Introduction: It has been nearly 25 years since the publication of Infectious Disease of Humans (1), the “vade mecum” of mathematical modeling of infectious disease; the proliferation of epidemiological careers that it initiated is now in its fourth generation. Epidemiological models have proved very powerful in shaping health policy discussions. The complex interactions that lead to pathogen (and pest) outbreaks make it necessary to use models to provide quantitative insights into the counterintuitive outcomes that are the rule of most nonlinear systems. Thus, epidemic models are most interesting when they suggest unexpected outcomes; they are most powerful when they describe the conditions that delineate the worst-case unexpected scenario, and provide a framework in which to compare alternative control strategies. But what are the limits of mathematical models and what kinds provide insight into emerging disease?