Quantitative risk assessment (QRA) is rapidly accumulating recognition as the most practical method for assessing the risks associated with microbial contamination of foodstuffs. These risk analyses are most commonly developed in commercial computer spreadsheet applications, combined with Monte Carlo simulation add-ins that enable probability distributions to be inserted into a spreadsheet. If a suitable model structure fan be defined and all of the variables within that model reasonably quantified, a QRA will demonstrate the sensitivity of the severity of the risk to each stage in the risk-assessment model. It can therefore provide guidance for the selection of appropriate risk-reduction measures and a quantitative assessment of the benefits and costs of these proposed measures. However, very few reports explaining QRA models have been submitted for publication in this area. There is, therefore, little guidance available to those who intend to embark on a full microbial QRA. This paper looks at a number of modeling techniques that can help produce more realistic and accurate Monte Carlo simulation models. The use and limitations of several distributions important to microbial risk assessment are explained. Some simple techniques specific to Monte Carlo simulation modelling of microbial risks using spreadsheets are also offered which will help the analyst more realistically reflect the uncertain nature of the scenarios bring modeled. simulation, food safety.
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Drexel Univ, Dept Civil Architectural & Environm Engn, Philadelphia, PA 19104 USADrexel Univ, Dept Civil Architectural & Environm Engn, Philadelphia, PA 19104 USA
Ryan, Michael O.
Haas, Charles N.
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Drexel Univ, Dept Civil Architectural & Environm Engn, Philadelphia, PA 19104 USADrexel Univ, Dept Civil Architectural & Environm Engn, Philadelphia, PA 19104 USA
Haas, Charles N.
Gurian, Patrick L.
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Drexel Univ, Dept Civil Architectural & Environm Engn, Philadelphia, PA 19104 USADrexel Univ, Dept Civil Architectural & Environm Engn, Philadelphia, PA 19104 USA
Gurian, Patrick L.
Gerba, Charles P.
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Univ Arizona, Dept Soil Water & Environm Sci, Tucson, AZ USADrexel Univ, Dept Civil Architectural & Environm Engn, Philadelphia, PA 19104 USA
Gerba, Charles P.
Panzl, Brian M.
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Michigan State Univ, Dept Fisheries & Wildlife, E Lansing, MI 48824 USADrexel Univ, Dept Civil Architectural & Environm Engn, Philadelphia, PA 19104 USA
Panzl, Brian M.
Rose, Joan B.
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Michigan State Univ, Dept Fisheries & Wildlife, E Lansing, MI 48824 USADrexel Univ, Dept Civil Architectural & Environm Engn, Philadelphia, PA 19104 USA
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Korea Food Res Inst, Food Safety & Distribut Res Grp, Wonju 55365, Jeollabuk Do, South Korea
Univ Sci & Technol, Dept Food Biotechnol, Daejeon, South KoreaKorea Food Res Inst, Food Safety & Distribut Res Grp, Wonju 55365, Jeollabuk Do, South Korea
Hwang, Daekeun
Park, Jin Hwa
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Korea Food Res Inst, Food Safety & Distribut Res Grp, Wonju 55365, Jeollabuk Do, South KoreaKorea Food Res Inst, Food Safety & Distribut Res Grp, Wonju 55365, Jeollabuk Do, South Korea
Park, Jin Hwa
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Yoon, Yohan
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Ha, Sang-Do
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Rhee, Min Suk
Koo, Minseon
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Korea Food Res Inst, Food Safety & Distribut Res Grp, Wonju 55365, Jeollabuk Do, South Korea
Univ Sci & Technol, Dept Food Biotechnol, Daejeon, South KoreaKorea Food Res Inst, Food Safety & Distribut Res Grp, Wonju 55365, Jeollabuk Do, South Korea