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

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.