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. In the last decade advances in performance in computer technology, data acquisition and complex networks theory allow the generation of sophisticated simulations on supercomputer infrastructures 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. In particular I will use the example of the current H1N1 pandemic and present computing tools with the ambition of anticipating trends, evaluating risks and eventually managing future public policies in real time. Delivered by Professor Alessandro Vespignani: Professor of Informatics, Indiana University Bloomington, USA. Creative Commons Attribution-Non-Commercial-Share Alike 2.0 UK: England & Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/