3rd Joint Seminar on Computational Intelligence

Abstract: The development of computational approaches to solving problems in Public Health has commenced only about two decades ago and has led to the emergence of the field of Computational Epidemiology. Its primary goal is to provide health researchers with computational tools that facilitate the prediction and analysis of the progression of diseases in time and space. Whether modeling an Influenza epidemic in Germany or the spread of Dengue Fever in Thailand, computational models must be developed, which are informed by data from disparate sources. The design, implementation, and execution of such models represents a significant scientific challenge as it is often difficult to validate their fidelity. Further, data availability and representation across different geographic regions is inconsistent at best, which necessitates the design of region specific models. In this tutorial, we will introduce Computational Epidemiology as a data-centric example of computational intelligence for which Big Data challenges will have to be addressed by today’s computer and information scientists. We will exemplify the types of data that will have to be integrated to inform model development in an effort to provide computational tools to researchers and practitioners in the domain of Population Health.