In order to improve confidence in model-based conclusions, it is necessary to gain a thorough understanding of the system and assess how model assumptions and parameters alter the results. Surrogate models are very useful for this purpose since they can be readily explored. We used Pareto-aware symbolic regression to analyze input-response data from an open source individual-based model for pandemic influenza, called FluTE . Here you find a visualization tool to explore the response surfaces from six parameters on the cumulative clinical attack rate. Every parameter must be chosen and the predicted response plots are shown for each parameter with all other parameters fixed.
Surrogate modeling is relevant for many public health problems. We also analyzed the results from an economic evaluation described in van Hoek et al  to estimate the quality adjusted life year gain of varicella-zoster vaccination.