The World Health Organization (WHO) estimates that 1.5 million deaths among children under 5 years are due to vaccine-preventable diseases (VPDs), predominantly in the developing world where achieving vaccine coverage necessary for elimination has proved a challenge. Surprisingly, even in wealthy countries such as the United States, herd immunity has proved elusive despite historically high vaccine coverage. This is in large part due to the phenomenon of vaccine hesitancy, or the desire to delay or refuse vaccination, which poses a barrier to covering the last mile in global disease eradication efforts for VPDs such as measles and polio. Achieving the “endgame” will require early detection of vaccine hesitancy, as well as a clear understanding of the impact of this behavior on the (re)emergence and spread of VPDs. What is urgently needed is a means of integrating disparate data sources within a mechanistic transmission model in order to quantify population immunity and discriminate among alternative courses of action. The proposed work aims to harness the potential of a unique high-resolution data set of medical claims to geographically localize vaccination hesitancy, to identify its socio- economic drivers, and dissect the epidemiological consequences of this behavior with measles and pertussis as case studies. Ultimately, our goal is to develop a predictive model for the spatial and temporal dynamics of both vaccination behavior and the incidence of affected infectious diseases. Such a model will be key in our ability to forecast disease re-emergence and design public health policy to positively change the vaccination landscape.