Northwestern professor uses transportation data to create pandemic computational model

A Northwestern University associate professor recently developed a computational model using transportation data to better pinpoint the source of an outbreak and determine how the disease could spread.

Dirk Brockmann, an associate professor of engineering sciences and applied mathematics, discussed his research on Saturday in a presentation called "Are Pandemics Predictable?" in Boston at the annual meeting of the American Association for the Advancement in Science. The new model could help health officials to better learn the location of a pandemic outbreak or bioterrorism attack.

Current pandemic models are based on geographical distance, which provides an incomplete picture of a pandemic. While New York City and London are geographically far apart, the approximately 10,000 people that travel between the cities each day make the locations more connected than New York City and Milwaukee, which are closer geographically.

"Furthermore, cities with a very high level of connectedness, such as London, are important epicenters for tracking the spread of diseases," Brockmann said. "When a disease reaches these cities, it is likely to spread far and quickly."

Brockmann used official transportation data and network theory to develop a model that accurately generates the origin of an outbreak and the predicted arrival times of a pandemic in specific locations. The system only requires data about the geographical location and the number of cases of the disease.

"Spatial disease dynamics become far more straightforward when viewed from the right perspective using our technique," Brockmann said.

Brockmann's presentation was part of a symposium titled "Predictability: From Physical to Data Sciences."