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In BMC Medicine: modeling the spread of infection

In BMC Medicine: modeling the spread of infection | FuturICT Journal Publications | Scoop.it

Research published in BMC Medicine this week has been centered around flu modeling. As part of a large international collaboration, Alessandro Vespignani and colleagues validated the Global Epidemic and Mobility Model, a computational model for the spread of disease, using surveillance data from the 2009 flu pandemic. The authors showed that computational forecasts of flu spread using this model are in good agreement with real-life data. They conclude that the Global Epidemic and Mobility Model could be used to predict the spread of disease during a pandemic, so long as high quality data are used to build the model.

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Quantifying the Behavior of Stock Correlations Under Market Stress: Tobias Preis, Helen Susannah Moat, H. Eugene Stanley & Steven R. Bishop

Understanding correlations in complex systems is crucial in the face of turbulence, such as the ongoing financial crisis. However, in complex systems, such as financial systems, correlations are not constant but instead vary in time. Here we address the question of quantifying state-dependent correlations in stock markets. Reliable estimates of correlations are absolutely necessary to protect a portfolio. We analyze 72 years of daily closing prices of the 30 stocks forming the Dow Jones Industrial Average (DJIA). We find the striking result that the average correlation among these stocks scales linearly with market stress reflected by

normalized DJIA index returns on various time scales. Consequently, the diversification effect which should protect a portfolio melts away in times of market losses, just when it would most urgently be needed. Our empirical analysis is consistent with the interesting possibility that one could anticipate diversification breakdowns, guiding the design of protected portfolios.

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