In a paper published in today's issue of the journal Nature, Los Alamos researchers Stephen Eubank, Anil Kumar, Madhav Marathe and Zoltan Toroczkai, along with Hasan Guclu of Rensselaer and Aravind Srinvasan and Nan Wang of the University of Maryland present details of a mathematical model based on physical contact patterns resulting from movements of individuals between specific locations.
According to the team leader Eubank, "Over the past century, changes in social structure driven by increased mobility, combined with the emergence of new diseases against which we have no defense, have raised the specter of massive disease outbreaks -- pandemics. Our model shows that available containment strategies can be effective even in the modern urban environment, if they are supported by early detection of outbreaks and quick isolation of infectious people. It also suggests that placing sensors at the potential hubs of any social network -- say in major airports -- provides an efficient way to monitor large populations.
"However, our model shows that the intuitively appealing strategy of isolating people with the most contacts is unlikely to work because, unlike most human-designed networks, the contact network among people is extremely robust to removing hubs," said Eubank.
The disease outbreak model uses a Los Alamos computer modeling system called EpiSims (Epidemiological Simulation System) that employs the urban population mobility data generated by the Laboratory's Transportation Analysis and Simulation System (TRANSIMS) model. The EpiSims model used by the researchers simulates the progress of a disease like smallpox as it is transmitted between hosts and through a simulated population by using actual census and land-use data, along with TRANSIMS' realistic estimates of population mobility and person-to-person, second-by-second interactions.
At the heart of the disease outbreak model is an understanding of the connection between structural properties of networks and the long-term dynamics of interactions among neighbors in those networks. For example, the scale-free types of networks commonly found in natural or organic systems, such as in a cell's metabolic system, behave differently than random or engineered networks like highway systems. Given what now appear to be fundamental differences in networks, it was perhaps not surprising that the disease containment strategies based on previous models might not be the most efficient or effective.
The disease outbreak model is based on TRANSIMS data gathered initially by Los Alamos National Laboratory for the model of Portland, Ore., although the investigators say that the approach is broadly applicable to other cities.
Funding for the modeling research was provided by the United States Department of Energy, the United States Department of Homeland Security and the National Science Foundation.
Los Alamos National Laboratory is operated by the University of California for the National Nuclear Security Administration (NNSA) of the U.S. Department of Energy and works in partnership with NNSA's Sandia and Lawrence Livermore national laboratories to support NNSA in its mission.
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Los Alamos enhances national security by ensuring the safety and reliability of the U.S. nuclear stockpile, developing technologies to reduce threats from weapons of mass destruction, and solving problems related to energy, environment, infrastructure, health, and global security concerns.