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The spreading of infectious diseases with recurrent mobility of community population

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  • Yang, Jin-Xuan

Abstract

To exchange information, recurrent mobility of population occurs among different communities in the network. Many researches have shown that spreading process of diseases is affected by the mobility dynamics of population. In this paper, we use the discrete-time Markov-chain approach to study the spreading process of diseases with recurrent population mobility. The epidemic threshold is given. We analyze some factors that affect the spreading of infectious diseases, including community size, mobility ratio, and the number of communities. The results show that a small-scale community structure, high mobility ratio of community population and a large number of temporal communities are conducive to preventing infectious diseases. As the infectious rate is close to 1, the fraction of infected individuals is only determined by the recovery rate. The numerical simulations further support our conclusions.

Suggested Citation

  • Yang, Jin-Xuan, 2020. "The spreading of infectious diseases with recurrent mobility of community population," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    • Handle: RePEc:eee:phsmap:v:541:y:2020:i:c:s0378437119318576
    DOI: 10.1016/j.physa.2019.123316
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    References listed on IDEAS

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    1. Wu, Minna & Han, She & Sun, Mei & Han, Dun, 2018. "How the distance between regional and human mobility behavior affect the epidemic spreading," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 1823-1830.
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    5. Martin Rosvall & Alcides V. Esquivel & Andrea Lancichinetti & Jevin D. West & Renaud Lambiotte, 2014. "Memory in network flows and its effects on spreading dynamics and community detection," Nature Communications, Nature, vol. 5(1), pages 1-13, December.
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    Cited by:

    1. Shao, Qi & Han, Dun, 2022. "Epidemic spreading in metapopulation networks with heterogeneous mobility rates," Applied Mathematics and Computation, Elsevier, vol. 412(C).
    2. Feng, Liang & Zhao, Qianchuan & Zhou, Cangqi, 2020. "Epidemic in networked population with recurrent mobility pattern," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    3. Lu, Zhong-Wen & Xu, Yuan-Hao & Chen, Jie & Hu, Mao-Bin, 2023. "Investigation of traffic-driven epidemic spreading by taxi trip data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).

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