IDEAS home Printed from https://ideas.repec.org/a/wsi/fracta/v30y2022i05ns0218348x2240148x.html
   My bibliography  Save this article

Skewed Auto-Regressive Process With Exogenous Input Variables: An Application In The Administered Vaccine Doses On Covid-19 Spread

Author

Listed:
  • MOHSEN MALEKI

    (Department of Statistics, Faculty of Mathematics and Statistics, University of Isfahan, Isfahan 81746-73441, Iran)

  • MOHAMMAD REZA MAHMOUDI

    (��Department of Statistics, Faculty of Science, Fasa University, Fasa, Fars, Iran)

  • HAMID BIDRAM

    (Department of Statistics, Faculty of Mathematics and Statistics, University of Isfahan, Isfahan 81746-73441, Iran)

  • AMIR MOSAVI

    (��Faculty of Civil Engineering, Technische Universität Dresden, 01069 Dresden, Germany§John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary¶Institute of Information Society, University of Public Service, 1083 Budapest, Hungary∥Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology in Bratislava, Bratislava, Slovakia)

Abstract

This study focuses on the prevalence of COVID-19 disease along with vaccination in the United States. We have considered the daily total infected cases of COVID-19 with total vaccinated cases as exogenous input and modeled them using light/heavy tailed auto-regressive with exogenous input model based on the innovations that belong to the flexible class of the two-piece scale mixtures of normal (TP–SMN) family. We have shown that the prediction of COVID-19 spread is affected by the rate of vaccine injection. In fact, the presence of exogenous input variables in time series models not only increases the accuracy of modeling, but also causes better and closer approximations in some issues including predictions. An Expectation-Maximization (EM) type algorithm has been considered for finding the maximum likelihood (ML) estimations of the model parameters, and modeling as well as predicting the infected numbers of COVID-19 in the presence of the vaccinated cases in the US.

Suggested Citation

  • Mohsen Maleki & Mohammad Reza Mahmoudi & Hamid Bidram & Amir Mosavi, 2022. "Skewed Auto-Regressive Process With Exogenous Input Variables: An Application In The Administered Vaccine Doses On Covid-19 Spread," FRACTALS (fractals), World Scientific Publishing Co. Pte. Ltd., vol. 30(05), pages 1-10, August.
    • Handle: RePEc:wsi:fracta:v:30:y:2022:i:05:n:s0218348x2240148x
    DOI: 10.1142/S0218348X2240148X
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0218348X2240148X
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0218348X2240148X?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:wsi:fracta:v:30:y:2022:i:05:n:s0218348x2240148x. 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: Tai Tone Lim (email available below). General contact details of provider: https://www.worldscientific.com/worldscinet/fractals .

    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.