Abstract
In this study, COVID-19 data in Turkey is investigated by Stochastic Differential Equation Modeling (SDEM). Firstly, parameters of SDE which occur in mentioned epidemic problem are estimated by using the maximum likelihood procedure. Then, we have obtained reasonable Stochastic Differential Equation (SDE) based on the given COVID-19 data. Moreover, by applying Euler-Maruyama Approximation Method trajectories of SDE are achieved. The performances of trajectories are established by Chi-Square criteria. The results are acquired by using statistical software R-Studio.These results are also corroborated by graphical representation.
Highlights
Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus
It is known that Stochastic differential equations model (SDEM) are more realistic mathematical model than normal differential equation models of the situation [3]
We have examined by SDEM the COVID-19 data in Turkey between 11.03.2020 and 09.06.2020
Summary
Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. Stochastic differential equations model (SDEM) stochastic evolution as time evolves. These models have a variety of applications in many disciplines and emerge naturally in the study of many phenomena. They devote with full strength our concentrated attention to sufficient conditions for extinction and persistence They examine the threshold of the proposed stochastic COVID-19 model, when noise is small or large. They show the numerical simulations graphically using MATLAB. Considering the studies mentioned above, the main features of the work finds an appropriate stochastic differential equation model for the given COVID-19 data. Likelihood procedure and approximate solutions of the established stochastic differential equation model have been obtained by using Euler-Maruyama method
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