ABSTRACT
Time series forecasting predictive modeling is used to generate the future trend of geometric progression of COVID-19 pandemic, i.e., to forecast the figures on the basis of frequentist approach. Thus, forecasting models are the act of predicting the future understanding of the current and post pandemic of diseases. In the present research intervention, we use different advanced methods of time series models for the prediction of expected cases of COVID-19 pandemic. It is done by constructing advanced simulation techniques and various mathematical iteration concepts. A stochastic time series ARIMA model is used for the estimation of the likely occurrence of disease and its endemic level. A random walk, hidden state, stochastic Markov chain model is formulated to determine the future trends at different stages of disease by using transient probability matrix. Our formulated models would increase the highest epoch of iteration techniques at the threshold level for propagating transient probability values. The constructed models show a greater and consistent accuracy and less standard error (SE) in predicting future values. The robustness of the demonstrated models is tested using sensitivity analysis. All predicted models are more reliable for the estimation of disease condition at population level. There is no complexity in data analysis for health care researchers and public health specialists worldwide.
