IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v541y2020ics0378437119318576.html

The spreading of infectious diseases with recurrent mobility of community population

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119318576
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2019.123316?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    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.
    2. Marcel Salathé & James H Jones, 2010. "Dynamics and Control of Diseases in Networks with Community Structure," PLOS Computational Biology, Public Library of Science, vol. 6(4), pages 1-11, April.
    3. Paolo Bajardi & Chiara Poletto & Jose J Ramasco & Michele Tizzoni & Vittoria Colizza & Alessandro Vespignani, 2011. "Human Mobility Networks, Travel Restrictions, and the Global Spread of 2009 H1N1 Pandemic," PLOS ONE, Public Library of Science, vol. 6(1), pages 1-8, January.
    4. Zhou, Jie & Liu, Zonghua, 2009. "Epidemic spreading in communities with mobile agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(7), pages 1228-1236.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Eugenio Valdano & Chiara Poletto & Armando Giovannini & Diana Palma & Lara Savini & Vittoria Colizza, 2015. "Predicting Epidemic Risk from Past Temporal Contact Data," PLOS Computational Biology, Public Library of Science, vol. 11(3), pages 1-19, March.
    2. Li, Yusheng & Yao, Yichao & Feng, Minyu & Benko, Tina P. & Perc, Matjaž & Završnik, Jernej, 2025. "Epidemic dynamics in homes and destinations under recurrent mobility patterns," Chaos, Solitons & Fractals, Elsevier, vol. 195(C).
    3. Feng, Liang & Zhao, Qianchuan & Zhou, Cangqi, 2020. "Epidemic in networked population with recurrent mobility pattern," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    4. Chao Min & Qingyu Chen & Erjia Yan & Yi Bu & Jianjun Sun, 2021. "Citation cascade and the evolution of topic relevance," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(1), pages 110-127, January.
    5. Gregory, Steve, 2012. "Ordered community structure in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(8), pages 2752-2763.
    6. Wei Zhong, 2017. "Simulating influenza pandemic dynamics with public risk communication and individual responsive behavior," Computational and Mathematical Organization Theory, Springer, vol. 23(4), pages 475-495, December.
    7. Britta Glennon & Francisco Morales & Seth Carnahan & Exequiel Hernández, 2025. "Does Employing Skilled Immigrants Enhance Competitive Performance? Evidence from European Football Clubs," Management Science, INFORMS, vol. 71(7), pages 5746-5766, July.
    8. Chen, Dandan & Zheng, Muhua & Zhao, Ming & Zhang, Yu, 2018. "A dynamic vaccination strategy to suppress the recurrent epidemic outbreaks," Chaos, Solitons & Fractals, Elsevier, vol. 113(C), pages 108-114.
    9. Xie, Xiaoxiao & Huo, Liang'an, 2024. "Co-evolution dynamics between information and epidemic with asymmetric activity levels and community structure in time-varying multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    10. Hakan Yilmazkuday, 2021. "Welfare costs of COVID‐19: Evidence from US counties," Journal of Regional Science, Wiley Blackwell, vol. 61(4), pages 826-848, September.
    11. Li, Xiang, 2025. "Higher-order dependencies for multi-step link prediction," Chaos, Solitons & Fractals, Elsevier, vol. 200(P1).
    12. Gong, Chang & Li, Jichao & Qian, Liwei & Li, Siwei & Yang, Zhiwei & Yang, Kewei, 2024. "HMSL: Source localization based on higher-order Markov propagation," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    13. Wang, Weihong & Chen, MingMing & Min, Yong & Jin, Xiaogang, 2016. "Structural diversity effects of multilayer networks on the threshold of interacting epidemics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 254-262.
    14. Bowen Yan & Steve Gregory, 2013. "Identifying Communities and Key Vertices by Reconstructing Networks from Samples," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-14, April.
    15. Wu, Di & Hamilton, Hanna & Jagrowski, Liam & Nazzal, Dima & Steimle, Lauren N., 2024. "Revisiting the small-world property of co-enrollment networks: A network analysis of hybrid course delivery strategies," Socio-Economic Planning Sciences, Elsevier, vol. 96(C).
    16. Zhou, Bin & Yan, Xiao-Yong & Xu, Xiao-Ke & Xu, Xiao-Ting & Wang, Nianxin, 2018. "Evolutionary of online social networks driven by pareto wealth distribution and bidirectional preferential attachment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 427-434.
    17. Michele Coscia & Ricardo Hausmann, 2015. "Evidence That Calls-Based and Mobility Networks Are Isomorphic," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-15, December.
    18. Hartwell, Christopher A., 2021. "What Drove the First Response to the COVID-19 Pandemic? The Role of Institutions and Leader Attributes," MPRA Paper 110563, University Library of Munich, Germany.
    19. Saxena, Chandni & Doja, M.N. & Ahmad, Tanvir, 2018. "Group based centrality for immunization of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 35-47.
    20. Xiaoyan Mu & Anthony Gar-On Yeh & Xiaohu Zhang, 2021. "The interplay of spatial spread of COVID-19 and human mobility in the urban system of China during the Chinese New Year," Environment and Planning B, , vol. 48(7), pages 1955-1971, September.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:541:y:2020:i:c:s0378437119318576. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.