Hostname: page-component-8448b6f56d-dnltx Total loading time: 0 Render date: 2024-04-18T01:34:48.306Z Has data issue: false hasContentIssue false
  • English
  • Français

The infection tree of global epidemics

Published online by Cambridge University Press:  10 April 2014

ANA PASTORE Y PIONTTI ,
MARCELO FERREIRA DA COSTA GOMES Open the ORCID record for MARCELO FERREIRA DA COSTA GOMES [Opens in a new window] ,
NICOLE SAMAY ,
NICOLA PERRA  and
ALESSANDRO VESPIGNANI
ANA PASTORE Y PIONTTI
Affiliation:
Laboratory for the Modeling of Biological and Socio-Technical Systems, Northeastern University, Boston, MA 02115, USA (e-mails: anapastore@gmail.com, marfcg@gmail.com, nsamay@gmail.com, nicolaperra@gmail.com, alexves@gmail.com)
MARCELO FERREIRA DA COSTA GOMES
Affiliation:
Laboratory for the Modeling of Biological and Socio-Technical Systems, Northeastern University, Boston, MA 02115, USA (e-mails: anapastore@gmail.com, marfcg@gmail.com, nsamay@gmail.com, nicolaperra@gmail.com, alexves@gmail.com)
NICOLE SAMAY
Affiliation:
Laboratory for the Modeling of Biological and Socio-Technical Systems, Northeastern University, Boston, MA 02115, USA (e-mails: anapastore@gmail.com, marfcg@gmail.com, nsamay@gmail.com, nicolaperra@gmail.com, alexves@gmail.com)
NICOLA PERRA
Affiliation:
Laboratory for the Modeling of Biological and Socio-Technical Systems, Northeastern University, Boston, MA 02115, USA (e-mails: anapastore@gmail.com, marfcg@gmail.com, nsamay@gmail.com, nicolaperra@gmail.com, alexves@gmail.com)
ALESSANDRO VESPIGNANI
Affiliation:
Laboratory for the Modeling of Biological and Socio-Technical Systems, Northeastern University, Boston, MA 02115, USA (e-mails: anapastore@gmail.com, marfcg@gmail.com, nsamay@gmail.com, nicolaperra@gmail.com, alexves@gmail.com)
Rights & Permissions [Opens in a new window]

Extract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The spreading of transmissible infectious diseases is inevitably entangled with the dynamics of human population. Humans are the carrier of the pathogen, and the large-scale travel and commuting patterns that govern the mobility of modern societies are defining how epidemics and pandemics travel across the world. For a long time, the development of quantitative spatially explicit models able to shed light on the global dynamics of pandemic has been limited by the lack of detailed data on human mobility. In the last 10 years, however, these limits have been lifted by the increasing availability of data generated by new information technologies, thus triggering the development of computational (microsimulation) models working at a level of single individuals in spatially extended regions of the world. Microsimulations can provide information at very detailed spatial resolutions and down to the level of single individuals. In addition, computational implementations explicitly account for stochasticity, allowing the study of multiple realizations of epidemics with the same parameters' distribution. While on the one hand these capabilities represent the richness of microsimulation methods, on the other hand they face us with a huge amount of information that requires the use of specific data reduction methods and visual analytics.

Type
End Note
Information
Network Science , Volume 2 , Issue 1 , April 2014 , pp. 132 - 137
Creative Commons
Creative Common License - CCCreative Common License - BY
The online version of this article is published within an Open Access environment subject to the conditions of the Creative Commons Attribution licence http://creativecommons.org/licenses/by/3.0/
Copyright
Copyright © Cambridge University Press 2014

References

Balcan, D., Colizza, V., Goncalves, B., Hu, H., Ramasco, J. J., & Vespignani, A. (2009). Multiscale mobility networks and the spatial spreading of infectious diseases. Proceedings of the National Academy of Sciences of the United States of America (PNAS), 106 (51), 21484–21489, doi:10.1073/pnas.0906910106.Google Scholar
Balcan, D., Goncalves, B., Hu, H., Ramasco, J. J., Colizza, V., & Vespignani, A. (2010). Modeling the spatial spread of infectious diseases: The global epidemic and mobility computational model. Journal of Computational Science, 1, 132–145.Google Scholar
Balcan, D., Hu, H., Goncalves, B., Bajardi, P., Poletto, C., Ramasco, J. J., Vespignani, A. (2009). Seasonal transmission potential and activity peaks of the new influenza A(H1N1): A Monte Carlo likelihood analysis based on human mobility. BMC Medicine, 7, 45, doi:10.1186/1741-7015-7-45.Google Scholar
Brockmann, D., & Helbing, D. (2014). The hidden geometry of complex, network-driven contagion phenomena. Science, 342, 1337–1342.Google Scholar
Chu, Y. J., & Liu, T. H. (1965), On the shortest arborescence of a directed graph. Science Sinica, 14, 1396–1400.Google Scholar
Edmonds, J. (1976). Optimum branchings. Journal Research of the National Bureau of Standards, 71B, 233–240.Google Scholar
Eggo, R. M., Cauchemez, S., & Ferguson, N. M. (2011). Spatial dynamics of the 1918 influenza pandemic in England, Wales and the United States. Journal of the Royal Society Interface, 8, 233–243.Google Scholar
Serrano, M. A., Boguna, M., & Vespignani, A. (2009). Extracting the multiscale backbone of complex weighted networks. Proceedings of the National Academy of Sciences of the United States of America (PNAS), 106, 6483–6488.Google Scholar
Tizzoni, M., Bajardi, P., Poletto, C., Ramasco, J. J., Balcan, D., Goncalves, B., Vespignani, A. (2012), Real-time numerical forecast of global epidemic spreading: Case study of 2009 A/H1N1pdm. BMC Medicine, 10, 165, doi:10.1186/1741-7015-10-165.Google Scholar