Identification and qualitative comparison of sensitivity analysis methods that have been used across various disciplines, and that merit consideration for application to food safety risk assessment models are presented in this paper. Sensitivity analysis can help in identifying critical control points, prioritizing additional data collection or research, and verifying and validating a model. Ten sensitivity analysis methods, including four mathematical methods, five statistical methods and one graphical method, are identified. Application of these methods was also illustrated with the examples from various fields. These methods were compared on the basis of their applicability to different types of models, computational issues such as initial data requirement, time requirement, and complexity of their application, representation of the sensitivity, and the specific uses of these methods. No one method is clearly best for food safety risk models. In general, the use of two or more methods may be needed to increase confidence on the rank ordering of key inputs.To identify specific issues with respect to the application to a typical food safety risk model, the sensitivity analysis methods were applied to the risk assessment model of the public health impact of vibrio Parahaemolyticus (the Vp model). The Vp model was modified so that proper sensitivity analysis can be done on independent inputs. The results of the sensitivity analyses were interpreted and discussed in detail. The rank ordering of key inputs was reasonably similar for most of the methods. For example, five of the seven methods ranked water temperature, the number of oysters per meal, and a new input IUR in the top three. Time on water and an input IG were identified as the least important inputs by six methods.
