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

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  • Christos Nicolaides
  • Luis Cueto-Felgueroso
  • Marta C González
  • Ruben Juanes

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.

Suggested Citation

  • Christos Nicolaides & Luis Cueto-Felgueroso & Marta C González & Ruben Juanes, 2012. "A Metric of Influential Spreading during Contagion Dynamics through the Air Transportation Network," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-10, July.
    • Handle: RePEc:plo:pone00:0040961
    DOI: 10.1371/journal.pone.0040961
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    References listed on IDEAS

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    1. Jukka-Pekka Onnela & Samuel Arbesman & Marta C González & Albert-László Barabási & Nicholas A Christakis, 2011. "Geographic Constraints on Social Network Groups," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-7, April.
    2. Paolo Bajardi & Chiara Poletto & Jose J Ramasco & Michele Tizzoni & Vittoria Colizza & Alessandro Vespignani, 2011. "Human Mobility Networks, Travel Restrictions, and the Global Spread of 2009 H1N1 Pandemic," PLOS ONE, Public Library of Science, vol. 6(1), pages 1-8, January.
    3. D. Brockmann & L. Hufnagel & T. Geisel, 2006. "The scaling laws of human travel," Nature, Nature, vol. 439(7075), pages 462-465, January.
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    Cited by:

    1. Chung, Lap Hang, 2015. "Impact of pandemic control over airport economics: Reconciling public health with airport business through a streamlined approach in pandemic control," Journal of Air Transport Management, Elsevier, vol. 44, pages 42-53.
    2. B. V. Nikitin & N. Yu. Zamyatina, 2023. "Waves of the COVID-19 Pandemic in Russia: Regional Projection," Regional Research of Russia, Springer, vol. 13(2), pages 271-286, June.
    3. Dube, Kaitano & Nhamo, Godwell & Chikodzi, David, 2021. "COVID-19 pandemic and prospects for recovery of the global aviation industry," Journal of Air Transport Management, Elsevier, vol. 92(C).
    4. Christos Ellinas & Christos Nicolaides & Naoki Masuda, 2022. "Mitigation strategies against cascading failures within a project activity network," Journal of Computational Social Science, Springer, vol. 5(1), pages 383-400, May.
    5. Christos Nicolaides & Demetris Avraam & Luis Cueto‐Felgueroso & Marta C. González & Ruben Juanes, 2020. "Hand‐Hygiene Mitigation Strategies Against Global Disease Spreading through the Air Transportation Network," Risk Analysis, John Wiley & Sons, vol. 40(4), pages 723-740, April.

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