IDEAS home Printed from https://ideas.repec.org/a/spr/joptap/v204y2025i3d10.1007_s10957-024-02604-1.html
   My bibliography  Save this article

Comparison of Two Mean Field Approaches to Modeling an Epidemic Spread

Author

Listed:
  • Viktoriya Petrakova

    (Institute of Computational Modelling SB RAS)

  • Olga Krivorotko

    (Sobolev Institute of Mathematics SB RAS)

Abstract

This paper describes and compares two approaches to modeling the spread of an epidemic based on the mean field theory, both with each other and with the original epidemiological SIR model. The first mean field approach is a model, in which an isolation strategy for each epidemiological group (Susceptible, Infected, and Removed) is chosen as an optimal control. The second is another mean field model, in which isolation strategy is common for the entire population. The considered models have been compared analytically, their sensitivity analysis has been carried out and their predictive capabilities have been estimated using sets of synthetic and real data. The well-known epidemiological SIR model is a part of comparison too. For one of the mean field models, its finite-difference analogue has been devised. The models have also been assessed in terms of their applicability for predicting a viral epidemic spread.

Suggested Citation

  • Viktoriya Petrakova & Olga Krivorotko, 2025. "Comparison of Two Mean Field Approaches to Modeling an Epidemic Spread," Journal of Optimization Theory and Applications, Springer, vol. 204(3), pages 1-33, March.
    • Handle: RePEc:spr:joptap:v:204:y:2025:i:3:d:10.1007_s10957-024-02604-1
    DOI: 10.1007/s10957-024-02604-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10957-024-02604-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10957-024-02604-1?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:spr:joptap:v:204:y:2025:i:3:d:10.1007_s10957-024-02604-1. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.