There is growing concern about our preparedness for controlling the spread of pandemics such as H1N1 Influenza. The dynamics of epidemic spread in large-scale populations are very complex. Further, human behavior, social contact networks, and pandemics are closely intertwined and evolve as the epidemic spread. Individuals changing behaviors in response to public policies and their evolving perception of how an infectious disease outbreak is unfolding can dramatically alter normal social interactions. Effective planning and response strategies must take these complicated interactions into account. Mathematical models are key to understanding the spread of epidemics. In this chapter, we discuss a recent approach of diffusion in network models for studying the complex dynamics of epidemics in large-scale populations. Analyzing these models leads to very challenging computational problems. Further, using these models for forecasting epidemic spread and developing public health policies leads to issues that are characteristic of big data applications. The chapter describes the state of the art in computational and big data epidemiology.