These model examples are just a few of the types of very high-dimensional models that are currently of interest. As a result of the high dimensionality, there is much computation involved, and the analysis is quite difficult. In particular, real-time computation is not currently possible. However, there are usually many time scales that are well separated ͑due typically to a large range in order of magnitude of the parameters͒ when considering such high-dimensional problems. In the presence of well-separated time scales, a model reduction method is needed to examine the dynamics on a lower-dimensional space. It is known that deterministic model reduction methods may not work well in the stochastic realm, which includes epidemic models. 17 The purpose of this article is to examine a method of nonlinear, stochastic projection so that the deterministic and stochastic dynamics interact correctly on the lower-dimensional manifold and predict correctly the dynamics when compared with the full system. Because the noise affects the timing of outbreaks, it is essential to produce a low-dimensional system that captures the correct timing of the outbreaks as well as the amplitude and phase of any recurrent behavior.