Discrete, algorithmic simulation and Monte Carlo methodologies are currently used in population biology, connectionist cognitive modeling, and physics. However, little is typically known about the sensitivity of such models to changes in the values of the model features. Traditional methods of sensitivity analysis for systems of differential equations do not apply. Sometimes, one or two parameters are modified at a time in an ad hoc fashion in an attempt to assess sensitivity. To include more model features and their interactions in a sensitivity study, while limiting computer utilization, various sampling methods have been suggested. In this article, a sensitivity study based on a Latin hypercube (LH) sampling design is compared with a similar study using a full factorial (FF), fixed-point sample. A discrete, Monte Carlo model of epidemics of influenzavirus infections in a human community is used for illustrative purposes. Although the FF scheme used over 14 times as many samples as the LH sampling one, both provided comparable predictive ability and comparable information about simulation sensitivity to model features.