Modeling and Simulation of Infectious Disease Dynamics Utilizing SEIR Model

In the perpetual challenge against infectious diseases, the implementation of effective public health strategies requires a deep understanding of how illnesses work and how they spread, accurate estimation of parameters, and strategic planning for interventions. This paper explores the fundamental principles of the Susceptible - Exposed - Infectious - Recovered (SEIR) model, a widely utilized mathematical framework employed for monitoring infectious diseases. The real-life applications of Matrices were utilized through the SEIR Model Simulation, which employs a simulation loop that constantly updates the state vector. This paper also highlights the critical role of mathematical modeling, especially the SEIR model, in advancing our comprehension of the dynamics of infectious diseases. It underscores the importance of utilizing matrix operations and examines various models and approaches, contributing to the ongoing discourse in computational epidemiology and initiatives for public health. The discourse further delves into the significance of matrix operations within the SEIR model, where matrices play a crucial role in quantifying transitions between compartments and influencing the outcomes of simulations. By drawing on insights from related literature, this paper explores the evolution of infectious disease modeling, giving emphasis to the extended SEIR model and its applications in diverse contexts, such as ACL transmission dynamics and the complexities of the SARS-Co V-2 pandemic.