Global numerical analysis of an improved IMEX numerical scheme for a reaction diffusion SIS model in advective heterogeneous environments

Liu, X.; Yang, Zhanwen; Zeng, Yimin

doi:10.1016/j.camwa.2023.06.018

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…Epidemiologists utilize these methods to model the spread of illnesses and learn more about the dynamics of epidemics. Since systems of nonlinear differential equations that make up epidemic models are frequently difficult to solve explicitly, numerical approaches such as [9][10][11] are used to approximate the behavior of the underlying system. To analyze epidemic models numerically, compartmental models, spatial models, and network-based models have all been established.…”

Section: Introductionmentioning

confidence: 99%

Extended Runge-Kutta Scheme and Neural Network Approach for SEIR Epidemic Model with Convex Incidence Rate

Al Ghafli,

Nawaz,

Al Salman

et al. 2023

Processes

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For solving first-order linear and nonlinear differential equations, a new two-stage implicit–explicit approach is given. The scheme’s first stage, or predictor stage, is implicit, while the scheme’s second stage is explicit. The first stage of the proposed scheme is an extended form of the existing Runge–Kutta scheme. The scheme’s stability and consistency are also offered. In two phases, the technique achieves third-order accuracy. The method is applied to the SEIR epidemic model with a convex incidence rate. The local stability is also examined. The technique is evaluated compared to existing Euler and nonstandard finite difference methods. In terms of accuracy, the produced plots show that the suggested scheme outperforms the existing Euler and nonstandard finite difference methods. Furthermore, a neural network technique is being considered to map the relationship between time and the amount of susceptible, exposed, and infected people.

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Section: Introductionmentioning

confidence: 99%