The research examines how different key parameters affect the SEIRD epidemic model through MATLAB Simulink simulations. The simulation model includes three scenarios which consist of a standard model and two alternative models for comparison. The model shows that higher parameter values speed up disease transmission while producing elevated peak infection rates. The SEIRD model demonstrates fundamental epidemiological patterns but it does not account for actual conditions such as underdiagnosis and mobility-based transmission. Recent studies indicate that models incorporating behavioral heterogeneity, diagnostic complexity, and adaptive parameters achieve superior predictive accuracy and enhance policy relevance. The research results demonstrate the value of parameter sensitivity analysis for disease spread understanding and establish a foundation for future model advancement.
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