IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v178y2024ics0960077923012717.html
   My bibliography  Save this article

The increasing strength of higher-order interactions may homogenize the distribution of infections in Turing patterns

Author

Listed:
  • Li, Xing
  • He, Runzi
  • Xi, Yuxia
  • Xue, Yakui
  • Wang, Yunfei
  • Luo, Xiaofeng

Abstract

The spatial pattern of epidemic is one key metric describing epidemiological spread features, beneficial to formulation of the intervention measures. Networked reaction–diffusion (RD) systems have become popular to mathematically portray such patterns due to discrete distribution of human habitat. However, most of the current research focused on diffusion along pairwise interactions. Effect of diffusion along higher-order interactions is still understood poorly. To this end, in the paper, based on one classic SIR epidemic RD model, we study its Turing instability in simplicial complexes and analyze the impact of the simplex strength on Turing patterns. It is found by theoretical analysis and simulation that the distribution of infections in patterns tends to become homogeneity with the increase of the simplex strength, i.e., infection density of most nodes concentrates near the steady state. Obviously, for a newly emerging epidemic, such homogeneous scenario is unfavorable to epidemic control. Because it may lead to the decentralized allocation of limited resources, which is not enough to contain epidemic. In contrast, heterogeneous scenario that nodes with low and high infection density prominently distribute in two sides of steady state allows to put all limited resources into the targeted treatment of nodes with high infection density. Our findings link epidemic control with higher-order interactions and may provide a new insight into intervening epidemic from higher-order networks.

Suggested Citation

  • Li, Xing & He, Runzi & Xi, Yuxia & Xue, Yakui & Wang, Yunfei & Luo, Xiaofeng, 2024. "The increasing strength of higher-order interactions may homogenize the distribution of infections in Turing patterns," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    • Handle: RePEc:eee:chsofr:v:178:y:2024:i:c:s0960077923012717
    DOI: 10.1016/j.chaos.2023.114369
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077923012717
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2023.114369?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 search for a different version of it.

    References listed on IDEAS

    as
    1. L. V. Gambuzza & F. Patti & L. Gallo & S. Lepri & M. Romance & R. Criado & M. Frasca & V. Latora & S. Boccaletti, 2021. "Stability of synchronization in simplicial complexes," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    2. Iacopo Iacopini & Giovanni Petri & Alain Barrat & Vito Latora, 2019. "Simplicial models of social contagion," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    3. Malbor Asllani & Joseph D. Challenger & Francesco Saverio Pavone & Leonardo Sacconi & Duccio Fanelli, 2014. "The theory of pattern formation on directed networks," Nature Communications, Nature, vol. 5(1), pages 1-9, December.
    4. Muolo, Riccardo & Gallo, Luca & Latora, Vito & Frasca, Mattia & Carletti, Timoteo, 2023. "Turing patterns in systems with high-order interactions," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    Full references (including those not matched with items on IDEAS)

    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. Muolo, Riccardo & Gallo, Luca & Latora, Vito & Frasca, Mattia & Carletti, Timoteo, 2023. "Turing patterns in systems with high-order interactions," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    2. Muolo, Riccardo & Carletti, Timoteo & Bianconi, Ginestra, 2024. "The three way Dirac operator and dynamical Turing and Dirac induced patterns on nodes and links," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    3. Krishnagopal, Sanjukta & Bianconi, Ginestra, 2023. "Topology and dynamics of higher-order multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    4. Martina Contisciani & Federico Battiston & Caterina De Bacco, 2022. "Inference of hyperedges and overlapping communities in hypergraphs," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    5. Ramasamy, Mohanasubha & Devarajan, Subhasri & Kumarasamy, Suresh & Rajagopal, Karthikeyan, 2022. "Effect of higher-order interactions on synchronization of neuron models with electromagnetic induction," Applied Mathematics and Computation, Elsevier, vol. 434(C).
    6. Zhang, Ziyu & Mei, Xuehui & Jiang, Haijun & Luo, Xupeng & Xia, Yang, 2023. "Dynamical analysis of Hyper-SIR rumor spreading model," Applied Mathematics and Computation, Elsevier, vol. 446(C).
    7. Yuanzhao Zhang & Maxime Lucas & Federico Battiston, 2023. "Higher-order interactions shape collective dynamics differently in hypergraphs and simplicial complexes," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    8. Xu, Can & Zhai, Yun & Wu, Yonggang & Zheng, Zhigang & Guan, Shuguang, 2023. "Enhanced explosive synchronization in heterogeneous oscillator populations with higher-order interactions," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    9. Guilherme Ferraz de Arruda & Giovanni Petri & Pablo Martin Rodriguez & Yamir Moreno, 2023. "Multistability, intermittency, and hybrid transitions in social contagion models on hypergraphs," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    10. Nie, Yanyi & Li, Wenyao & Pan, Liming & Lin, Tao & Wang, Wei, 2022. "Markovian approach to tackle competing pathogens in simplicial complex," Applied Mathematics and Computation, Elsevier, vol. 417(C).
    11. Keliger, Dániel & Horváth, Illés, 2023. "Accuracy criterion for mean field approximations of Markov processes on hypergraphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    12. Nie, Yanyi & Zhong, Xiaoni & Lin, Tao & Wang, Wei, 2022. "Homophily in competing behavior spreading among the heterogeneous population with higher-order interactions," Applied Mathematics and Computation, Elsevier, vol. 432(C).
    13. Mock, Andrea & Volić, Ismar, 2021. "Political structures and the topology of simplicial complexes," Mathematical Social Sciences, Elsevier, vol. 114(C), pages 39-57.
    14. Almiala, Into & Aalto, Henrik & Kuikka, Vesa, 2023. "Influence spreading model for partial breakthrough effects on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    15. Zhou, Jiaying & Ye, Yong & Arenas, Alex & Gómez, Sergio & Zhao, Yi, 2023. "Pattern formation and bifurcation analysis of delay induced fractional-order epidemic spreading on networks," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    16. You, Xuemei & Zhang, Man & Ma, Yinghong & Tan, Jipeng & Liu, Zhiyuan, 2023. "Impact of higher-order interactions and individual emotional heterogeneity on information-disease coupled dynamics in multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    17. Li, Wenyao & Cai, Meng & Zhong, Xiaoni & Liu, Yanbing & Lin, Tao & Wang, Wei, 2023. "Coevolution of epidemic and infodemic on higher-order networks," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    18. Václav Snášel & Pavla Dráždilová & Jan Platoš, 2021. "Cliques Are Bricks for k-CT Graphs," Mathematics, MDPI, vol. 9(11), pages 1-9, May.
    19. Chen, Mengxin & Zheng, Qianqian, 2023. "Diffusion-driven instability of a predator–prey model with interval biological coefficients," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    20. Ide, Yusuke & Izuhara, Hirofumi & Machida, Takuya, 2016. "Turing instability in reaction–diffusion models on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 331-347.

    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:chsofr:v:178:y:2024:i:c:s0960077923012717. 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.