INVESTIGATION THE CASES OF NOVEL CORONA VIRUS DISEASE (COVID 19) USING DYNAMIC STATISTICAL TECHNIQUES

Abstract

This is the first to create concepts to examine the links between COVID-19 mortality and confirmed case, using both time series and Panel data models. We collected data each day in the 31 provinces, including deaths, confirmed cases, suspected cases and recoveries of four health indices. We examined the unrecognised elements including environmental expositions that contribute to the propagation of the illness through human-to-human transmission because of the intricacies of COVID-19. We have used estimate techniques to manage sectional dependency, endogeneity and heterogeneity uncontrolled. The estimates for the more effective dynamic reproduction number have also been calculated, showing that the gravity has typically declined since the initial instances were verified inside the individual nations. It was determined that the predictive capacity of the loglinear regression model was better suited and a basic daily estimate was calculated for both nations. The dynamic behaviour of COVID-19 infection by isolation class incorporation is developed in the mathematical model. First, it proposes the model formulation and then it discusses the positive nature of the model. The local stability of the suggested model and global stability dependent on the fundamental reproductive system are provided. The non-standard NSFD system and the Runge-Kutta 4th order approach are employed for the numerical solution of the suggested model. Finally there are some pictorial findings. Our data reveal that the likely source of COVID-19 outbreaks is human to human contact.