IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/109893.html
   My bibliography  Save this paper

Modélisation et prévision du nombre d’infections au coronavirus au Togo: une approche par un modèle ARIMA avec le logiciel R
[Modeling and forecasting the number of coronavirus infections in Togo: an ARIMA model approach with R software]

Author

Listed:
  • Kadanga, Mayo Takémsi Norris
  • Togbenu, Fo-Kossi Edem

Abstract

In this paper, we attempt to propose a short-term prediction model of the number of new cases of coronavirus infections in Togo using the R software. From the original daily data, a new weekly database containing 80 observations was constructed. After splitting this new database into training and test samples in order to select the appropriate model, the database was then used to build our forecasting model, the ARIMA(2,1,2) model. This model was used to make forecasts for the next four weeks. The findings show that Togo can expect approximately 1200 infections in average every week if suitable measures are not adopted in order to stop the rapid spread of the virus in the country.

Suggested Citation

  • Kadanga, Mayo Takémsi Norris & Togbenu, Fo-Kossi Edem, 2021. "Modélisation et prévision du nombre d’infections au coronavirus au Togo: une approche par un modèle ARIMA avec le logiciel R [Modeling and forecasting the number of coronavirus infections in Togo: ," MPRA Paper 109893, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:109893
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/109893/1/MPRA_paper_109893.pdf
    File Function: original version
    Download Restriction: no

    File URL: https://mpra.ub.uni-muenchen.de/109929/1/MPRA_paper_109929.pdf
    File Function: revised version
    Download Restriction: no

    File URL: https://mpra.ub.uni-muenchen.de/110535/1/MPRA_paper_110535.pdf
    File Function: revised version
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Coronavirus; COVID-19; Forecast; ARIMA;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:pra:mprapa:109893. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    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.