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首页> 外文期刊>Mathematical Methods in the Applied Sciences >Global dynamics and bifurcation analysis of a fractional‐order SEIR epidemic model with saturation incidence rate
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Global dynamics and bifurcation analysis of a fractional‐order SEIR epidemic model with saturation incidence rate

机译:Global dynamics and bifurcation analysis of a fractional‐order SEIR epidemic model with saturation incidence rate

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摘要

The present paper studies a fractional‐order SEIR epidemic model for the transmission dynamics of infectious diseases such as HIV and HBV that spreads in the host population. The total host population is considered bounded, and Holling type‐II saturation incidence rate is involved as the infection term. Using the proposed SEIR epidemic model, the threshold quantity, namely, basic reproduction number R0, is obtained that determines the status of the disease, whether it dies out or persists in the whole population. The model's analysis shows that two equilibria exist, namely, disease‐free equilibrium (DFE) and endemic equilibrium (EE). The global stability of the equilibria is determined using a Lyapunov functional approach. The disease status can be verified based on obtained threshold quantity R0. If R01, a unique EE exists, and that is globally stable under certain conditions in the feasible region. The Caputo type fractional derivative is taken as the fractional operator. The bifurcation and sensitivity analyses are also performed for the proposed model that determines the relative importance of the parameters in disease transmission. The numerical solution of the model is obtained by the generalized Adams–Bashforth–Moulton method. Finally, numerical simulations are performed to illustrate and verify the analytical results. From the obtained results, it is concluded that the order of the fractional derivative plays a significant role in the dynamic process. Also, from the sensitivity analysis results, it is seen that the parameter δ ( infection rate) is positively correlated to R0, while the parameter μ (inhibition rate) is negatively correlated to R0, which means these parameters are more sensitive than others in disease dynamics.

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