Numerical Performances of the SIR Dynamical Prototype with the Hospital Bed Impacts Using Artificial Neural Network

Abstract

This study is conducted to check the behavior of SEIR model based on the Levenberg-Marquardt backpropagation (LMQB) along with the neural networks (NN) i.e., LMQB neural networks describes the mathematical evaluation of SEIR model with available number of bed in hospitals. The epidemic SEIR model works on four dimensions, where S is the number of susceptible people who are admitted in hospital. E is represented the number of exposed persons, I shows the infected persons and R indicates the recovered persons respectively. The numerical findings are evaluated through LMQB neural networks. These findings are measured for the four dimensions of SEIR model by taking the data samples, training of dataset, authentication, and testing the results. The results show the metrics are selected as 70% for dataset training, 18% for confirmation and 12% for testing. The theoretical analysis is presented to show the numerical modeling. The SEIR model outcome is described using LMQB neural networks to overcome the mean square error (MSE). The numerical results are described using the LMQB neural networks through the MSE, error histograms (EHs), state transitions (STs), regression and correlation for attaining the correctness, consistency, capability, and productivity.