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This research used resources of the Argonne Leadership Computing Facility at Argonne National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under contract DE-AC02-06CH11357.

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The research group of Alessandro Vespignani at Indiana University is also using mathematical models to forecast the world-wide spread of the H1N1 flu.

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Team

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From left to right, top: Christian Thiemann, Alejandro Morales Gallardo, Rafael Brune, Dirk Brockmann. Bottom: Vincent David, Olivia Woolley, Daniel Grady.

Related Links

page2_sidebar_2 CDC H1N1 Flu Website
page2_sidebar_2 WHO Influenza A(H1N1) Website
page2_sidebar_2 Illinois Dept. of Public Health

Yahoo! News: 'Worst Case' Scenario for Flu Estimated (May 1, 2009)
Welt am Sonntag: Kleine Geschichte der großen Unsicherheit (May 3, 2009 – German)
WBEZ Chicago Public Radio: Eight Forty-Eight (Apr 29, 2009)
WBEZ Chicago Public Radio (Apr 28, 2009)
ABC7 Chicago (Apr 30, 2009)
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FOX News Chicago (Apr 27, 2009)
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Our Model

Multiscale human travel network obtained by observing the flux of dollar bills in the US.
In our simulations for the spread in the United States we take Mexico City to be the starting point of the infection. The initial conditions of the model are chosen to match the available information on confirmed cases in the United States and Mexico, and we permit for a bracket of unconfirmed cases. We take into account the travel of individuals between counties in the United States and the incoming flux of airline passengers from Mexico City. We consider a worst-case scenario based on assumptions made from the information we have gathered thus far. The key factors in our modeling approach are very accurate human mobility datasets on scales from a few to a few thousand kilometers. We obtained the underlying multi-scale human mobility network indirectly by our recent investigation on the geographic circulation of dollar bills in the United States, which is an excellent proxy for human mobility and includes small scale daily commuting traffic, intermediate traffic, and long distance travel by air. Our simulations consist of multiple layers, each layer possessing and increasing degree of accuracy and complexity. Our final projections are done with a fully stochastic model that incorporates the inherent randomness in disease dynamics that is particularly important at the onset of an epidemic when the number of infected individuals is small compared to the whole population.