A Multifaceted Study of COVID-19: Stability Analysis, Numerical Solution, and Sensitivity Analysis
DOI:
https://doi.org/10.23918/eajse.v11i3p4Keywords:
Mathematical Modeling, COVID-19 Disease, Stability Analysis, Implicit-Explicit Runge-Kutta Method, Sensitivity AnalysisAbstract
The COVID-19 epidemic has emerged as a significant worldwide health concern, necessitating a thorough analysis of medical data pertaining to the virus. This research highlights the need to use mathematical modeling and computer simulations to comprehend fundamental transmission properties on a nationwide level. To efficiently control illnesses, it is essential to include computational methodologies and behavioral evaluations into the mathematical equations of the model. This article analyzes the model of the coronavirus, beginning with significant global healthcare challenges and offering essential guidance. The objective of the research is to investigate the COVID-19 case model in Nigeria for the year 2020, using approaches to ascertain the existence and uniqueness of the system's solution. The Implicit-Explicit Runge-Kutta (4,5,5) method was used for numerical computations, stability assessment, and sensitivity analysis. The Implicit-Explicit Runge-Kutta methods are often used as computing techniques for solving differential equations. Furthermore, the classical Runge-Kutta techniques are also used to solve intricate differential equations to see the efficiency of our technique. The data acquired via these procedures provide vital insights into the worldwide epidemic. These models have the capacity to forecast forthcoming data pertaining to persons who are ill, susceptible, socially isolated, and have recovered. This adds to worldwide endeavors in enhancing preventative measures and intervention initiatives. Stability analysis is used to identify crucial locations where disease transmission may be halted, while sensitivity analysis assesses the unique sensitivities of each component of the model to factors such as full, half, and non-normalizations. The results indicate that nearly every parameter in the model affects the spread of the virus among susceptible, exposed, and separated people.
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Copyright (c) 2025 Hemn Mohammed Rasool, Berivan Faris Azeez, Mardan Ameen Pirdawood
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