Implementing Machine Learning Using the Neural Network for the Time Delay SIR Epidemic Model for Future Forecasting
Sayed Allamah Iqbal, Md. Golam Hafez, A.N.M. Rezaul Karim · 2023
Forecasting is significant for the stability of future danger. The dynamical analysis provides the characteristics and behavior of various kinds of physical phenomena. Nevertheless, in some of the dynamical systems, which are based on unknown parameters, it isn’t easy to obtain the model function to predict the future. On the flip side, machine learning algorithms are based on data-driven solutions and are not essential mathematical models for foresight. This work forecasts the future of infectious disease by considering the time delay SIR infections mathematical model. The data-driven dynamical analysis is made for this model by applying Neural Networks (NNs) techniques. This function-free data-driven analysis is examined for future predictions of infectious diseases. Notably, these exertions observe that the function free forecast is similar to predicting the time delay nonlinear SIR model function.