Air travel can serve as a handy conduit for contagion—and researchers in MIT’s Department of Civil and Environmental Engineering have developed a computer model that identifies which U.S. airports are best at spreading disease. The study, led by Ruben Juanes, a computational geoscientist at MIT, and recently published in the journal PLoS One, predicts the “footprint of disease”—how far it’s likely to have spread—during the first 14 days of a pandemic. Though the model is still under development, the results have attracted the attention of the International Society for Disease Surveillance and are an important first step toward accurately predicting epidemic patterns in real time. Unlike previous studies that presumed that early spread of contagions would be predicted solely by traffic volume, MIT’s list yielded some surprising results—namely, that bigger isn’t always badder. Here are a few examples.


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