New study of disease contagion ranks U.S. airports in terms of their spreading influence
July 23, 2012
By Denise Brehm
Civil & Environmental Engineering
Public health crises of the past decade — such as the 2003 SARS outbreak, which spread to 37 countries and caused about 1,000 deaths, and the 2009 H1N1 flu pandemic that killed about 300,000 people worldwide — have heightened awareness that new viruses or bacteria could spread quickly across the globe, aided by air travel.
While epidemiologists and scientists who study complex network systems — such as contagion patterns and information spread in social networks — are working to create mathematical models that describe the worldwide spread of disease, to date these models have focused on the final stages of epidemics, examining the locations that ultimately develop the highest infection rates
But a new study by researchers in MIT's Department of Civil and Environmental Engineering (CEE) shifts the focus to the first few days of an epidemic, determining how likely the 40 largest U.S. airports are to influence the spread of a contagious disease originating in their home cities. This new approach could help determine appropriate measures for containing infection in specific geographic areas and aid public health officials in making decisions about the distribution of vaccinations or treatments in the earliest days of contagion.
Unlike existing models, the new MIT model incorporates variations in travel patterns among individuals, the geographic locations of airports, the disparity in interactions among airports, and waiting times at individual airports to create a tool that could be used to predict where and how fast a disease might spread.
"Our work is the first to look at the spatial spreading of contagion processes at early times, and to propose a predictor for which 'nodes' — in this case, airports — will lead to more aggressive spatial spreading," says Ruben Juanes, the ARCO Associate Professor in Energy Studies in CEE. "The findings could form the basis for an initial evaluation of vaccine allocation strategies in the event of an outbreak, and could inform national security agencies of the most vulnerable pathways for biological attacks in a densely connected world."
A more realistic model
Juanes' studies of the flow of fluids through fracture networks in subsurface rock and the research of CEE's Marta González, who uses cellphone data to model human mobility patterns and trace contagion processes in social networks, laid the basis for determining individual travel patterns among airports in the new study. Existing models typically assume a random, homogenous diffusion of travelers from one airport to the next.
However, people don't travel randomly; they tend to create patterns that can be replicated. Using González's work on human mobility patterns, Juanes and his research group — including graduate student Christos Nicolaides and research associate Luis Cueto-Felgueroso — applied Monte Carlo simulations to determine the likelihood of any single traveler flying from one airport to another.
"The results from our model are very different from those of a conventional model that relies on the random diffusion of travelers … [and] similar to the advective flow of fluids," says Nicolaides, first author of a paper by the four MIT researchers that was published in the journal PLoS ONE. "The advective transport process relies on distinctive properties of the substance that's moving, as opposed to diffusion, which assumes a random flow. If you include diffusion only in the model, the biggest airport h