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.

Keywords:

• ARIMA models
• COVID-19
• forecast
• trend
• Ethiopia

Abbreviations

ACF, autocorrelation function; ANFIS, adaptive neuro-fuzzy inference system; ADF, augmented Dickey–Fuller test; ARIMA, autoregressive integrated moving average; BIC, Bayesian information criteria; PACF, partial autocorrelation function; CDC, communicable disease control; CI, confidence interval; CMC, composite Monte-Carlo; CUBIST, cubist regression; COVID-19, corona virus disease 2019; EPHI, Ethiopia Public Health Institute; MAPE, mean absolute percentage error; RF, random forest; RMSE, root mean squared error; SPSS, Statistical Package for Social Science; VMD, variational mode decomposition; WHO, World Health Organization.

Data Sharing Statement

All daily series of open-source data that support the findings of this study are also available from regular updates by the Ethiopian Public Health Institute: https://www.ephi.gov.et/[accessed on 10/01/2020].

Consent for Publication

All authors provided written informed consent to publish this study.

Acknowledgments

The authors gratefully acknowledge the Ethiopian Public Health Institute for publicly releasing updated datasets on the number of confirmed, recovered and death COVID-19 cases in Ethiopia. And we acknowledged the feedbacks from participants of the 32nd Ethiopian Public Health Association annual conference.

Author Contributions

Both authors made substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; took part in drafting the article or revising it critically for important intellectual content; agreed to submit to the current journal; gave final approval of the version to be published; and agreed to be accountable for all aspects of the work.

Disclosure

The authors reported no conflicts of interest for this work.

Additional information

Funding

The authors received no specific funding for this work.