount of egg contained in the food, whether eggs are pooled, and the degree of cooking practiced by consumers. We used bootstrap simulation to quantify uncertainty in risk estimates due to sampling error, and sensitivity analysis to identify key sources of variability and uncertainty in the model. Because of typical model characteristics such as nonlinearity, interaction between inputs, thresholds, and saturation points, Sobol's method, a novel sensitivity analysis approach, was used to identify key sources of variability. Based on the mean probability of illness, examples of foods from the food categories ranked from most to least risk of illness were: (1) home‐made salad dressings/ice cream; (2) fried eggs/boiled eggs; (3) omelettes; and (4) baked foods/breads. For food categories that may include uncooked eggs (e.g., home‐made salad dressings/ice cream), consumer handling conditions such as storage time and temperature after food preparation were the key sources of variability. In contrast, for food categories associated with undercooked eggs (e.g., fried/soft‐boiled eggs), the initial level of Salmonella contamination and the log10 reduction due to cooking were the key sources of variability. Important sources of uncertainty varied with both the risk percentile and the food category under consideration. This work adds to previous risk assessments focused on egg production and storage practices, and provides a science‐based approach to inform consumer risk communications regarding safe egg handling practices.}, number={3}, journal={RISK ANALYSIS}, author={Mokhtari, Amirhossein and Moore, Christina M. and Yang, Hong and Jaykus, Lee-Ann and Morales, Roberta and Cates, Sheryl C. and Cowen, Peter}, year={2006}, month={Jun}, pages={753–768} }
 @article{mokhtari_frey_zheng_2006, title={Evaluation and recommendation of sensitivity analysis methods for application to Stochastic Human Exposure and Dose Simulation models}, volume={16}, ISSN={["1559-064X"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-33751086727&partnerID=MN8TOARS}, DOI={10.1038/sj.jes.7500472}, abstractNote={Sensitivity analyses of exposure or risk models can help identify the most significant factors to aid in risk management or to prioritize additional research to reduce uncertainty in the estimates. However, sensitivity analysis is challenged by non-linearity, interactions between inputs, and multiple days or time scales. Selected sensitivity analysis methods are evaluated with respect to their applicability to human exposure models with such features using a testbed. The testbed is a simplified version of a US Environmental Protection Agency's Stochastic Human Exposure and Dose Simulation (SHEDS) model. The methods evaluated include the Pearson and Spearman correlation, sample and rank regression, analysis of variance, Fourier amplitude sensitivity test (FAST), and Sobol's method. The first five methods are known as "sampling-based" techniques, wheras the latter two methods are known as "variance-based" techniques. The main objective of the test cases was to identify the main and total contributions of individual inputs to the output variance. Sobol's method and FAST directly quantified these measures of sensitivity. Results show that sensitivity of an input typically changed when evaluated under different time scales (e.g., daily versus monthly). All methods provided similar insights regarding less important inputs; however, Sobol's method and FAST provided more robust insights with respect to sensitivity of important inputs compared to the sampling-based techniques. Thus, the sampling-based methods can be used in a screening step to identify unimportant inputs, followed by application of more computationally intensive refined methods to a smaller set of inputs. The implications of time variation in sensitivity results for risk management are briefly discussed.}, number={6}, journal={JOURNAL OF EXPOSURE SCIENCE AND ENVIRONMENTAL EPIDEMIOLOGY}, author={Mokhtari, Amirhossein and Frey, H. Christopher and Zheng, Junyu}, year={2006}, month={Nov}, pages={491–506} }
