Emboldened by a string of insights gleaned from time series using likelihood-based inference and stochastic dynamical systems models, we undertook to exploit age-specific disease incidence data using an age-structured stochastic transmission model. In this talk, I explain the study's motivation, in questions surrounding the current resurgence of pertussis in countries with high vaccine coverage, and describe the model we formulated to address these questions. I point out the interesting features of the model implementation and the critical aspects of the inference methodology, with special attention to the challenges associated with this high-dimensional context. I highlight the surprises among the scientific conclusions we drew and conclude by speculating on the unreasonable effectiveness of stochastic models in population biology.