Comparison of Doubly and Intelligent Threshold Geometric Stochastic Process in the Study of Covid-19 Virus Infection

Abstract

During the outbreak of a particular epidemic disease, Sometimes, the number of daily cases of a given epidemic shows several trends: a monotonous increase during the epidemic's development or outbreak period, accompanied by the stabilized stage of the number of daily cases of infection (stabilization), that is, controlling the epidemic to eradicate it, and then decreasing during the epidemic's decline. A comparison of the doubly geometric stochastic process and the intelligent threshold geometric stochastic process was performed in this paper using the chicken swarm optimization algorithm to determine the optimal stratigraphic boundaries for modelling data on the daily numbers of Coronavirus infections (Covid-19) on the three Iraqi governorates (Baghdad, Erbil, and Basra). To that end, a comparison of the two proposed models was performed to determine which model best fitted the data under study. It was discovered that the intelligent threshold geometric stochastic process model outperformed the doubly geometric stochastic process model in modelling epidemic data in the Baghdad governorate by (11.1%), while the supremacy in epidemic data for Basra and Erbil governorates was (3.98%) and (13.90%), respectively. this demonstrates the relevance of the theoretical postulates discussed on the theoretical side.