Analysis of the COVID-19 epidemic utilizing Ornstein-Uhlenbeck processes in Iraq
Keywords:
COVID-19, Ornstein-Uhlenbeck process, numerical simulation, stochastic anal- ysis. Euler approximation, maximum likelihood estimatorAbstract
The swift and unforeseen global spread of COVID-19 has intensified the focus on mathematical modeling of the disease worldwide. This study presents a stochastic differential equation, specifically the Ornstein-Uhlenbeck process, representing Iraq's COVID-19 time series data. This dataset includes the number of infected individuals, fatalities, and vaccinated cases. We estimated the process parameters using the maximum likelihood estimator for the counts of infected, deceased, and vaccinated individuals.
We utilized the Euler approximation and the Milstein method within MATLAB to simulate the epidemic's time series based on estimated and actual data. Finally, we compared the effectiveness of the Euler and Milstein methods in approximating the processing of the Ornstein-Uhlenbeck process concerning the numbers of infected, deceased, and vaccinated individuals in Iraq. We evaluated actual and estimated cases, highlighting the reliability and precision of the two numerical methods.
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