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.