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Editor, Senior Moderator
Citation: Nicolaides C, Cueto-Felgueroso L, Gonz?lez MC, Juanes R (2012) A Metric of Influential Spreading during Contagion Dynamics through the Air Transportation Network. PLoS ONE 7(7): e40961. doi:10.1371/journal.pone.0040961
Christos Nicolaides1, Luis Cueto-Felgueroso1, Marta C. Gonz?lez1,2, Ruben Juanes1,3*
1 Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America, 2 Engineering Sciences Division, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America, 3 Center for Computational Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
Abstract Top
The spread of infectious diseases at the global scale is mediated by long-range human travel. Our ability to predict the impact of an outbreak on human health requires understanding the spatiotemporal signature of early-time spreading from a specific location. Here, we show that network topology, geography, traffic structure and individual mobility patterns are all essential for accurate predictions of disease spreading. Specifically, we study contagion dynamics through the air transportation network by means of a stochastic agent-tracking model that accounts for the spatial distribution of airports, detailed air traffic and the correlated nature of mobility patterns and waiting-time distributions of individual agents. From the simulation results and the empirical air-travel data, we formulate a metric of influential spreading??the geographic spreading centrality??which accounts for spatial organization and the hierarchical structure of the network traffic, and provides an accurate measure of the early-time spreading power of individual nodes.
http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040961
Christos Nicolaides1, Luis Cueto-Felgueroso1, Marta C. Gonz?lez1,2, Ruben Juanes1,3*
1 Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America, 2 Engineering Sciences Division, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America, 3 Center for Computational Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
Abstract Top
The spread of infectious diseases at the global scale is mediated by long-range human travel. Our ability to predict the impact of an outbreak on human health requires understanding the spatiotemporal signature of early-time spreading from a specific location. Here, we show that network topology, geography, traffic structure and individual mobility patterns are all essential for accurate predictions of disease spreading. Specifically, we study contagion dynamics through the air transportation network by means of a stochastic agent-tracking model that accounts for the spatial distribution of airports, detailed air traffic and the correlated nature of mobility patterns and waiting-time distributions of individual agents. From the simulation results and the empirical air-travel data, we formulate a metric of influential spreading??the geographic spreading centrality??which accounts for spatial organization and the hierarchical structure of the network traffic, and provides an accurate measure of the early-time spreading power of individual nodes.
http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040961