With escalating globalization, urbanization, and ecological pressures, the threat of devastating global pandemics becomes more pronounced. The impact of Zika, MERS, and Ebola outbreaks over the past decade has strongly illustrated our enormous vulnerability to emerging infectious diseases. There is an urgent need to develop sound theoretical principles and transformative computational approaches that will allow us to address the escalating threat of a future pandemic. Data mining and Knowledge discovery have an important role to play in this regard. Different aspects of infectious disease modeling, analysis and control have traditionally been studied within the confines of individual disciplines, such as mathematical epidemiology and public health, and data mining and machine learning. Coupled with increasing data generation across multiple domains (like electronic medical records and social media), there is a clear need for analyzing them to inform public health policies and outcomes. Recent advances in disease surveillance and forecasting, and initiatives such as the CDC Flu Challenge, have brought these disciplines closer––public health practitioners seek to use novel datasets and techniques whereas researchers from data mining and machine learning develop novel tools for solving many fundamental problems in the public health policy planning process. We believe the next stage of advances will result from closer collaborations between these two communities, which is the main objective of epiDAMIK.