Forecasting COVID-19 Disease Cases Using the SARIMA-NNAR Hybrid Model
Abstract
Background: COVID-19 is a new disease that is associated with high morbidity that has spread around the world. Credible estimating is crucial for control and prevention. Nowadays, hybrid models have become popular, and these models have been widely implemented. Better estimation accuracy may be attained using time-series models. Thus, our aim is to forecast the number of COVID-19 cases with time-series models.
Objective: Using time-series models to predict deaths due to COVID-19.
Design: SARIMA, NNAR, and SARIMA-NNAR hybrid time series models were used using the COVID-19 information of the Republic of Turkey Health Ministry.
Participants: We analyzed data on COVID-19 in Turkey from March 11, 2020 to February 22, 2021.
Main Measures: Daily numbers of COVID-19 confirmed cases and deaths.
Materials and methods: We fitted a seasonal autoregressive integrated moving average (SARIMA)–neural network nonlinear autoregressive (NNAR) hybrid model with COVID-19 monthly cases from March 11, 2020, to February 22, 2021, in Turkey. Additionally, a SARIMA model, an NNAR model, and a SARIMA–NNAR hybrid model were established for comparison and estimation.
Results
The RMSE, MAE, and MAPE values of the NNAR model were obtained the lowest in the training set and the validation set. Thus, the NNAR model demonstrates excellent performance whether in fitting or forecasting compared with other models.
Conclusions
The NNAR model that fits this study is the most suitable for estimating the number of deaths due to COVID-19. Hence, it will facilitate the prevention and control of COVID-19.
Keywords
COVID-19, time series, SARIMA, NNAR, forecasting, R Programming
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