摘要
Coronavirus(COVID-19)has continued to be a global threat to public health.As the matter of fact,it needs unreserved effort to monitor the prevalence of the virus.However,applying an effective prediction of the prevalence is thought to be the fundamental requirement to effectively control the spreading rate.Time series models have extensively been considered as the convenient methods to predict the prevalence or spreading rate of the disease.This study,therefore,aimed to apply the Autoregressive Integrated Moving Average(ARIMA)modeling approach for projecting coronavirus(COVID-19)prevalence patterns in East Africa Countries,mainly Ethiopia,Djibouti,Sudan and Somalia.The data for the study were obtained from the reports of confirmed COVID-19 cases by the official website of Johns Hopkins University from 13th March,2020 to 30th June,2020.The results of the study,then,showed that in the coming four month,the number of COVID-19 positive people in Ethiopia may reach up to 56,610 from 5,846 on June 30,2020 in averagerate scenario.However,in worst case scenario forecast,the model showed that the cases will be around 84,497.The analysis further depicted that with average interventions and control scenario,cumulative number of infected persons in Djibouti,Somalia and Sudan will increase from 4,656,2,904 and 9,258 respectively at the end of June to 8,336,3,961 and 21,388,which is by the end of October,2020,after four-months.But,with insufficient intervention,the number of infected persons may grow quickly and reach up to 14,072,10,037 and 38,174 in Djibouti,Somalia and Sudan respectively.Generally,the extent of the coronavirus spreading was increased from time to time in the past four month,until 30 th June,2020,and it is expected to continue quicker than before for the coming 4-month,until the end of October,2020,in Ethiopia,Djibouti,Somalia,and Sudan and more rapidly than before in Sudan and Ethiopia,while the peak will remain unknown yet.Therefore,an effective implementation of the preventive measures and a rigorous comp
基金
The authors would like to thank the John Hopkins University for publicly releasing the updated datasets on the number of infected cases of COVID-19.
