In this dissertation, we have proposed solutions to long-standing challenges in data-driven networked epidemic modeling. In the aftermath of the COVID-19 pandemic, and with several other epidemics in recent history, it is clear that advances in how to better integrate large-scale human mobility data are of considerable societal importance. Data-driven models over networks face two fundamental challenges. First, the epidemic network over which an infectious disease spreads is unknown and needs to be estimated. Second, sophisticated networked models are intractable and, consequently, so are parameter identification and optimal control problems in their most general form. The specific problems addressed in this dissertation aim to make contributions on both fronts and span theory and applications. Our contributions to the estimation of the epidemic networks are twofold. First, we propose a theoretical result combining measure theory and spectral graph theory to produce estimates of the spectral radius of a network based on local measurements; which is known to be the epidemic threshold of many epidemic models over networks. Second is a data-driven approach, in which we validate novel mobility data sources and propose a deep-learning-based methodology to construct networks of contact from such sparse signals. On the other hand; we present two tractable methodologies for model calibration and optimal control, respectively. These methodologies combine modern machine-learning tools and large-scale mobility datasets with classical tools from control and optimization. Namely, the extended Kalman Filter and Geometric Programming.