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A novel epidemic spreading model with decreasing infection rate based on infection times

Author

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  • Huang, Yunhan
  • Ding, Li
  • Feng, Yun

Abstract

A new epidemic spreading model where individuals can be infected repeatedly is proposed in this paper. The infection rate decreases according to the times it has been infected before. This phenomenon may be caused by immunity or heightened alertness of individuals. We introduce a new parameter called decay factor to evaluate the decrease of infection rate. Our model bridges the Susceptible–Infected–Susceptible(SIS) model and the Susceptible–Infected–Recovered(SIR) model by this parameter. The proposed model has been studied by Monte-Carlo numerical simulation. It is found that initial infection rate has greater impact on peak value comparing with decay factor. The effect of decay factor on final density and threshold of outbreak is dominant but weakens significantly when considering birth and death rates. Besides, simulation results show that the influence of birth and death rates on final density is non-monotonic in some circumstances.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:444:y:2016:i:c:p:1041-1048
    DOI: 10.1016/j.physa.2015.10.104
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    References listed on IDEAS

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    1. Zhou, Yinzuo & Xia, Yingjie, 2014. "Epidemic spreading on weighted adaptive networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 399(C), pages 16-23.
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    4. Li, Chun-Hsien, 2015. "Dynamics of a network-based SIS epidemic model with nonmonotone incidence rate," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 234-243.
    5. Gong, Yong-Wang & Song, Yu-Rong & Jiang, Guo-Ping, 2014. "Epidemic spreading in metapopulation networks with heterogeneous infection rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 208-218.
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    Cited by:

    1. Yuan Liu & Chuyao Liao & Li Zhuo & Haiyan Tao, 2022. "Evaluating Effects of Dynamic Interventions to Control COVID-19 Pandemic: A Case Study of Guangdong, China," IJERPH, MDPI, vol. 19(16), pages 1-17, August.
    2. Fu, Minglei & Yang, Hongbo & Feng, Jun & Guo, Wen & Le, Zichun & Lande, Dmytro & Manko, Dmytro, 2018. "Preferential information dynamics model for online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 993-1005.
    3. Yunhan Huang & Quanyan Zhu, 2022. "Game-Theoretic Frameworks for Epidemic Spreading and Human Decision-Making: A Review," Dynamic Games and Applications, Springer, vol. 12(1), pages 7-48, March.
    4. Jia, Peng & Liu, Jiayong & Fang, Yong & Liu, Liang & Liu, Luping, 2018. "Modeling and analyzing malware propagation in social networks with heterogeneous infection rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 240-254.

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