Predicting COVID-19 Cases using Some Statistical Models: An Application to the Cases Reported in China Italy and USA

Mostafa Salaheldin Abdelsalam Abotaleb
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Mostafa Salaheldin Abdelsalam Abotaleb: Sigma Academic building, South Ural State University, Chelyabinsk, Russia

Academic Journal of Applied Mathematical Sciences, 2020, vol. 6, issue 4, 32-40

Abstract: Today, the new coronavirus disease (COVID-19) is a global epidemic that spreads rapidly among individuals in most countries around the world and, therefore, becomes the greatest worldwide threat. The aim of this study is to find the best predictive models for the confirmation of daily situations in countries with a large number of confirmed cases. The study was conducted on the countries that recorded the highest infection rate, namely China, Italy and the United States of America. The second goal is using predictive models to get more prepared in terms of health care systems. In this study, predictions were made through statistical prediction models using the ARIMA and exponential growth model. The results indicate that the exponential growth model is better than ARIMA models for forecasting the COVID-19 cases.

Keywords: ARIMA model; Box-Jenkins approach; Coronavirus; Forecasting; Holt’s linear trend. (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:arp:ajoams:2020:p:32-40

DOI: 10.32861/ajams.64.32.40

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