tetano
Editor, Senior Moderator
Infect Dis Model
. 2020;5:827-838.
doi: 10.1016/j.idm.2020.10.002. Epub 2020 Oct 12.
COVID-19 prevalence estimation: Four most affected African countries
Adewale F Lukman[SUP] 1 [/SUP], Rauf I Rauf[SUP] 2 [/SUP], Oluwakemi Abiodun[SUP] 1 [/SUP], Olajumoke Oludoun[SUP] 1 [/SUP], Kayode Ayinde[SUP] 3 [/SUP], Roseline O Ogundokun[SUP] 1 [/SUP]
Affiliations
Abstract
The world at large has been confronted with several disease outbreak which has posed and still posing a serious menace to public health globally. Recently, COVID-19 a new kind of coronavirus emerge from Wuhan city in China and was declared a pandemic by the World Health Organization. There has been a reported case of about 8622985 with global death of 457,355 as of 15.05 GMT, June 19, 2020. South-Africa, Egypt, Nigeria and Ghana are the most affected African countries with this outbreak. Thus, there is a need to monitor and predict COVID-19 prevalence in this region for effective control and management. Different statistical tools and time series model such as the linear regression model and autoregressive integrated moving average (ARIMA) models have been applied for disease prevalence/incidence prediction in different diseases outbreak. However, in this study, we adopted the ARIMA model to forecast the trend of COVID-19 prevalence in the aforementioned African countries. The datasets examined in this analysis spanned from February 21, 2020, to June 16, 2020, and was extracted from the World Health Organization website. ARIMA models with minimum Akaike information criterion correction (AICc) and statistically significant parameters were selected as the best models. Accordingly, the ARIMA (0,2,3), ARIMA (0,1,1), ARIMA (3,1,0) and ARIMA (0,1,2) models were chosen as the best models for SA, Nigeria, and Ghana and Egypt, respectively. Forecasting was made based on the best models. It is noteworthy to claim that the ARIMA models are appropriate for predicting the prevalence of COVID-19. We noticed a form of exponential growth in the trend of this virus in Africa in the days to come. Thus, the government and health authorities should pay attention to the pattern of COVID-19 in Africa. Necessary plans and precautions should be put in place to curb this pandemic in Africa.
Keywords: ARIMA models; COVID-19; Exponential growth; Forecasting; Prevalence; Statistical tools; Time series model.
. 2020;5:827-838.
doi: 10.1016/j.idm.2020.10.002. Epub 2020 Oct 12.
COVID-19 prevalence estimation: Four most affected African countries
Adewale F Lukman[SUP] 1 [/SUP], Rauf I Rauf[SUP] 2 [/SUP], Oluwakemi Abiodun[SUP] 1 [/SUP], Olajumoke Oludoun[SUP] 1 [/SUP], Kayode Ayinde[SUP] 3 [/SUP], Roseline O Ogundokun[SUP] 1 [/SUP]
Affiliations
- PMID: 33073068
- PMCID: PMC7550075
- DOI: 10.1016/j.idm.2020.10.002
Abstract
The world at large has been confronted with several disease outbreak which has posed and still posing a serious menace to public health globally. Recently, COVID-19 a new kind of coronavirus emerge from Wuhan city in China and was declared a pandemic by the World Health Organization. There has been a reported case of about 8622985 with global death of 457,355 as of 15.05 GMT, June 19, 2020. South-Africa, Egypt, Nigeria and Ghana are the most affected African countries with this outbreak. Thus, there is a need to monitor and predict COVID-19 prevalence in this region for effective control and management. Different statistical tools and time series model such as the linear regression model and autoregressive integrated moving average (ARIMA) models have been applied for disease prevalence/incidence prediction in different diseases outbreak. However, in this study, we adopted the ARIMA model to forecast the trend of COVID-19 prevalence in the aforementioned African countries. The datasets examined in this analysis spanned from February 21, 2020, to June 16, 2020, and was extracted from the World Health Organization website. ARIMA models with minimum Akaike information criterion correction (AICc) and statistically significant parameters were selected as the best models. Accordingly, the ARIMA (0,2,3), ARIMA (0,1,1), ARIMA (3,1,0) and ARIMA (0,1,2) models were chosen as the best models for SA, Nigeria, and Ghana and Egypt, respectively. Forecasting was made based on the best models. It is noteworthy to claim that the ARIMA models are appropriate for predicting the prevalence of COVID-19. We noticed a form of exponential growth in the trend of this virus in Africa in the days to come. Thus, the government and health authorities should pay attention to the pattern of COVID-19 in Africa. Necessary plans and precautions should be put in place to curb this pandemic in Africa.
Keywords: ARIMA models; COVID-19; Exponential growth; Forecasting; Prevalence; Statistical tools; Time series model.