Analysis of Ebola virus model using intelligent computing of a new stochastic neural network

Abstract

In this research, we conducted an analysis using a stochastic neural network (SNN) approach to explore the dynamics of Ebola virus disease. Ebola virus, commonly referred to as Ebola hemorrhagic fever, is initially transmitted to humans through contact with wild and domesticated animals. Subsequently, it spreads rapidly among individuals through person-to-person transmission. To control the spread of the Ebola virus, we employed a five-compartmental mathematical model comprising the following classes: susceptible ([Formula: see text]), exposed ([Formula: see text]), infected ([Formula: see text]), quarantined ([Formula: see text]) and recovered ([Formula: see text]). We utilized the aforementioned SNN method for this purpose. The primary aim of this study is two-fold: first, to assess the convergence and accuracy of our proposed method, and second, to elucidate the impact of Ebola disease control measures. Lastly, we conducted a comparative analysis of our findings with those obtained from the numerical solvers ode15s and ode23e, demonstrating the precision and efficacy of our technique in addressing the current disease challenge.