Perelson and colleagues, for example, have used their simple model of the immune system to clarify a debate about the effectiveness of repeated annual flu shots. Previous field studies had led to conflicting conclusions. Statistics to the Rescue As long as models are in the hands of experts, who know the extent to which their simulations rely on approximations and simplifications, no one's fooled by the illusion of accuracy. But when models escape the confines of the cognoscenti, all bets are off. And if the ultimate goal is to take humans out of the loop altogether and let computers run things unsupervised, the need for quantifying uncertainties in the models becomes imperative.
Statistical approaches can help in several ways, workshop participants stress.
Modelers can also look to statistics for help with experimental design and parameter estimation. Another role for statisticians is in building stochastic components or submodels to replace slow-running deterministic computations. And the crucial task of analyzing the uncertainties that propagate through complex models is fertile ground for statistical thinking. There's a lot to be gained on both sides. Tracking transitions between two stable states is tricky business for standard methods, he says.