Prediction of COVID-19 Data Using Hybrid Modeling Approaches - Zhao, Weiping; Sun, Yunpeng; Li, Ying; Guan, Weimin (Frontiers in Public Health, 2022)
A major emphasis of the research is the dissemination of COVID-19 across the country's many regions and provinces. Using the present COVID-19 pandemic as a guide, the researchers suggest a hybrid model architecture for analyzing and optimizing COVID-19 data throughout the whole country. The analysis of COVID-19's exploration and death rate was done using an ARIMA model with SIR and SEIR models. The logistic model's failure to forecast the number of confirmed diagnoses and the snags of the SEIR model's too many tuning parameters are both addressed by a hybrid model method. LR, ARIMA, SVR, MLP, RNN, GRU, and LSTM were all utilised for the same purpose. RMSE, MAE, and MAPE are used to show these models. New COVID-19 cases, the number of quarantines, mortality rates, and the deployment of public self-protection measures to reduce the epidemic are all outlined in the study's findings. Government officials can use the findings to guide future choices about illness prevention and control.