Modeling and Forecasting the Spread of Novel Corona Virus Disease (COVID-19) Number of New Cases in Ethiopia

  • Tesfaye Denano Sorre Department of Statistics, Wolaita Sodo University, Ethiopia
Keywords: COVID-19 new cases, ARIMA model, Forecasting, Ethiopia


The novel Corona virus disease (COVID-19) was first found in Wuhan, China in December 2019, but later spread to other parts of the world. The World Health Organization (WHO) defines a pandemic as “the worldwide spread of a new disease”. The main aim of this study was to forecast the spread of COVID-19 new cases in Ethiopia. In this study we used the data of confirmed Coronavirus Disease (COVID-19) cases reported daily from March 13, 2020 until April 4, 2021 that were obtained from ministry of health Ethiopia and WHO. ARIMA model was applied to examine the data used to generate 60 days forecast. ARIMA (0, 1, 1) estimates the number of confirmed COVID-19 new cases based on a 95 percent confidence interval between March 13, 2020 and April 4, 2021. In the next two months, the maximum expected new cases per day were 2353, while the minimum estimate was 2042 cases per day. Furthermore, by the end of May 2021, the total number of confirmed COVID-19 predicted cases could reach about 343,563. Although more data are needed to have a more detailed prevision, the spread of the virus seems to be increasing according to time series plot. However the forecasting ARIMA (0, 1, 1) model shows fast increasing within a predicted time period in Ethiopia. According to the study results, the government and all concerned bodies should work to stop the pandemic from spreading through Ethiopia. Increasing the regular test potential for Covid-19 by increasing test sites and implementing successful and efficient behavioral change and communication interventions should be considered throughout the country.


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How to Cite
Sorre , T. D. (2021). Modeling and Forecasting the Spread of Novel Corona Virus Disease (COVID-19) Number of New Cases in Ethiopia. International Journal of Applied Sciences: Current and Future Research Trends , 10(01), 1-15. Retrieved from