 @article{mokhtari_frey_2006, title={Evaluation of sampling-based methods for sensitivity analysis: Case study for the E-Coli food safety process risk model}, volume={12}, ISSN={["1080-7039"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-33750417713&partnerID=MN8TOARS}, DOI={10.1080/10807030600977251}, abstractNote={ABSTRACT This article evaluates selected sensitivity analysis methods applicable to risk assessment models with two-dimensional probabilistic frameworks, using a microbial food safety process risk model as a test-bed. Six sampling-based sensitivity analysis methods were evaluated including Pearson and Spearman correlation, sample and rank linear regression, and sample and rank stepwise regression. In a two-dimensional risk model, the identification of key controllable inputs that can be priorities for risk management can be confounded by uncertainty. However, despite uncertainty, results show that key inputs can be distinguished from those that are unimportant, and inputs can be grouped into categories of similar levels of importance. All selected methods are capable of identifying unimportant inputs, which is helpful in that efforts to collect data to improve the assessment or to focus risk management strategies can be prioritized elsewhere. Rank-based methods provided more robust insights with respect to the key sources of variability in that they produced narrower ranges of uncertainty for sensitivity results and more clear distinctions when comparing the importance of inputs or groups of inputs. Regression-based methods have advantages over correlation approaches because they can be configured to provide insight regarding interactions and nonlinearities in the model.}, number={6}, journal={HUMAN AND ECOLOGICAL RISK ASSESSMENT}, author={Mokhtari, Amirhossein and Frey, H. Christopher}, year={2006}, month={Dec}, pages={1128–1152} }
 @article{mokhtari_frey_2005, title={Recommended practice regarding selection of sensitivity analysis methods applied to microbial food safety process risk models}, volume={11}, ISSN={["1549-7860"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-22244443715&partnerID=MN8TOARS}, DOI={10.1080/10807030590949672}, abstractNote={ABSTRACT A guideline is presented for selection of sensitivity analysis methods applied to microbial food safety process risk (MFSPR) models. The guideline provides useful boundaries and principles for selecting sensitivity analysis methods for MSFPR models. Although the guideline is predicated on a specific branch of risk assessment models related to food-borne diseases, the principles and recommendations provided are typically generally applicable to other types of risk models. Applicable situations include: prioritizing potential critical control points; identifying key sources of variability and uncertainty; and refinement, verification, and validation of a model. Based on the objective of the analysis, characteristics of the model under study, amount of detail expected from sensitivity analysis, and characteristics of the sensitivity analysis method, recommendations for selection of sensitivity analysis methods are provided. A decision framework for method selection is introduced. The decision framework can substantially facilitate the process of selecting a sensitivity analysis method.}, number={3}, journal={HUMAN AND ECOLOGICAL RISK ASSESSMENT}, author={Mokhtari, A and Frey, HC}, year={2005}, month={Jun}, pages={591–605} }
 @article{mokhtari_frey_2005, title={Sensitivity analysis of a two-dimensional probabilistic risk assessment model using analysis of variance}, volume={25}, ISSN={["1539-6924"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-31944435421&partnerID=MN8TOARS}, DOI={10.1111/j.1539-6924.2005.00679.x}, abstractNote={This article demonstrates application of sensitivity analysis to risk assessment models with two‐dimensional probabilistic frameworks that distinguish between variability and uncertainty. A microbial food safety process risk (MFSPR) model is used as a test bed. The process of identifying key controllable inputs and key sources of uncertainty using sensitivity analysis is challenged by typical characteristics of MFSPR models such as nonlinearity, thresholds, interactions, and categorical inputs. Among many available sensitivity analysis methods, analysis of variance (ANOVA) is evaluated in comparison to commonly used methods based on correlation coefficients. In a two‐dimensional risk model, the identification of key controllable inputs that can be prioritized with respect to risk management is confounded by uncertainty. However, as shown here, ANOVA provided robust insights regarding controllable inputs most likely to lead to effective risk reduction despite uncertainty. ANOVA appropriately selected the top six important inputs, while correlation‐based methods provided misleading insights. Bootstrap simulation is used to quantify uncertainty in ranks of inputs due to sampling error. For the selected sample size, differences in F values of 60% or more were associated with clear differences in rank order between inputs. Sensitivity analysis results identified inputs related to the storage of ground beef servings at home as the most important. Risk management recommendations are suggested in the form of a consumer advisory for better handling and storage practices.}, number={6}, journal={RISK ANALYSIS}, author={Mokhtari, A and Frey, HC}, year={2005}, month={Dec}, pages={1511–1529} }