Meningitis epidemic model analysis using artificial neural networks: A Levenberg-Marquardt backpropagation neural network approach

Motivation The current study provides the numerical results of a mathematical Neisseria meningitis system, the bacterium accountable for producing meningitis by engaging the Levenberg-Marquardt backpropagation neural network. The meningitis dynamics inside the population are categorized into five different groups: susceptible ( S ), vaccinated ( V ), carrier ( C ), infectious ( I ), and recovered ( R ). Method A reference dataset is produced by relating the numerical Runge-Kutta scheme for the meningitis system. The reference dataset assists as an essential reserve for the training, validation, and testing segments of the proposed process across three dissimilar variations. Results The numerical results gotten from the approach are thoroughly associated with those resultant from the Runge-Kutta method to measure the correctness, accuracy, and competence of the designed practice. To confirm authentication of the designed methodology, it is employed numerous assessment actions, e.g., the error histogram, mean square error, regression, and fitness designs. Novelty The proposed structure has been applied for the first time to solve the Neisseria meningitis.