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SOE: Fachverband Physik sozio-ökonomischer Systeme

SOE 13: Economic Growth and Longevity II (Invited Talk Geoffrey West)

SOE 13.1: Hauptvortrag

Dienstag, 12. März 2013, 12:00–12:45, H37

Have we been looking at the spread of epidemics all wrong? — •Dirk Brockmann — Northwestern University, USA

In 2011 the United Nations estimated that the world's population will have reached the seven billion mark at the end of that year with more than 50% of humanity living in densely populated urban areas and megacities. At the same time, our world is massively connected and mobile, more than 3 billion passengers travel on the worldwide air 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.