IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v646y2024ics0378437124003807.html
   My bibliography  Save this article

Finite-size behavior in phase transitions and scaling in the progress of an epidemic

Author

Listed:
  • Das, Subir K.

Abstract

Analytical descriptions of patterns concerning spread and fatality during an epidemic, covering natural as well as restriction periods, are important for reducing damage. We employ a scaling model to investigate this aspect in the real data of COVID-19. It transpires from our study that the statistics of fatality in many geographical regions follow unique behavior, implying the existence of universality. Via Monte Carlo simulations in finite boxes, we also present results on coarsening dynamics in a generic model that is used frequently for the studies of phase transitions in various condensed matter systems. It is shown that the overall growth, in a large set of regions, concerning disease dynamics, and that during phase transitions in a broad class of finite systems are closely related to each other and can be described via a single and compact mathematical function, thereby providing a new angle to the studies of the former. In the case of disease we have demonstrated that the employed scaling model has predictive capability, when used appropriately, even for periods with complex social restrictions. These results are of much general epidemiological relevance.

Suggested Citation

  • Das, Subir K., 2024. "Finite-size behavior in phase transitions and scaling in the progress of an epidemic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 646(C).
    • Handle: RePEc:eee:phsmap:v:646:y:2024:i:c:s0378437124003807
    DOI: 10.1016/j.physa.2024.129871
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437124003807
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2024.129871?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:phsmap:v:646:y:2024:i:c:s0378437124003807. 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: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.