An Overview of Discrete Distributions in Modelling COVID-19 Data Sets

Ehab M. Almetwally, Sanku Dey () and Saralees Nadarajah ()
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Ehab M. Almetwally: Delta University of Science and Technology
Sanku Dey: St. Anthony’s College
Saralees Nadarajah: University of Manchester

Sankhya A: The Indian Journal of Statistics, 2023, vol. 85, issue 2, No 12, 1403-1430

Abstract: Abstract The mathematical modeling of the coronavirus disease-19 (COVID-19) pandemic has been attempted by a large number of researchers from the very beginning of cases worldwide. The purpose of this research work is to find and classify the modelling of COVID-19 data by determining the optimal statistical modelling to evaluate the regular count of new COVID-19 fatalities, thus requiring discrete distributions. Some discrete models are checked and reviewed, such as Binomial, Poisson, Hypergeometric, discrete negative binomial, beta-binomial, Skellam, beta negative binomial, Burr, discrete Lindley, discrete alpha power inverse Lomax, discrete generalized exponential, discrete Marshall-Olkin Generalized exponential, discrete Gompertz-G-exponential, discrete Weibull, discrete inverse Weibull, exponentiated discrete Weibull, discrete Rayleigh, and new discrete Lindley. The probability mass function and the hazard rate function are addressed. Discrete models are discussed based on the maximum likelihood estimates for the parameters. A numerical analysis uses the regular count of new casualties in the countries of Angola,Ethiopia, French Guiana, El Salvador, Estonia, and Greece. The empirical findings are interpreted in-depth.

Keywords: COVID-19; hazard rate; discrete distributions; survival discretization; maximum likelihood estimation.; Primary; 62E99 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s13171-022-00291-6

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