The Validity of Autoregressive Integrated Moving Average Approach to Forecast the Spread of COVID-19 Pandemic in Africa

Africa’s first COVID-19 case was recorded in Egypt on February 14, 2020. Although it is not as expected by the World Health Organization (WHO) and other international organizations, currently a large number of Africans are getting infected by the virus. In this work, we studied the trend of the COVID-19 outbreak generally in Africa as a continent and in the five African regions separately. The study also investigated the validity of the ARIMA approach to forecast the spread of COVID-19 in Africa. The data of daily confirmed new COVID-19 cases from February 15 to October 16, 2020, were collected from the official website of Our World in Data to construct the autoregressive integrated moving average (ARIMA) model and to predict the trend of the daily confirmed cases through STATA 13 and EViews 9 software. The model used for our ARIMA estimation and prediction was (3, 1, 4) for Africa as a continent, ARIMA (3, 1, 3) for East Africa, ARIMA (2, 1, 3) for West Africa, ARIMA (2, 1, 3) for Central Africa, ARIMA (1, 1, 4) for North Africa, and ARIMA (4, 1, 5) for Southern Africa. Finally, the forecasted values were compared with the actual number of COVID-19 cases in the region. At the African level, the ARIMA model forecasted values and the actual data have similar signs with slightly different sizes, and there were some deviations at the subregional level. However, given the uncertain nature of the current COVID-19 pandemic, it is helpful to forecast the future trend of such pandemics by employing the ARIMA model.

Employment Status during the COVID-19 Pandemic: Evidence from Ethiopia.

Since the beginning of the year 2020, the world has been suffering from an unprecedented situation due to the Corona Virus Disease (COVID-19). The negative impact of COVID-19 is one of the worrisome issues across the globe. Among others, employment is one area affected during the COVID-19, which requires considerable scientific studies to identify factors affecting employment status throughout the disease crisis. Therefore, this study has mainly aimed to investigate the factors affecting the employment status during the COVID-19 pandemic in Ethiopia, taking a total of 2,396 respondents who had jobs before the COVID-19 outbreak. To achieve the stated objectives, the study has employed a binary logit regression model considering the employment status of respondents who lost their job (unemployed) and who secured their job (employed) during the pandemic. The model result indicates that females were more likely to be unemployed than males, persons living in a rural area were more likely to be unemployed than persons living in an urban area, and persons engaged in industry, service, and trade were more likely to be unemployed than people engaged in agriculture during the pandemic. Furthermore, during the pandemic, people living in the capital city of Ethiopia (Addis Ababa) were more likely to be unemployed compared to people living in the other regions of the country. Finally, based on these findings, critical recommendations were forwarded to the government and policymakers for their intervention.