Trend Analysis and Forecasting the Spread of COVID-19 Pandemic in Ethiopia Using Box–Jenkins Modeling Procedure

Abstract

Introduction: COVID-19, which causes severe acute respiratory syndrome, is spreading rapidly across the world, and the severity of this pandemic is rising in Ethiopia. The main objective of the study was to analyze the trend and forecast the spread of COVID-19 and to develop an appropriate statistical forecast model. Methodology: Data on the daily spread between 13 March, 2020 and 31 August 2020 were collected for the development of the autoregressive integrated moving average (ARIMA) model. Stationarity testing, parameter testing and model diagnosis were performed. In addition, candidate models were obtained using autocorrelation function (ACF) and partial autocorrelation functions (PACF). Finally, the fitting, selection and prediction accuracy of the ARIMA models was evaluated using the RMSE and MAPE model selection criteria. Results: A total of 51,910 confirmed COVID-19 cases were reported from 13 March to 31 August 2020. The total recovered and death rates as of 31 August 2020 were 37.2% and 1.57%, respectively, with a high level of increase after the mid of August, 2020. In this study, ARIMA (0, 1, 5) and ARIMA (2, 1, 3) were finally confirmed as the optimal model for confirmed and recovered COVID-19 cases, respectively, based on lowest RMSE, MAPE and BIC values. The ARIMA model was also used to identify the COVID-19 trend and showed an increasing pattern on a daily basis in the number of confirmed and recovered cases. In addition, the 60-day forecast showed a steep upward trend in confirmed cases and recovered cases of COVID-19 in Ethiopia. Conclusion: Forecasts show that confirmed and recovered COVID-19 cases in Ethiopia will increase on a daily basis for the next 60 days. The findings can be used as a decision-making tool to implement health interventions and reduce the spread of COVID-19 infection.

Assessing the dynamic impacts of non-pharmaceutical and pharmaceutical intervention measures on the containment results against COVID-19 in Ethiopia

“…Eticha employed a case-based autoregressive integrated moving average model to predict the new cases of COVID-19 [ 35 ]. In a study done by Gebretensae and Asmelash, the researchers used Box–Jenkins modeling framework, i.e., ARIMA (p, d, q), to forecast the trend of COVID-19 spread in Ethiopia [ 36 ]. Gebremeskel et al applied a compartmental epidemic model to predict the transmission dynamics of COVID-19 in Ethiopia [ 37 ].…”

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

“…Gebretensae YA and Asmelash D analyzed trends and forecasted the spread of COVID-19 for developing an appropriate statistical forecast model using the COVID-19 dataset that was reported from the Ethiopian Public Health Institute (EPHI) from 13 March to 31 August 2020 [ 18 ]. The auto-regressive integrated moving average (ARIMA) model was used to forecast the confirmed cases, recovered cases, and deaths in Ethiopia.…”

“…Seven days after detection of the initial cluster, daily COVID-19 confirmed cases climbed into the 100s. Daily case counts dropped to below 2 of 18 10 per day and the first wave was over by the end of April 2020. The government responded to the first outbreak by declaring a strict policy of disease outbreak control and increasing the level of disease control at international points of entry such as airports, seaports, and land borders.…”

A Survey of Recent Studies on COVID-19 Outbreak Prediction Using Statistical and Machine Learning Methods

“…Gebretensae et al [14] predicted the future trend of COVID-19 in Ethiopia using ARIMA. The dataset used in this study was obtained from the official website of the Ethiopian Public Health Institute and consisted of confirmed, recovered, and death cases in Ethiopia.…”