• Institution: Staats- und Universitaetsbibliothek Bremen
  • Read news stories, core concepts, and more through the PNAS Front Matter Portal
  • Sign up for PNAS eTOC alerts

The role of the airline transportation network in the prediction and predictability of global epidemics

  1. Vittoria Colizza *,
  2. Alain Barrat ,
  3. Marc Barthélemy * , , and
  4. Alessandro Vespignani * , §
  1. Communicated by Giorgio Parisi, University of Rome, Rome, Italy, December 8, 2005 (received for review June 18, 2005)

Abstract

The systematic study of large-scale networks has unveiled the ubiquitous presence of connectivity patterns characterized by large-scale heterogeneities and unbounded statistical fluctuations. These features affect dramatically the behavior of the diffusion processes occurring on networks, determining the ensuing statistical properties of their evolution pattern and dynamics. In this article, we present a stochastic computational framework for the forecast of global epidemics that considers the complete worldwide air travel infrastructure complemented with census population data. We address two basic issues in global epidemic modeling: (i) we study the role of the large scale properties of the airline transportation network in determining the global diffusion pattern of emerging diseases; and (ii) we evaluate the reliability of forecasts and outbreak scenarios with respect to the intrinsic stochasticity of disease transmission and traffic flows. To address these issues we define a set of quantitative measures able to characterize the level of heterogeneity and predictability of the epidemic pattern. These measures may be used for the analysis of containment policies and epidemic risk assessment.

Footnotes

  • §To whom correspondence should be addressed. E-mail: alexv@indiana.edu
  • On leave from: Departement de Physique Théorique et Appliquée BP12, Commissariat à l’Energie Atomique, 91680 Bruyères-le-Chatel, France.

  • Author contributions: V.C., A.B., M.B., and A.V. designed research, performed research, contributed new reagents/analytic tools, analyzed data, and wrote the paper.

  • Conflict of interest statement: No conflicts declared.

  • Abbreviations:

    Abbreviations:

    SIR,
    susceptible–infected–removed.

HighWire Press-hosted articles citing this article