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The infection tree of global epidemics

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  • PASTORE Y PIONTTI, ANA
  • GOMES, MARCELO FERREIRA DA COSTA
  • SAMAY, NICOLE
  • PERRA, NICOLA
  • VESPIGNANI, ALESSANDRO

Abstract

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.

Suggested Citation

  • Pastore Y Piontti, Ana & Gomes, Marcelo Ferreira Da Costa & Samay, Nicole & Perra, Nicola & Vespignani, Alessandro, 2014. "The infection tree of global epidemics," Network Science, Cambridge University Press, vol. 2(1), pages 132-137, April.
  • Handle: RePEc:cup:netsci:v:2:y:2014:i:01:p:132-137_00
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    Cited by:

    1. Tsiligianni, Christiana & Tsiligiannis, Aristeides & Tsiliyannis, Christos, 2023. "A stochastic inventory model of COVID-19 and robust, real-time identification of carriers at large and infection rate via asymptotic laws," European Journal of Operational Research, Elsevier, vol. 304(1), pages 42-56.
    2. Ana Carolina Carioca da Costa & Cláudia Torres Codeço & Elias Teixeira Krainski & Marcelo Ferreira da Costa Gomes & Aline Araújo Nobre, 2018. "Spatiotemporal diffusion of influenza A (H1N1): Starting point and risk factors," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-20, September.

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