Use an Improved Chicken Swarm Algorithm to Determine Optimal Stratigraphic Boundaries for a Threshold Geometric Stochastic Process for the Epidemic Covid-19

Abstract

The number of daily cases of a specific infection, such as the
emerging acute respiratory syndrome Corona (Covid-19), frequently
reveals several patterns during the outbreak of a particular epidemic
disease: a monotonic increase during the growing stage or the
outbreak of the epidemic, stationary in the number of daily cases
called in Some sources are the stabilization stage, that is, controlling
the epidemic to eliminate it, and then decreasing during the declining
stage. In this research, an artificial intelligence technique is used,
represented by the improved chicken swarm optimization algorithm to
determine the optimum stratigraphic limits for modeling data on the
numbers of daily cases of the Corona virus for the three Iraqi
governorates (Baghdad, Erbil and Basra). For this purpose, a
stochastic model called the Geometric stochastic process model with
an intelligent threshold was proposed, Since the turning points
(inversions) of the data were determined using an intelligent technique
to reach the best representation of the data under study. It was
concluded that the proposed model outperformed to the traditional
model by (23.41%) in modeling epidemic data in Baghdad
governorate, while the superiority in epidemic data for Basra and Erbil
governorates (23.96%) and (26.40%), respectively, and this indicates
That validity of the theoretical postulates that have been addressed in
the theoretical side.