Modelling covid-19 data using double geometric stochastic process

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.

Modelling covid-19 data using double geometric stochastic process Keywords:

double geometric stochastic process , geometric stochastic process , Parameter estimation , chicken swarm optimization algorithm , multiple monotone trends , root mean square criteria