Computational epidemiology aims to understand the spread of diseases and efficient strategies to mitigate their outbreak. It studies dynamics in socio-technical systems, where disease spread co-evolves with public health interventions as well as individual behavior. It has evolved from ODE models to networked models which apply agent based modeling and simulation methodologies. Computation of such high resolution models involves processing data sets that are massive, disparate, heterogeneous, evolving (at an ever increasing rate), and potentially unstructured and of various quality. The workshop brings together researchers from epidemiology, data science, computational science, and health IT domains to tap the potential of emerging technologies in data intensive computations and analytical processing to advance the state of art in computational epidemiology. The central theme of how to manage, integrate, analyze, and visualize vast array of datasets has wider applications in the bio- and physical- simulation and informatics based sciences such as immunology, high energy physics, and, medical informatics.