| Abstract: |
| This talk presents a novel epidemic model that incorporates both reported and unreported cases, distinguishing between symptomatic and asymptomatic individuals. The global stability of the model is demonstrated using a Lyapunov function, emphasizing the significant impact of asymptomatic cases on disease dynamics and control measures. The results of elasticity analysis also explores, revealing how this division influences the fundamental reproduction number. Additionally, sensitivity analysis is performed using Partial Rank Correlation Coefficient (PRCC) and Sobol indices to assess the influence of various parameters on the model`s compartments ($E$, $I_r$, $I_u$, $R$). The results reveal the critical roles of transmission rate ($\beta$), recovery rates ($\lambda_r$, $\lambda_u$), and immunity loss rate ($\alpha$) in shaping model dynamics. These findings provide insights into the primary drivers of the model`s behavior and underscore the importance of considering both symptomatic and asymptomatic cases in developing effective epidemic models and control strategies. |
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