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Nonlinear Dynamics of Networks

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Predicting the Behaviour of Techno-Social Systems:
How Complex Networks, Physics and Computing Help to Fight Off Global Pandemics

Alessandro Vespignani

Indiana University


Abstract:   We live in an increasingly interconnected world of “techno-social” systems, where infrastructures composed of different technological layers are interoperating within the social component that drives their use and development. The multi-scale nature and complexity of these networks are crucial features in understanding and managing them. I will review the recent advances and challenge in this area and how we can look forward to the generation of sophisticated modeling and computational approaches to anticipate the spreading pattern of a pandemic, predict the traffic pattern of successful web sites or provides insight and recommendations in the case of natural or intentional disruptive events. As a foremost example I will focus on a class of epidemic models that allow the analysis of the impact of complex mobility networks on the behavior of emergent disease spreading and the general issue of the predictive power offered by modeling approaches. In this framework it is possible to tackle foundational issues by using the particle-network approach and provide new mathematical and computational tools for the study of large scale epidemics. In particular I will present recent results concerning the 2009 H1N1 pandemic that exemplify how complex networks science, physics and computing provide predictive tools that help us to battle epidemics.

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