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Washington, Feb 18 (IANS) Predicting how pandemics spread has become a huge challenge, as they no longer follow centuries-old patterns when town after town fell prey. Instead, diseases spread seemingly at random, and sometimes spread like wildfire, precipitated by the interactions of three billion air travellers per year. A new computational model shows promise in throwing light on the spread of pandemics. Dirk Brockmann, associate professor of engineering sciences and applied mathematics at the Northwestern University's McCormick School of Engineering and Applied Science, uses transportation data to develop models that better locate the source of an outbreak and help determine how a disease could spread. The new tool would give governments and clinicians an important -- and potentially lifesaving -- advantage in responding to the disease, but current prediction models are limited. Previous pandemic models have been based on geographical distance, but geography provides an incomplete picture of a pandemic, according to a Northwestern statement. For instance, New York City and London are geographically very far apart, but with approximately 10,000 people travelling between the cities daily, the cities are far more connected than, for instance, New York City and Milwaukee, which are geographically closer. Using network theory and official transportation data, Brockmann developed a model that can accurately generate the origin of an outbreak and the predicted arrival times of a pandemic in specific locations. The model can generate these findings using only data about the geographical location and number of occurrences of the disease. "Spatial disease dynamics become far more straightforward when viewed from the right perspective using our technique," Brockmann said. These findings were presented at the 2013 American Association for the Advancement of Science (AAAS) annual meeting in Boston.
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