Forecasting COVID-19 cases in Egypt using ARIMA-based time-series analysis
Eurasian Journal of Medicine and Oncology
; 5(2):123-131, 2021.
Article
in English
| EMBASE | ID: covidwho-2325976
ABSTRACT
Objectives:
The World Health Organization declared the novel coronavirus (COVID-19) outbreak a public health emer-gency of international concern on January 30, 2020. Since it was first identified, COVID-19 has infected more than one hundred million people worldwide, with more than two million fatalities. This study focuses on the interpretation of the distribution of COVID-19 in Egypt to develop an effective forecasting model that can be used as a decision-making mechanism to administer health interventions and mitigate the transmission of COVID-19. Method(s) A model was developed using the data collected by the Egyptian Ministry of Health and used it to predict possible COVID-19 cases in Egypt. Result(s) Statistics obtained based on time-series and kinetic model analyses suggest that the total number of CO-VID-19 cases in mainland Egypt could reach 11076 per week (March 1, 2020 through January 24, 2021) and the number of simple regenerations could reach 12. Analysis of the ARIMA (2, 1, 2) and (2, 1, 3) sequences shows a rise in the number of COVID-19 events. Conclusion(s) The developed forecasting model can help the government and medical personnel plan for the imminent conditions and ensure that healthcare systems are prepared to deal with them.Copyright © 2021 by Eurasian Journal of Medicine and Oncology.
arima; Coronavirus; covid-19; Egypt; Forecast; Pandemic; article; clinical decision making; coronavirus disease 2019; data collection method; disease transmission; forecasting; government; health care; health care personnel; health care system; human; kinetics; major clinical study; mathematical model
Full text:
Available
Collection:
Databases of international organizations
Database:
EMBASE
Type of study:
Experimental Studies
/
Prognostic study
Language:
English
Journal:
Eurasian Journal of Medicine and Oncology
Year:
2021
Document Type:
Article
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