SARIMA Model Forecasting Performance of the COVID-19 Daily Statistics in Thailand during the Omicron Variant Epidemic

Abstract

This study aims to identify and evaluate a robust and replicable public health predictive model that can be applied to the COVID-19 time-series dataset, and to compare the model performance after performing the 7-day, 14-day, and 28-day forecast interval. The seasonal autoregressive integrated moving average (SARIMA) model was developed and validated using a Thailand COVID-19 open dataset from 1 December 2021 to 30 April 2022, during the Omicron variant outbreak. The SARIMA model with a non-statistically significant p-value of the Ljung–Box test, the lowest AIC, and the lowest RMSE was selected from the top five candidates for model validation. The selected models were validated using the 7-day, 14-day, and 28-day forward-chaining cross validation method. The model performance matrix for each forecast interval was evaluated and compared. The case fatality rate and mortality rate of the COVID-19 Omicron variant were estimated from the best performance model. The study points out the importance of different time interval forecasting that affects the model performance.

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“… 16 Furthermore, the mean absolute percentage error (MAPE) evaluated for the goodness-of-fit models, which is categorized as highly accurate forecasting (<10%), good forecasting (10%–20%), reasonable forecasting (20%–50%), and inaccurate forecasting (>50%). 16 , 24 , 25 In order to test the accuracy in prediction of model, we used absolute percentage error (APE), which was calculated by 16 : …”

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“…There are several time-series models, such as ARIMA, exponential smoothing, GARCH, VAR, and prophet models. However, ARIMA is one of the most classic time-series models and has been widely used to predict infectious diseases, including COVID-19[ 50 ], hepatitis B [ 28 ], tuberculosis [ 19 ], human brucellosis [ 51 ], HFMD [ 52 ], and pertussis [ 53 ]. SARIMA is a powerful forecasting tool in public health informatics [ 50 ] that provides an important reference for surveillance and early warning of infectious diseases.…”

“…However, ARIMA is one of the most classic time-series models and has been widely used to predict infectious diseases, including COVID-19[ 50 ], hepatitis B [ 28 ], tuberculosis [ 19 ], human brucellosis [ 51 ], HFMD [ 52 ], and pertussis [ 53 ]. SARIMA is a powerful forecasting tool in public health informatics [ 50 ] that provides an important reference for surveillance and early warning of infectious diseases. Several studies have confirmed that SARIMA achieves better predictive performance in forecasting the incidence of HFMD [ 16 , 53 – 56 ].…”

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“…The pandemic has also generated extensive datasets related to COVID-19. The vast amount of COVID-19 data presents numerous health informatics research opportunities [ 4 , 5 , 6 ].…”

“…A dramatic increase in the number of COVID-19 cases without early information or notification may lead to a shortage of medical facilities and personnel, which may result in a disaster. Therefore, the study by Duangchaemkarn et al [ 6 ] developed a seasonal autoregressive integrated moving average (SARIMA) model to predict the number of COVID-19 cases in Thailand. Their study presented the most accurate SARIMA model to forecast at least 28 days ahead of the current outbreak in Thailand, especially for the daily COVID-19 confirmed cases.…”