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

Ehab M Almetwally1,2, Sanku Dey3, Saralees Nadarajah4

  • 1Faculty of Business Administration, Delta University of Science and Technology, Gamasa, 11152 Egypt.

Sankhya. Series A. (2008)
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Summary
This summary is machine-generated.

This study evaluates discrete statistical models to accurately predict daily COVID-19 fatalities. It identifies optimal models for analyzing coronavirus disease-19 (COVID-19) mortality data across several countries.

Keywords:
COVID-19discrete distributionshazard ratemaximum likelihood estimation.survival discretization

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Mathematical Biology

Background:

  • The COVID-19 pandemic necessitated robust mathematical modeling for tracking and prediction.
  • Accurate modeling of daily fatalities requires appropriate discrete probability distributions.

Purpose of the Study:

  • To identify and classify optimal discrete statistical models for COVID-19 fatality data.
  • To evaluate various discrete distributions for their efficacy in modeling new coronavirus disease-19 (COVID-19) deaths.
  • To determine the best statistical approach for analyzing COVID-19 mortality trends.

Main Methods:

  • Reviewed and analyzed numerous discrete probability distributions, including Binomial, Poisson, and discrete Weibull.
  • Investigated probability mass functions and hazard rate functions for selected models.
  • Employed maximum likelihood estimation for parameter estimation.
  • Conducted numerical analysis using COVID-19 fatality data from Angola, Ethiopia, French Guiana, El Salvador, Estonia, and Greece.

Main Results:

  • Several discrete models were assessed for their ability to fit COVID-19 daily fatality counts.
  • The study provides a comparative analysis of model performance based on empirical data.
  • Maximum likelihood estimates were utilized to fit model parameters to real-world data.

Conclusions:

  • The research offers insights into the most effective discrete statistical models for COVID-19 mortality analysis.
  • Findings can aid public health officials and researchers in better understanding and predicting pandemic-related deaths.
  • The study contributes to the field of epidemiological modeling by validating discrete distributions for time-series mortality data.