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

Abstract

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.

Authors:
 [1];  [1];  [1];  [1];  [1]
  1. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Publication Date:
Research Org.:
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1557420
Grant/Contract Number:  
SC0003907
Resource Type:
Accepted Manuscript
Journal Name:
PLoS ONE
Additional Journal Information:
Journal Volume: 7; Journal Issue: 7; Journal ID: ISSN 1932-6203
Publisher:
Public Library of Science
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING

Citation Formats

Nicolaides, Christos, Cueto-Felgueroso, Luis, González, Marta C., Juanes, Ruben, and Vespignani, Alessandro. A Metric of Influential Spreading during Contagion Dynamics through the Air Transportation Network. United States: N. p., 2012. Web. doi:10.1371/journal.pone.0040961.
Nicolaides, Christos, Cueto-Felgueroso, Luis, González, Marta C., Juanes, Ruben, & Vespignani, Alessandro. A Metric of Influential Spreading during Contagion Dynamics through the Air Transportation Network. United States. doi:10.1371/journal.pone.0040961.
Nicolaides, Christos, Cueto-Felgueroso, Luis, González, Marta C., Juanes, Ruben, and Vespignani, Alessandro. Thu . "A Metric of Influential Spreading during Contagion Dynamics through the Air Transportation Network". United States. doi:10.1371/journal.pone.0040961. https://www.osti.gov/servlets/purl/1557420.
@article{osti_1557420,
title = {A Metric of Influential Spreading during Contagion Dynamics through the Air Transportation Network},
author = {Nicolaides, Christos and Cueto-Felgueroso, Luis and González, Marta C. and Juanes, Ruben and Vespignani, Alessandro},
abstractNote = {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.},
doi = {10.1371/journal.pone.0040961},
journal = {PLoS ONE},
number = 7,
volume = 7,
place = {United States},
year = {2012},
month = {7}
}

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Cited by: 22 works
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