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A dynamic vaccination strategy to suppress the recurrent epidemic outbreaks

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  • Chen, Dandan
  • Zheng, Muhua
  • Zhao, Ming
  • Zhang, Yu

Abstract

Efficient vaccination strategy is crucial for controlling recurrent epidemic spreading on networks. In this paper, based on the analysis of real epidemic data and simulations, it’s found that the risk indicator of recurrent epidemic outbreaks could be determined by the ratio of the epidemic infection rate of the year to the average infected density of the former year. According to the risk indicator, the dynamic vaccination probability of each year can be designed to suppress the epidemic outbreaks. Our simulation results show that the dynamic vaccination strategy could effectively decrease the maximal and average infected density, and meanwhile increase the time intervals of epidemic outbreaks and individuals attacked by epidemic. In addition, our results indicate that to depress the influenza outbreaks, it is not necessary to keep the vaccination probability high every year; and adjusting the vaccination probability at right time could decrease the outbreak risks with lower costs. Our findings may present a theoretical guidance for the government and the public to control the recurrent epidemic outbreaks.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:chsofr:v:113:y:2018:i:c:p:108-114
    DOI: 10.1016/j.chaos.2018.04.026
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    1. Xing, Yi & Song, Lipeng & Sun, Gui-Quan & Jin, Zhen & Zhang, Juan, 2017. "Assessing reappearance factors of H7N9 avian influenza in China," Applied Mathematics and Computation, Elsevier, vol. 309(C), pages 192-204.
    2. Shen, Chen & Lu, Jun & Shi, Lei, 2016. "Does coevolution setup promote cooperation in spatial prisoner's dilemma game?," Applied Mathematics and Computation, Elsevier, vol. 290(C), pages 201-207.
    3. J. Gómez-Gardeñes & P. Echenique & Y. Moreno, 2006. "Immunization of real complex communication networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 49(2), pages 259-264, January.
    4. Li, Li, 2015. "Patch invasion in a spatial epidemic model," Applied Mathematics and Computation, Elsevier, vol. 258(C), pages 342-349.
    5. Marcel Salathé & James H Jones, 2010. "Dynamics and Control of Diseases in Networks with Community Structure," PLOS Computational Biology, Public Library of Science, vol. 6(4), pages 1-11, April.
    6. 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.
    7. Zheng, Muhua & Wang, Wei & Tang, Ming & Zhou, Jie & Boccaletti, S. & Liu, Zonghua, 2018. "Multiple peaks patterns of epidemic spreading in multi-layer networks," Chaos, Solitons & Fractals, Elsevier, vol. 107(C), pages 135-142.
    8. Flaviano Morone & Hernán A. Makse, 2015. "Influence maximization in complex networks through optimal percolation," Nature, Nature, vol. 524(7563), pages 65-68, August.
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    Cited by:

    1. Kejriwal, Saransh & Sheth, Sarjan & Silpa, P.S. & Sarkar, Sumit & Guha, Apratim, 2022. "Attaining herd immunity to a new infectious disease through multi-stage policies incentivising voluntary vaccination," Chaos, Solitons & Fractals, Elsevier, vol. 154(C).

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