IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v468y2024ics0096300323006768.html
   My bibliography  Save this article

A two-layer model with partial mapping: Unveiling the interplay between information dissemination and disease diffusion

Author

Listed:
  • Wang, Runzhou
  • Zhang, Xinsheng
  • Wang, Minghu

Abstract

This study delves into the pivotal role of information dissemination in public health, particularly how it influences the spread of diseases. By implementing a sophisticated two-layer partial mapping network model (UAU-SIRS), we investigate the dynamic relationship between information flow and disease transmission. Our approach utilizes extensive multiplexed network data, processed through a micro Markov chain (MMC) model, to simulate the interplay between information spread and disease dynamics. The findings reveal a noteworthy positive correlation between the rates of information dissemination, recovery in the network, and the epidemic threshold. Conversely, the conversion rate is inversely related to this threshold. A critical observation is that Scale-free (SF) networks, characterized by their uneven node distribution, are more susceptible to the impacts of information spread on their outbreak thresholds compared to Erdős-Rényi (ER) networks. This research offers crucial insights for epidemic prevention strategies and provides valuable guidance for managing the dissemination of disease-related information within complex network structures.

Suggested Citation

  • Wang, Runzhou & Zhang, Xinsheng & Wang, Minghu, 2024. "A two-layer model with partial mapping: Unveiling the interplay between information dissemination and disease diffusion," Applied Mathematics and Computation, Elsevier, vol. 468(C).
    • Handle: RePEc:eee:apmaco:v:468:y:2024:i:c:s0096300323006768
    DOI: 10.1016/j.amc.2023.128507
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300323006768
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2023.128507?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.

    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:apmaco:v:468:y:2024:i:c:s0096300323006768. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    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.