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Rapid disease spread on dense networks with power-law topology

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Listed:
  • J. J. Esquivel-Gómez

    (Instituto Potosino de Investigación Científica y Tecnológica (IPICYT))

  • J. G. Barajas-Ramírez

    (Instituto Potosino de Investigación Científica y Tecnológica (IPICYT))

Abstract

Models of disease spread in networks typically focus on exploring various measures to reduce the spread of disease across individuals within a network. However, the topology of the underlying network plays an important role in determining the best time to implement mitigation measures to achieve better results. In this article we show the behavior of the well-known SIR (susceptible-infected-removed) and SIS (susceptible-infected-susceptible) models over networks with both scale-free and dense structure with power-law topology $$P(k)\sim k^{-\zeta }$$ P ( k ) ∼ k - ζ with $$1

Suggested Citation

  • J. J. Esquivel-Gómez & J. G. Barajas-Ramírez, 2024. "Rapid disease spread on dense networks with power-law topology," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 97(5), pages 1-10, May.
  • Handle: RePEc:spr:eurphb:v:97:y:2024:i:5:d:10.1140_epjb_s10051-024-00675-7
    DOI: 10.1140/epjb/s10051-024-00675-7
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    References listed on IDEAS

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    1. Huayan Pei & Guanghui Yan & Yaning Huang, 2023. "Impact of contact rate on epidemic spreading in complex networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 96(4), pages 1-7, April.
    2. Xiao-Jie Li & Xiang Li, 2020. "Vaccinating SIS epidemics under evolving perception in heterogeneous networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 93(10), pages 1-7, October.
    3. Yutaka Okabe & Akira Shudo, 2021. "Microscopic Numerical Simulations of Epidemic Models on Networks," Mathematics, MDPI, vol. 9(9), pages 1-19, April.
    4. Zhidong He & Piet Van Mieghem, 2018. "The fastest spreader in SIS epidemics on networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 91(5), pages 1-8, May.
    5. H. Jeong & B. Tombor & R. Albert & Z. N. Oltvai & A.-L. Barabási, 2000. "The large-scale organization of metabolic networks," Nature, Nature, vol. 407(6804), pages 651-654, October.
    6. Nekovee, M. & Moreno, Y. & Bianconi, G. & Marsili, M., 2007. "Theory of rumour spreading in complex social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 374(1), pages 457-470.
    7. Garlaschelli, Diego & Loffredo, Maria I., 2005. "Structure and evolution of the world trade network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 355(1), pages 138-144.
    8. D. Garlaschelli & M. I. Loffredo, 2005. "Structure and Evolution of the World Trade Network," Papers physics/0502066, arXiv.org, revised May 2005.
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