Dynamical behavior of a stochastic highly pathogenic avian influenza A (HPAI) epidemic model via piecewise fractional differential technique

Abstract

<abstract><p>In this research, we investigate the dynamical behaviour of a HPAI epidemic system featuring a half-saturated transmission rate and significant evidence of crossover behaviours. Although simulations have proposed numerous mathematical frameworks to portray these behaviours, it is evident that their mathematical representations cannot adequately describe the crossover behaviours, particularly the change from deterministic reboots to stochastics. Furthermore, we show that the stochastic process has a threshold number $ {\bf R}_{0}^{s} $ that can predict pathogen extermination and mean persistence. Furthermore, we show that if $ {\bf R}_{0}^{s} > 1 $, an ergodic stationary distribution corresponds to the stochastic version of the aforementioned system by constructing a sequence of appropriate Lyapunov candidates. The fractional framework is expanded to the piecewise approach, and a simulation tool for interactive representation is provided. We present several illustrated findings for the system that demonstrate the utility of the piecewise estimation technique. The acquired findings offer no uncertainty that this notion is a revolutionary viewpoint that will assist mankind in identifying nature.</p></abstract>

Novel insights for a nonlinear deterministic-stochastic class of fractional-order Lassa fever model with varying kernels

“…When juxtaposed with deterministic designs, stochastic algorithms produce superior outcomes because, after multiple runs of operation, a distribution of the anticipated outcomes, including the mean of ailments at any moment , is capable of being developed, while deterministic frameworks produce one estimated value 39 , 46 . There are plenty of strategies and techniques for examining stochastic systems 47 .…”