A Metric of Influential Spreading during Contagion Dynamics through the Air Transportation Network

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, ge...

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Bibliographic Details
Main Authors: Nicolaides, Christos, Cueto-Felgueroso, Luis, Gonzalez, Marta C., Juanes, Ruben
Other Authors: Massachusetts Institute of Technology. Center for Computational Engineering
Format: Article
Language:en_US
Published: Public Library of Science 2012
Online Access:http://hdl.handle.net/1721.1/72352
https://orcid.org/0000-0002-7370-2332
https://orcid.org/0000-0002-8482-0318
https://orcid.org/0000-0002-1485-2736
https://orcid.org/0000-0003-3958-7382
Description
Summary: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.