CPMF2—Flemish Cluster Predictive Microbiology in Foods, Division of Chemical and Biochemical Process Technologyand Control (BioTeC), Department of Chemical Engineering, Katholieke Universiteit Leuven. W. de Croylaan 46, B-3001 Leuven, Belgium.
Risk Anal. 2011 Aug;31(8):1295-307. doi: 10.1111/j.1539-6924.2011.01592.x. Epub 2011 Mar 18.
The aim of quantitative microbiological risk assessment is to estimate the risk of illness caused by the presence of a pathogen in a food type, and to study the impact of interventions. Because of inherent variability and uncertainty, risk assessments are generally conducted stochastically, and if possible it is advised to characterize variability separately from uncertainty. Sensitivity analysis allows to indicate to which of the input variables the outcome of a quantitative microbiological risk assessment is most sensitive. Although a number of methods exist to apply sensitivity analysis to a risk assessment with probabilistic input variables (such as contamination, storage temperature, storage duration, etc.), it is challenging to perform sensitivity analysis in the case where a risk assessment includes a separate characterization of variability and uncertainty of input variables. A procedure is proposed that focuses on the relation between risk estimates obtained by Monte Carlo simulation and the location of pseudo-randomly sampled input variables within the uncertainty and variability distributions. Within this procedure, two methods are used-that is, an ANOVA-like model and Sobol sensitivity indices-to obtain and compare the impact of variability and of uncertainty of all input variables, and of model uncertainty and scenario uncertainty. As a case study, this methodology is applied to a risk assessment to estimate the risk of contracting listeriosis due to consumption of deli meats.
定量微生物风险评估的目的是估计某种食物类型中病原体存在导致疾病的风险，并研究干预措施的影响。由于存在固有变异性和不确定性，风险评估通常是随机进行的，如果可能，建议将变异性与不确定性分别进行描述。敏感性分析可用于指示定量微生物风险评估的结果对输入变量的哪些变量最为敏感。尽管存在许多方法可用于对具有概率输入变量的风险评估（如污染、储存温度、储存时间等）进行敏感性分析，但在风险评估中包括对输入变量的变异性和不确定性进行单独描述的情况下，进行敏感性分析具有挑战性。提出了一种程序，该程序侧重于通过蒙特卡罗模拟获得的风险估计值与在不确定性和变异性分布内伪随机抽样的输入变量位置之间的关系。在该程序中，使用两种方法，即类似于方差分析的模型和 Sobol 敏感性指数，以获得和比较所有输入变量的变异性和不确定性的影响，以及模型不确定性和情景不确定性的影响。作为案例研究，将该方法应用于风险评估，以估计因食用熟食肉类而感染李斯特菌病的风险。
