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Sensitivity analysis of a two-dimensional quantitative microbiological risk assessment: keeping variability and uncertainty separated

(2011) RISK ANALYSIS. 31(8). p.1295-1307
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Abstract
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.
Keywords
variability, LISTERIA-MONOCYTOGENES, ASSESSMENT MODEL, uncertainty, sensitivity analysis, Quantitative microbiological risk assessment, SALMON, VARIANCE, CONSUMPTION

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Chicago
Busschaert, Pieter, Annemie H Geeraerd, Mieke Uyttendaele, and Jan F Van Impe. 2011. “Sensitivity Analysis of a Two-dimensional Quantitative Microbiological Risk Assessment: Keeping Variability and Uncertainty Separated.” Risk Analysis 31 (8): 1295–1307.
APA
Busschaert, Pieter, Geeraerd, A. H., Uyttendaele, M., & Van Impe, J. F. (2011). Sensitivity analysis of a two-dimensional quantitative microbiological risk assessment: keeping variability and uncertainty separated. RISK ANALYSIS, 31(8), 1295–1307.
Vancouver
1.
Busschaert P, Geeraerd AH, Uyttendaele M, Van Impe JF. Sensitivity analysis of a two-dimensional quantitative microbiological risk assessment: keeping variability and uncertainty separated. RISK ANALYSIS. 2011;31(8):1295–307.
MLA
Busschaert, Pieter, Annemie H Geeraerd, Mieke Uyttendaele, et al. “Sensitivity Analysis of a Two-dimensional Quantitative Microbiological Risk Assessment: Keeping Variability and Uncertainty Separated.” RISK ANALYSIS 31.8 (2011): 1295–1307. Print.
@article{1985658,
  abstract     = {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.},
  author       = {Busschaert, Pieter and Geeraerd, Annemie H and Uyttendaele, Mieke and Van Impe, Jan F},
  issn         = {0272-4332},
  journal      = {RISK ANALYSIS},
  keyword      = {variability,LISTERIA-MONOCYTOGENES,ASSESSMENT MODEL,uncertainty,sensitivity analysis,Quantitative microbiological risk assessment,SALMON,VARIANCE,CONSUMPTION},
  language     = {eng},
  number       = {8},
  pages        = {1295--1307},
  title        = {Sensitivity analysis of a two-dimensional quantitative microbiological risk assessment: keeping variability and uncertainty separated},
  url          = {http://dx.doi.org/10.1111/j.1539-6924.2011.01592.x},
  volume       = {31},
  year         = {2011},
}

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