The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
The role of the airline transportation network in the prediction and predictability of global epidemics
Open accessAbstract
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.
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Cites
Dorogovtsev, S. N., & Mendes, J. F. F. (2003) · doi:10.1093/acprof:oso/9780198515906.001.0001 · Cited by 1,563
The effects of local spatial structure on epidemiological invasions
Keeling, M. J. (1999) · Proceedings of the Royal Society of London. Series B: Biological Sciences · doi:10.1098/rspb.1999.0716 · Cited by 798
Network theory and SARS: predicting outbreak diversity
Meyers, L. A., Pourbohloul, B., Newman, M. E. J., Skowronski, D. M., & Brunham, R. C. (2005) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2004.07.026 · Cited by 705
How Viruses Spread Among Computers and People
Lloyd, A. L., & May, R. M. (2001) · Science · doi:10.1126/science.1061076 · Cited by 661
Cited by
Epidemic processes in complex networks
Pastor-Satorras, R., Castellano, C., Van Mieghem, P., & Vespignani, A. (2015) · Reviews of Modern Physics · doi:10.1103/revmodphys.87.925 · Cited by 3,697
Self-citationLazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., et al. (2009) · Science · doi:10.1126/science.1167742 · Cited by 3,365
The Hidden Geometry of Complex, Network-Driven Contagion Phenomena
Brockmann, D., & Helbing, D. (2013) · Science · doi:10.1126/science.1245200 · Cited by 1,253
Population flow drives spatio-temporal distribution of COVID-19 in China
Jia, J. S., Lu, X., Yuan, Y., Xu, G., Jia, J., & Christakis, N. A. (2020) · Nature · doi:10.1038/s41586-020-2284-y · Cited by 835
Human Mobility Networks, Travel Restrictions, and the Global Spread of 2009 H1N1 Pandemic
Bajardi, P., Poletto, C., Ramasco, J. J., Tizzoni, M., Colizza, V., & Vespignani, A. (2011) · PLoS ONE · doi:10.1371/journal.pone.0016591 · Cited by 495
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Metadata
- Authors
- Colizza, V., Barrat, A., Barthélemy, M., & Vespignani, A.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2006-02-03
- Volume
- 103
- Issue
- 7
- Pages
- 2015-2020
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2043890595
- Cited by
- 1,155
- References
- 4
- FWCI
- 26.70
- Type
- journal article
- Language
- en
- Open access
- bronze
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