Sensitivity analysis by experimental design and metamodelling: Case study on simulation in national animal disease control

Sensitivity analysis by experimental design and metamodelling: Case study on simulation in national animal disease control

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Article ID: iaor20042647
Country: Netherlands
Volume: 146
Issue: 3
Start Page Number: 433
End Page Number: 443
Publication Date: May 2003
Journal: European Journal of Operational Research
Authors: , ,
Keywords: simulation: applications
Abstract:

Simulation is a frequently applied tool in the discipline of animal health economics. Application of sensitivity analysis, however, is often limited to changing only one factor at a time (OAT designs). In this study, the statistical techniques of Design of Experiments (DOE) and regression metamodelling were applied to a simulation model developed to support decision making in national animal disease control. Since the simulation response of interest was censored, we also applied – besides ordinary least squares (OLS) regression – tobit and logistic regression. Furthermore, a comparison was made with analysis based on an OAT design. The metamodel estimated by OLS regression showed reasonable fit, but was not considered a valid approximation of the simulation model. We concluded that logistic regression can be applied if output data are binary, whereas tobit regression is most appropriate when dealing with censored data. Furthermore, we concluded that the DOE and metamodelling approach – compared with a simple OAT design – is more effective in describing the relationships between model input and output in this case study.

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