<p>Estimating the Prevalence and Mortality of Coronavirus Disease 2019 (COVID-19) in the USA, the UK, Russia, and India</p>

Abstract

The aim of this study is to apply the advanced error-trend-seasonal (ETS) framework to forecast the prevalence and mortality series of COVID-19 in the USA, the UK, Russia, and India, and the predictive performance of the ETS framework was compared with the most frequently used autoregressive integrated moving average (ARIMA) model. Materials and Methods: The prevalence and mortality data of COVID-19 in the USA, the UK, Russia, and India between 20 February 2020 and 15 May 2020 were extracted from the WHO website. Then, the data subsamples between 20 February 2020 and 3 May 2020 were treated as the training horizon, and the others were used as the testing horizon to construct the ARIMA models and the ETS models. Results: Based on the model evaluation criteria, the ARIMA (0,2,1) and ETS (M,MD,N), sparse coefficient ARIMA (0,2,(1,6)) and ETS (A,AD,M), ARIMA (1,1,1) and ETS (A,MD, A), together with ARIMA (2,2,1) and ETS (A,M,A) specifications were identified as the preferred ARIMA and ETS models for the prevalence data in the USA, the UK, Russia, and India, respectively; the ARIMA (0,2,1) and ETS (M,A,M), ARIMA (0,2,1) and ETS (M,A, N), ARIMA (0,2,1) and ETS (A,A,N), coupled with ARIMA (0,2,2) and ETS (M,M,N) specifications were selected as the optimal ARIMA and ETS models for the mortality data in these four countries, respectively. Among these best-fitting models, the ETS models produced smaller forecasting error rates than the ARIMA models in all the datasets. Conclusion: The ETS framework can be used to nowcast and forecast the long-term temporal trends of the COVID-19 prevalence and mortality in the USA, the UK, Russia, and India, and which provides a notable performance improvement over the most frequently used ARIMA model. Our findings can aid governments as a reference to prepare for and respond to the COVID-19 pandemic both in restricting the transmission of the disease and in lowering the disease-related deaths in the upcoming days.

Time Series Analysis and Forecasting of the Hand-Foot-Mouth Disease Morbidity in China Using An Advanced Exponential Smoothing State Space TBATS Model

“…Given its attractive applications of the TBATS model, 21 it appeared therefore that this model can be adopted to analyze the HFMD incidence in other countries or regions, even for all the time series, and yet additional work on the current topic is still required to verify its versatility. Additionally, of note, current studies have demonstrated the potential of some new models to perform the long-term forecasting and to handle the nonlinear issue, such as ETS model, 44 long short-term memory neural network (LSTM), 9 and neural network nonlinear autoregression (NNAR) model. 45 Consequently, further investigations into the predictive performance comparison between the TBATS model and the above models are needed.…”

Estimating the Long-Term Epidemiological Trends and Seasonality of Hemorrhagic Fever with Renal Syndrome in China

“…Therefore, the current study, for the first time, focused on the exploration of the utility and adequacy in nowcasting and forecasting the long-term epidemiological trends and seasonality of HFRS, and its predictive accuracy level was compared with the most versatile SARIMA method in the field of time series modeling. 11 , 13 , 16 , 33 It was discovered that the advanced TBATS methods get a more clear perspective of capturing the dynamic dependency structure in the spreading of HFRS over the SARIMA methods in a series of modeling experiments. What’s more, the long-term forecasting results to emerge from the advanced TBATS method remained reliable and robust with the increase of prediction time windows, despite a slight rise in the values of measurement metrics, including MAD, MAPE, RMSE, RMSPE, and MER.…”

“… 46 Our recent work suggests that the Error-Trend-Seasonal framework (ETS) technique has many attractive applications in analyzing the long-run temporal behaviors. 33 , 46 Accordingly, we further established the ETS approach based on the HFRS incidence data to nowcast and forecast its epidemics, and the resulting results indicated that the TBATS method is also more useful and robust for describing the long-term trends of HFRS than the ETS technique, except that in 60-step ahead forecast ( Table S8 ). Also, we used the HFRS incidence data in Liaoning province and Shanxi province (which were hit the hardest with HFRS in the past decades in China) to validate the forecasting reliability of TBATS model.…”

“…32 Of all the plausible parameters, those that maximize the R-square (R 2 ) and stationary R 2 , and minimize the Normalized Schwarz’s Bayesian Information Criterion (NBIC) were first selected. 33 Subsequently, the t -test was used to investigate the statistical significance of the identified parameters. 29 Next, autocorrelogram, partial autocorrelogram, and Ljung-Box Q test were applied to the residuals to investigate whether the produced forecasting errors behaved like a white noise series.…”

Forecasting COVID-19 pandemic in Alberta, Canada using modified ARIMA models

“…Since the comparations included subsite models, the “best” models specified by iterations had higher possibility to be the real best models. To my knowledge, at least for COVID-19 studies, very few researchers [20] considered subset models (sparse coefficient ARIMA) when they specified their best ARIMA models. With the traditional ARIMA modeling technique, manually specifying lags and model type for the best subsite model is difficult, or sometimes even impossible.…”