Modeling and forecasting number of confirmed and death caused COVID-19 in IRAN: A comparison of time series forecasting methods

Abstract

Background The COVID-19 pandemic conditions are still prevalent in Iran and other countries and the monitoring system is gradually discovering new cases every day. Therefore, it is a cause for concern around the world, and forecasting the number of future patients and death cases, although not entirely accurate, helps the governments and health-policy makers to make the necessary decisions and impose restrictions to reduce prevalence. Methods In this study, we aimed to find the best model for forecasting the number of confirmed and death cases in Iran. For this purpose, we applied nine models including NNETAR, ARIMA, Hybrid, Holt-Winter, BSTS, TBATS, Prophet, MLP, and ELM network models. The quality of forecasting models is evaluated by three performance metrics, RMSE, MAE, and MAPE. The best model is selected by the lowest value of performance metrics. Then, the number of confirmed and the death cases forecasted for the 30 next days. The used data in this study is the absolute number of confirmed, death cases from February 20 to August 15, 2020. Results Our findings suggested that based on existing data in Iran, the suitable model with the lowest performance metrics for confirmed cases data obtained MLP network and the Holt-Winter model is the suitable model for forecasting death cases in the future. These models forecasted on September 14, 2020, we will have 2,484 new confirmed and 114 new death cases of COVID-19. Conclusion According to the results of this study and the existing data, we concluded that the MLP and Holt-Winter models had the lowest error in forecasting in comparison to other methods. Some models had fitted poorly in the test phase and this is because many other factors that are either not available or have been ignored in this study and can affect the accuracy of forecast results. Based on the trend of data and forecast results, the number of confirmed cases and death cases are almost constant and decreasing, respectively. However, due to disease progression and ignoring the recommendations and protocols of the Ministry of health, there is a possibility of re-emerging this disease more seriously in Iran and this requires more preventive care.

Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy

“…From the beginning of 2020, an increasing body of literature has employed various approaches to forecast the spread of the COVID-2019 outbreak [ 9 , 22 , 26 , 58 , 73 , 78 , 79 , 83 , 85 ]. The most frequently used were ARIMA models [ 3 , 8 , 14 , 62 ], ETS models [ 13 , 44 ], artificial neural network (ANN) models [ 55 , 75 ], TBATS models [ 68 , 71 ], models derived from the susceptible–infected–removed (SIR) basic approach [ 22 , 26 , 58 , 78 , 85 ], and hybrid models [ 15 , 29 , 68 , 69 ]. The implementation and comparison of these approaches—with the exception of mechanistic–statistical models (such as SIR)—represents the core of this paper.…”

“…Talkhi et al [ 71 ] attempted to forecast the number of COVID-19 confirmed infections and deaths in Iran between August 15, 2020, and September 14, 2020, using several single and hybrid models. The extreme learning machine (ELM) and hybrid ARIMA–NNAR models were the most suitable for forecasting confirmed cases, while the Holt–Winters (HW) approach outperformed the others in predicting death cases.…”

Comparison between Mexican and International Medical Graduates’ scores in the ENARM Competing for Clinical Specialities in Mexico during 2012-2019: Data Visualization, Trends and Forecasting Analyses

“…This method is used for forecasting the univariate time series when the data might have both linear trend and seasonal pattern. In Holt-Winters exponential smoothing, recent observations are given relatively more weight than older observations; it is suitable for short-term forecasting and uses the maximum likelihood function for estimating parameters [21]. We calculated models that captured the evolving trend or seasonality of the data and extrapolated them into the future five-year period with 95% confidence prediction intervals.…”

Modelling and forecasting of growth rate of new COVID-19 cases in top nine affected countries: Considering conditional variance and asymmetric effect

“…Talkhi et al [35] forecast the number of confirmed and death caused COVID-19 in Iran using nine different time series models, i.e. NNETAR, ARIMA, Hybrid, Holt-Winter, BSTS, TBATS, Prophet, MLP, and ELM network models.…”