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Suppressing Epidemics with a Limited Amount of Immunization Units

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  • C. M. Schneider
  • T. Mihaljev
  • S. Havlin
  • H. J. Herrmann

Abstract

The way diseases spread through schools, epidemics through countries and viruses through the Internet is crucially determining their risk. Although each of these threats has its own characteristics, its underlying network determines the spreading. To restrain the spreading, a widely used approach is the fragmentation of these networks through immunization, so that epidemics cannot spread. Here we develop a novel immunization approach outperforming the best known strategy, based on immunizing the highest betweenness links or nodes. We find that the network's vulnerability can be significantly reduced demonstrating this on three different real networks: the global flight network, a school friendship network and the Internet. In all cases, we find that not only the average infection probability is significantly suppressed, but also for the most relevant case of a small and limited number of immunization units the infection probability can be reduced by up to 55%.

Suggested Citation

  • C. M. Schneider & T. Mihaljev & S. Havlin & H. J. Herrmann, "undated". "Suppressing Epidemics with a Limited Amount of Immunization Units," Working Papers ETH-RC-12-007, ETH Zurich, Chair of Systems Design.
  • Handle: RePEc:stz:wpaper:eth-rc-12-007
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    References listed on IDEAS

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    1. Réka Albert & Hawoong Jeong & Albert-László Barabási, 2000. "Error and attack tolerance of complex networks," Nature, Nature, vol. 406(6794), pages 378-382, July.
    2. Vittoria Colizza & Alain Barrat & Marc Barthelemy & Alain-Jacques Valleron & Alessandro Vespignani, 2007. "Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions," PLOS Medicine, Public Library of Science, vol. 4(1), pages 1-16, January.
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    Citations

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    Cited by:

    1. Bouveret, Géraldine & Mandel, Antoine, 2021. "Social interactions and the prophylaxis of SI epidemics on networks," Journal of Mathematical Economics, Elsevier, vol. 93(C).
    2. Yuan, Peiyan & Tang, Shaojie, 2015. "Community-based immunization in opportunistic social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 85-97.
    3. Geraldine Bouveret & Antoine Mandel, 2020. "Prophylaxis of Epidemic Spreading with Transient Dynamics," Papers 2007.07580, arXiv.org.
    4. Samuel F Rosenblatt & Jeffrey A Smith & G Robin Gauthier & Laurent Hébert-Dufresne, 2020. "Immunization strategies in networks with missing data," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-21, July.
    5. Teruyoshi Kobayashi & Kohei Hasui, 2013. "Efficient immunization strategies to prevent financial contagion," Papers 1308.0652, arXiv.org, revised Dec 2013.
    6. Xia, Ling-Ling & Song, Yu-Rong & Li, Chan-Chan & Jiang, Guo-Ping, 2018. "Improved targeted immunization strategies based on two rounds of selection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 540-547.
    7. Yu, Yang & Deng, Ye & Tan, Suo-Yi & Wu, Jun, 2018. "Efficient disintegration strategy in directed networks based on tabu search," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 435-442.
    8. Liu, Xiang-Chun & Zhu, Xu-Zhen & Tian, Hui & Zhang, Zeng-Ping & Wang, Wei, 2019. "Identifying localized influential spreaders of information spreading," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 92-97.
    9. Chen, Dandan & Zheng, Muhua & Zhao, Ming & Zhang, Yu, 2018. "A dynamic vaccination strategy to suppress the recurrent epidemic outbreaks," Chaos, Solitons & Fractals, Elsevier, vol. 113(C), pages 108-114.
    10. Shams, Bita & Khansari, Mohammad, 2015. "On the impact of epidemic severity on network immunization algorithms," Theoretical Population Biology, Elsevier, vol. 106(C), pages 83-93.
    11. Olivier Tsemogne & Yezekael Hayel & Charles Kamhoua & Gabriel Deugoue, 2022. "A Partially Observable Stochastic Zero-sum Game for a Network Epidemic Control Problem," Dynamic Games and Applications, Springer, vol. 12(1), pages 82-109, March.
    12. Miclo, Laurent & Weibull, Jörgen W. & Spiro, Daniel, 2020. "Optimal epidemic suppression under an ICU constraint," TSE Working Papers 20-1111, Toulouse School of Economics (TSE).
    13. Laurent Miclo & Daniel Spiro & Jörgen Weibull, 2020. "Optimal epidemic suppression under an ICU constraint ," Working Papers hal-02563023, HAL.

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