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Impact of heterogeneous human activities on epidemic spreading

Author

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  • Yang, Zimo
  • Cui, Ai-Xiang
  • Zhou, Tao

Abstract

Recent empirical observations suggest a heterogeneous nature of human activities. The heavy-tailed inter-event time distribution at the population level is well accepted, while whether the individual acts in a heterogeneous way is still under debate. Motivated by the impact of temporal heterogeneity of human activities on epidemic spreading, this paper studies the susceptible-infected model on a fully mixed population, where each individual acts in a completely homogeneous way but different individuals have different mean activities. Extensive simulations show that the heterogeneity of activities at the population level remarkably affects the speed of spreading, even though each individual behaves regularly. Furthermore, the spreading speed of this model is more sensitive to the change of system heterogeneity compared with the model consisted of individuals acting with heavy-tailed inter-event time distributions. This work refines our understanding of the impact of heterogeneous human activities on epidemic spreading.

Suggested Citation

  • Yang, Zimo & Cui, Ai-Xiang & Zhou, Tao, 2011. "Impact of heterogeneous human activities on epidemic spreading," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4543-4548.
    • Handle: RePEc:eee:phsmap:v:390:y:2011:i:23:p:4543-4548
    DOI: 10.1016/j.physa.2011.06.068
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    Citations

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    Cited by:

    1. Hong, Xiao & Han, Yuexing & Wang, Bing, 2023. "Impacts of detection and contact tracing on the epidemic spread in time-varying networks," Applied Mathematics and Computation, Elsevier, vol. 439(C).
    2. Li, Ruiqi & Wang, Wenxu & Di, Zengru, 2017. "Effects of human dynamics on epidemic spreading in Côte d’Ivoire," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 30-40.
    3. Zhang, Xin & Xie, Sheng & Vilela, André L.M. & Stanley, H. Eugene, 2019. "Inter-event time interval analysis of organizational-level activity: Venture capital market case," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 346-355.
    4. Huo, Jie & Wang, Xu-Ming & Zhao, Ning & Hao, Rui, 2016. "Statistical characteristics of dynamics for population migration driven by the economic interests," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 123-134.
    5. Wu, Lianren & Qi, Jiayin & Shi, Nan & Li, Jinjie & Yan, Qiang, 2022. "Revealing the relationship of topics popularity and bursty human activity patterns in social temporal networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    6. 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.
    7. Chen, Ning & Zhu, Xuzhen & Chen, Yanyan, 2019. "Information spreading on complex networks with general group distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 671-676.
    8. Wei Duan, 2021. "Matrix-Based Formulation of Heterogeneous Individual-Based Models of Infectious Diseases: Using SARS Epidemic as a Case Study," IJERPH, MDPI, vol. 18(11), pages 1-20, May.

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