IDEAS home Printed from https://ideas.repec.org/a/taf/gcmbxx/v26y2023i2p138-159.html
   My bibliography  Save this article

The measles epidemic model assessment under real statistics: an application of stochastic optimal control theory

Author

Listed:
  • Peijiang Liu
  • Rukhsar Ikram
  • Amir Khan
  • Anwarud Din

Abstract

A stochastic epidemic model with random noise transmission is taken into account, describing the dynamics of the measles viral infection. The basic reproductive number is calculated corresponding to the stochastic model. It is determined that, given initial positive data, the model has bounded, unique, and positive solution. Additionally, utilizing stochastic Lyapunov functional theory, we study the extinction of the disease. Stationary distribution and extinction of the infection are examined by providing sufficient conditions. We employed optimal control principles and examined stochastic control systems to regulate the transmission of the virus using environmental factors. Graphical representations have been offered for simplicity of comprehending in order to further verify the acquired analytical findings.

Suggested Citation

  • Peijiang Liu & Rukhsar Ikram & Amir Khan & Anwarud Din, 2023. "The measles epidemic model assessment under real statistics: an application of stochastic optimal control theory," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 26(2), pages 138-159, January.
    • Handle: RePEc:taf:gcmbxx:v:26:y:2023:i:2:p:138-159
    DOI: 10.1080/10255842.2022.2050222
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/10255842.2022.2050222
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/10255842.2022.2050222?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. Fikriye Yılmaz & Hacer Öz Bakan & Gerhard-Wilhelm Weber, 2024. "Weak Second-Order Conditions of Runge–Kutta Method for Stochastic Optimal Control Problems," Journal of Optimization Theory and Applications, Springer, vol. 202(1), pages 497-517, July.

    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:26:y:2023:i:2:p:138-159. 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.