IDEAS home Printed from https://ideas.repec.org/a/taf/gcmbxx/v25y2022i16p1852-1869.html
   My bibliography  Save this article

Fractal fractional based transmission dynamics of COVID-19 epidemic model

Author

Listed:
  • Peijiang Liu
  • Mati ur Rahman
  • Anwarud Din

Abstract

We investigate the dynamical behavior of Coronavirus (COVID-19) for different infections phases and multiple routes of transmission. In this regard, we study a COVID-19 model in the context of fractal-fractional order operator. First, we study the COVID-19 dynamics with a fractal fractional-order operator in the framework of Atangana–Baleanu fractal-fractional operator. We estimated the basic reduction number and the stability results of the proposed model. We show the data fitting to the proposed model. The system has been investigated for qualitative analysis. Novel numerical methods are introduced for the derivation of an iterative scheme of the fractal-fractional Atangana–Baleanu order. Finally, numerical simulations are performed for various orders of fractal-fractional dimension.

Suggested Citation

  • Peijiang Liu & Mati ur Rahman & Anwarud Din, 2022. "Fractal fractional based transmission dynamics of COVID-19 epidemic model," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 25(16), pages 1852-1869, December.
  • Handle: RePEc:taf:gcmbxx:v:25:y:2022:i:16:p:1852-1869
    DOI: 10.1080/10255842.2022.2040489
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/10255842.2022.2040489
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/10255842.2022.2040489?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.

    Citations

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


    Cited by:

    1. Xuan Leng & Asad Khan & Anwarud Din, 2023. "Probability Analysis of a Stochastic Non-Autonomous SIQRC Model with Inference," Mathematics, MDPI, vol. 11(8), pages 1-18, April.

    More about this item

    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:taf:gcmbxx:v:25:y:2022:i:16:p:1852-1869. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/gcmb .

    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.