Modelling covid-19 data using double geometric stochastic process

Document Type : Research Paper

Authors
Omar R. Jasim 1
Qutaiba N. Nauef 2
1 College of Administration and Economics, University of Al-Hamdaniya, Iraq
2 College of Administration and Economics, University of Bagdad, Iraq
10.22075/ijnaa.2021.5224
Abstract
Some properties of the geometric stochastic process (GSP) are studied along with those of a related process which we propose to call the Double geometric stochastic process (DGSP), under certain conditions. This process also has the same advantages of tractability as the geometric stochastic process; it exhibits some properties which may make it a useful complement to the multiple Trends geometric stochastic process. Also, it may be fit to observed data as easily as the geometric stochastic process. As a first attempt, the proposed model was applied to model the data and the Coronavirus epidemic in Iraq to reach the best model that represents the data under study. A chicken swarm optimization algorithm is proposed to choose the best model representing the data, in addition to estimating the parameters a, b, \(\mu\), and \(\sigma^{2}\) of the double geometric stochastic process, where \(\mu\) and \(\sigma^{2}\) are the mean and variance of \(X_{1}\), respectively.
Keywords
double geometric stochastic process
geometric stochastic process
parameter estimation
chicken swarm optimization algorithm
multiple monotone trends
root mean square criteria
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International Journal of Nonlinear Analysis and Applications
Volume 12, Issue 2
November 2021
Pages 1243-1254

  • Receive Date 16 April 2021
  • Revise Date 20 May 2021
  • Accept Date 28 May 2021

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