Numerical computing with Levenberg–Marquardt backpropagation networks for nonlinear SEIR Ebola virus epidemic model

Abstract

In this study, a new computing technique is introduced to solve the susceptible-exposed-infected-and-recovery (SEIR) Ebola virus model represented with the system of ordinary differential equations through Levenberg–Marquardt backpropagation neural networks. The dynamics of the SEIR model are examined by the variation in different parameters, such as the increase in the susceptible rate while keeping other parameters fixed, such as the natural death rate of susceptibility, susceptible exposed rate, infected exposed rate, and infected to recovered rate; the four types of infected rates, namely, the natural mortality rate, rate of exposed death due to the disease, natural infected mortality rate, and rate of infected death due to the disease; and the rate of natural mortality of the recovered. The datasets for the SEIR nonlinear system for measuring the effects of Ebola virus disease spread dynamics are generated through the Runge–Kutta method for each scenario. The efficiency of the proposed computing technique—LMBNNs—is analyzed through absolute deviation, mean square error, learning curves, histogram analysis, and regression metrics, which provides a way for validation, testing, and training through the scheme.

Integrated intelligence of inverse multiquadric radial base neuro‐evolution for radiative MHD Prandtl–Eyring fluid flow model with convective heating

“…ANNs-based stochastic numerical techniques are used quite successfully to solve nonlinear systems of ODEs involved in real-world problems to a large extent for rapid convergence in the fields of biomedical, engineering, welding, finance, material science, medicine and geology. Some recent applications of ANNs in the fields of applied science are including the SEIR Ebola model [48], COVID-19 [49,50], squeezing flow paradigm [51], delay model [52], computer virus handling [53], spline model [54], corneal model [55], Lassa fever model [56], Falkner-Skan model [57], VPSA model [58], tuberculosis model [59], hydrogen purification model [60], nanofluids [61], nanoparticles [62], hybrid nanofluids [63], and induction motor [64]. The above-mentioned literature interprets the successful application of ANNs but as per the author's knowledge, ANNs-IMQ algorithm is never applied to scrutinize the MHD Prandtl-Eyring fluid flow model with radiation and convecting heating impacts over a stretched sheet.…”

Computational and stability analysis of Ebola virus epidemic model with piecewise hybrid fractional operator

“…To test the equilibrium’s overall stability, they next used the LaSalle invariance rule and a powerful Lyapunov function. A supervised learning-based computer paradigm [ 19 ] to examine a nonlinear SEIR model of EVD. In the work of [ 20 ], a mathematical model known as the fractional SIRI model which incorporates time delay is investigated.…”