Network Science

  • Network Science / Volume 2 / Issue 01 / April 2014, pp 132-137
  • Copyright © Cambridge University Press 2014 The online version of this article is published within an Open Access environment subject to the conditions of the Creative Commons Attribution licence
  • DOI: http://dx.doi.org/10.1017/nws.2014.5 (About DOI), Published online: 10 April 2014
  • OPEN ACCESS

End Note

The infection tree of global epidemics

ANA PASTORE Y PIONTTIa1, MARCELO FERREIRA DA COSTA GOMESa1  id1, NICOLE SAMAYa1, NICOLA PERRAa1 and ALESSANDRO VESPIGNANIa1

a1 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)

id1     http://orcid.org/0000-0003-4693-5402

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.

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