Neural network modeling of Lassa fever spread and disability effects

Lassa fever is one of the viral hemorrhagic illnesses produced through the Lassa virus and may be transmitted to individuals via contact with diseased rodents or their feces and urine. The current study presents the solutions to the nonlinear zoonotic spread of Lassa virus fever leading to disabilities model by designing a supervised neural network. The model is divided into the human (susceptible, severely infected, mildly infected, and recovered) and rodent (susceptible, infected) populations. The dynamic structure of one class to the other performs the zoonotic spread of Lassa virus fever model makes nonlinear. A dataset via Runge-Kutta is obtained, which decreases the mean square error by dividing the training, validation, and testing with 78%, 12% and 10%. The proposed neural network construction is designed with a single hidden layer keeping 20 neurons and sigmoid function, while Bayesian regularization is used for the optimization. The designed neural network scheme’s reliability is obtained using different tests including best training values, correlation performances, state transition, and error histogram. The correctness of the designed neural network solver is observed through the matching of the proposed and Runge-Kutta outputs, and insignificant absolute error calculated as 10− 02 to 10− 03.