Air-Transportation Networks and the Spatial Spreading of Infectious Diseases

Article Preview

Abstract:

Many studies suggest that air-transportation networks contribute a lot to the spatiotemporal dynamics of infectious diseases. The mobility of individuals over the networks has greatly speeded up the spreading processes of the epidemics and pushed the population in non-epidemic areas into the risk of infection. To figure out the underlying interactions between the air-transportation networks and the transmission of the epidemics, we have (i) analyzed the air-routes and the statistical data on the passenger throughput of the civil aviation of China and (ii) carried out a computer simulation based on the assumption that a novel influenza outbreaks in Southeast Asia. The results show that the topology of the air-transportations networks has a typical structure of heterogeneities. We also find that the epidemics will soon strike China after the initial outbreaks and rapidly spread throughout the whole networks without air-travel restrictions even the reproductive number () is small.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

4020-4024

Citation:

Online since:

February 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] R. F. Grais, J. H. Ellis, and G. E. Glass, Assessing the impact of airline travel on the geographic spread of pandemic influenza, European journal of epidemiology, vol. 18, pp.1065-1072, (2003).

Google Scholar

[2] R. Grais, J. H. Ellis, A. Kress, and G. Glass, Modeling the spread of annual influenza epidemics in the US: The potential role of air travel, Health Care Management Science, vol. 7, pp.127-134, (2004).

DOI: 10.1023/b:hcms.0000020652.38181.da

Google Scholar

[3] V. Colizza, A. Barrat, M. Barthélemy, and A. Vespignani, The role of the airline transportation network in the prediction and predictability of global epidemics, Proceedings of the National Academy of Sciences of the United States of America, vol. 103, pp.2015-2020, (2006).

DOI: 10.1073/pnas.0510525103

Google Scholar

[4] J. M. Epstein, D. M. Goedecke, F. Yu, R. J. Morris, D. K. Wagener, and G. V. Bobashev, Controlling pandemic flu: the value of international air travel restrictions, PLoS One, vol. 2, p. e401, (2007).

DOI: 10.1371/journal.pone.0000401

Google Scholar

[5] D. Balcan, V. Colizza, B. Gonçalves, H. Hu, J. J. Ramasco, and A. Vespignani, Multiscale mobility networks and the spatial spreading of infectious diseases, Proceedings of the National Academy of Sciences, vol. 106, pp.21484-21489, (2009).

DOI: 10.1073/pnas.0906910106

Google Scholar

[6] M. J. Keeling and P. Rohani, Modeling infectious diseases in humans and animals: Princeton University Press, (2011).

Google Scholar

[7] L. A Rvachev and I. M. Longini Jr, A mathematical model for the global spread of influenza, Mathematical Biosciences, vol. 75, pp.3-22, (1985).

DOI: 10.1016/0025-5564(85)90063-x

Google Scholar

[8] I. M. Longini, M. E. Halloran, A. Nizam, and Y. Yang, Containing pandemic influenza with antiviral agents, American Journal of Epidemiology, vol. 159, pp.623-633, (2004).

DOI: 10.1093/aje/kwh092

Google Scholar

[9] L. R. Elveback, J. P. Fox, E. Ackerman, A. Langworthy, M. Boyd, and L. Gatewood, An influenza simualtion model for immunization studies, American Journal of Epidemiology, vol. 103, pp.152-165, (1976).

DOI: 10.1093/oxfordjournals.aje.a112213

Google Scholar

[10] S. Cauchemez, F. Carrat, C. Viboud, A. Valleron, and P. Boelle, A Bayesian MCMC approach to study transmission of influenza: application to household longitudinal data, Statistics in medicine, vol. 23, pp.3469-3487, (2004).

DOI: 10.1002/sim.1912

Google Scholar

[11] I. M. Longini, A. Nizam, S. Xu, K. Ungchusak, W. Hanshaoworakul, D. A. Cummings, and M. E. Halloran, Containing pandemic influenza at the source, Science, vol. 309, pp.1083-1087, (2005).

DOI: 10.1126/science.1115717

Google Scholar