transportation every years. Increased interactions and mobility has increased the emergence and rapid spread of novel diseases, as exemplified by the 2009 H1N1 pandemic and the 2003 outbreak of SARS. One of the most important challenges in complex systems research is the development of models that can predict the time course of epidemics, as well as the improve our understanding of fundamental properties that govern the dynamics of such events. To this end a massive amount of research effort based on pervasive data in combination with extremely sophisticated computer simulations has been designed to tackle this problem. It is generally agreed that the complexity of the problem is driven by the interaction of a variety of factors, disease specific parameters, social structure, population heterogeneity, etc., and that these factors need to be taken into account in order to improve predictability of pandemic events. I will report on recent research which shows that the dynamics of pandemic diseases are much simpler than expected if viewed from the right angle. I will show that to a large extent the complexity of modern pandemics is only dominated by the intricate connectivity of large scale mobility networks. I will propose a way to unravel the complexity of multiscale mobility networks based on the intuitive notion of effective distance. This method not only simplifies our view of pandemic dynamics, and explains the degree of their predictability, it allow the determination of outbreak origins, a feature particularly important during the onset of an epidemic.