Abstract. We study the inflence of global, local and community-level risk perception on the extinction probability of a disease in several mod- els of social networks. In particular, we study the infection progression as a susceptible-infected-susceptible (SIS) model on several modular net- works, formed by a certain number of random and scale-free communi- ties. We find that in the scale-free networks the progression is faster than in random ones with the same average connectivity degree. For what concerns the role of perception, we find that the knowledge of the infection level in one's own neighborhood is the most e ective property in stopping the spreading of a disease, but at the same time the more expensive one in terms of the quantity of required information, thus the cost/e ectiveness optimum is a trade of etween several parameters. Key words: risk perception, SIS model, complex networks
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""Influenza recurs seasonally in temperate regions of the world; however, our ability to predict the timing, duration, and magnitude of local seasonal outbreaks of influenza remains limited. Here we develop a framework for initializing real-time forecasts of seasonal influenza outbreaks, using a data assimilation technique commonly applied in numerical weather prediction. The availability of realtime, web-based estimates of local influenza infection rates makes this type of quantitative forecasting possible. Retrospective ensemble forecasts are generated on a weekly basis following assimilation of these web-based estimates for the 2003–2008 influenza seasons in New York City. The findings indicate that real-time skillful predictions of peak timing can be made more than 7 wk in advance of the actual peak. In addition, confidence in those predictions can be inferred from the spread of the forecast ensemble. This work represents an initial step in the development of a statistically rigorous system for real-time forecast of seasonal influenza." ." Delete the scoop?
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