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How the heterogeneous infection rate effect on the epidemic spreading in activity-driven network

Author

Listed:
  • Dun Han

    (Nonlinear Scientific Research Center, Jiangsu University, Zhenjiang, Jiangsu 212013, P. R. China)

  • Dandan Li

    (School of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 210029, P. R. China)

  • Chao Chen

    (Nonlinear Scientific Research Center, Jiangsu University, Zhenjiang, Jiangsu 212013, P. R. China)

  • Mei Sun

    (Nonlinear Scientific Research Center, Jiangsu University, Zhenjiang, Jiangsu 212013, P. R. China)

Abstract

In this paper, we research the impact of the heterogeneous infection rate on the epidemic spread in the activity-driven networks. By using the mean field approximation, the epidemic threshold is theoretically obtained. Several immunization strategies that could curb the epidemic spread are presented. Based on the theoretical analysis and simulation results, we obtain that the epidemic would be prevented effectively if the infection rate strongly correlates with the individual activity. However, if infection rate has a little correlation with the individual activity, most of the individuals may be infected. In addition, the epidemic can be suppressed soon if the individuals with high activity are immunized preferentially.

Suggested Citation

  • Dun Han & Dandan Li & Chao Chen & Mei Sun, 2016. "How the heterogeneous infection rate effect on the epidemic spreading in activity-driven network," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 27(06), pages 1-12, June.
    • Handle: RePEc:wsi:ijmpcx:v:27:y:2016:i:06:n:s0129183116500571
    DOI: 10.1142/S0129183116500571
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    Cited by:

    1. Li, Dandan & Ma, Jing, 2017. "How the government’s punishment and individual’s sensitivity affect the rumor spreading in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 284-292.
    2. Ding, Hong & Xu, Jia-Hao & Wang, Zhen & Ren, Yi-Zhi & Cui, Guang-Hai, 2018. "Subsidy strategy based on history information can stimulate voluntary vaccination behaviors on seasonal diseases," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 390-399.
    3. Hu, Yuhan & Pan, Qiuhui & Hou, Wenbing & He, Mingfeng, 2018. "Rumor spreading model with the different attitudes towards rumors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 331-344.

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