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The virus variation model by considering the degree-dependent spreading rate

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  • Han, Dun
  • Sun, Mei
  • Li, Dandan

Abstract

Considering the difference of different individuals’ physical quality and antibody, this paper investigates the epidemic spreading model with the virus mutation. By using the mean-field theory, the epidemic threshold with degree-dependent spreading rate can be theoretical drawn. According to the numerical simulations, we can obtain that the average infected virus version in the BA network is less than the ER network. In addition, if the effective spreading rate is either small or large enough, the average virus version of the whole infected individuals will reduce. However, when the spreading rate takes some proper values, the average infected virus version can greatly increase. Finally, we study how the different initial infected nodes influence the average virus version of the whole infected individuals. The numerical results show that the greater of the initial infected degree, the smaller of the average virus version of the whole infected individuals.

Suggested Citation

  • Han, Dun & Sun, Mei & Li, Dandan, 2015. "The virus variation model by considering the degree-dependent spreading rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 433(C), pages 42-50.
    • Handle: RePEc:eee:phsmap:v:433:y:2015:i:c:p:42-50
    DOI: 10.1016/j.physa.2015.03.077
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    References listed on IDEAS

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    1. Yang, Xu-Hua & Wang, Bo & Chen, Sheng-Yong & Wang, Wan-Liang, 2012. "Epidemic dynamics behavior in some bus transport networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(3), pages 917-924.
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    3. Han, Dun & Sun, Mei, 2014. "Can memory and conformism resolve the vaccination dilemma?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 95-104.
    4. Chen, Jiancong & Zhang, Huiling & Guan, Zhi-Hong & Li, Tao, 2012. "Epidemic spreading on networks with overlapping community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1848-1854.
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    Cited by:

    1. Huang, Yunhan & Ding, Li & Feng, Yun, 2016. "A novel epidemic spreading model with decreasing infection rate based on infection times," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 1041-1048.
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    3. Ma, Jing & Li, Dandan & Tian, Zihao, 2016. "Rumor spreading in online social networks by considering the bipolar social reinforcement," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 108-115.
    4. Han, Dun & Sun, Mei, 2016. "An evolutionary vaccination game in the modified activity driven network by considering the closeness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 49-57.

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