Computational Epidemiology: The role of big data and pervasive informatics

Pandemics such as H1N1 influenza are global outbreaks of infectious disease. Human behavior, social contact networks, and pandemics are closely intertwined. The ordinary behavior and daily activities of individuals create varied and dense social interactions that are characteristic of modern urban societies. They provide a perfect fabric for rapid, uncontrolled disease propagation. During the course of an epidemic, individuals and institutions modify their normal behavior based on their perceived severity and risk. The resulting co-evolution of individual and collective behaviors, contact networks and epidemics must be taken into account while designing effective planning and response strategies.

Recent advances in high performance pervasive computing and big data have created new opportunities for collecting, integrating, analyzing and accessing information about evolving social interactions. The advances in network and information science that build on this new capability provide entirely new ways for reasoning and controlling epidemics.

In this talk I will overview of the state of the art in computational networked epidemiology with an emphasis on computational thinking and high performance computing oriented decision-support environments to support planning and response in the event of pandemics. I will describe our approach within the context of a specific recent application: modeling to support Ebola Outbreak Response in West Africa.

Speaker: Madhav Marathe, Virginia Tech