IDEAS home Printed from https://ideas.repec.org/a/erh/journl/v15y2023i2p45-58.html
   My bibliography  Save this article

Long Run Predictions Using Gompertz Curves - A State Wise Analysis of COVID-19 Infections in India

Author

Listed:
  • Abhigayan Adhikary
  • Manoranjan Pal

    (Economic Research Unit, Indian Statistical Institute, 203 Barrackpore Trunk Road, Kolkata 700108, India)

Abstract

The aim of this paper is to perform a State wise Analysis of the First and the Second COVID- 19 Waves experienced by India using the Gompertz Curves and to estimate the maximum number of affected individuals for each wave with the best possible accuracy. A total of 21 large States are chosen for the analysis encompassing 97% of the Indian population. Data on cumulative number of cases is available till 31st October 2021. The entire dataset is segregated into two parts, i.e., the First and the Second Waves and then modelled individually by the Gompertz Curves with some generalizations. The predicted maximum cumulative numbers of COVID-19 affected individuals are found to be quite accurate. Besides, it is found to be possible to give a methodology how one can predict these numbers with a much smaller dataset. This is important as it can help the authorities in taking an informed decision on the efficient allocation of the limited health care resources.

Suggested Citation

  • Abhigayan Adhikary & Manoranjan Pal, 2023. "Long Run Predictions Using Gompertz Curves - A State Wise Analysis of COVID-19 Infections in India," International Econometric Review (IER), Econometric Research Association, vol. 15(2), pages 45-58, September.
    • Handle: RePEc:erh:journl:v:15:y:2023:i:2:p:45-58
    as

    Download full text from publisher

    File URL: https://www.era.org.tr/makaleler/1419054.pdf
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    COVID-19; disease modeling; Gompertz Curve; Non-linear least squares; time series; Forecasting; Prediction;
    All these keywords.

    JEL classification:

    • E0 - Macroeconomics and Monetary Economics - - General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    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:erh:journl:v:15:y:2023:i:2:p:45-58. 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: M. F. Cosar (email available below). General contact details of provider: https://edirc.repec.org/data/eratrea.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.