Yasir, HamidDAr, Qaiser FarooqAl-Karaki, Jamal N.Nseobot, Ime RobsonEffiong, Anietie ImoDinnoo, VineshEdet, Akpan Udemeobong2021-06-182021-06-182020-12-31Hamid, Y., Dar, Q.F., Al-Karaki, J.N. et al. 2020, 'A simple and predictive model for COVID-19 evolution in large scale infected countries', Journal of Theoretical and Applied Information Technology, vol. 98, no. 24, pp. 3961-3971.1992-8645 (print)1817-3195 (online)http://hdl.handle.net/2263/80385This paper analyzes the reported COVID-19 cases in some largely affected countries around the world and accurately predicts the future values of new, death, recovery, and active COVID-19 cases for effective decision making. The objective is to provide scientific insights for decision makers in these countries to avoid higher levels of severity and large waves of infections. The data for this study were obtained from COVID- 19 stylized facts, extracted from the well-known worlddometer website and verified against the WHO’s COVID-19 Dashboard, Johns Hopkins University’s COVID-19 Dashboard, and CDC from mid of February 2020 – Early April 2020. The data covered the highest five affected countries, namely, Brazil, India, Russia, South Africa, and the USA. The data were analyzed using time series forecasting model and presented pictorially in graphs bar charts and pie charts. Based on the outcome of the analyzed data, it was concluded that the predicted COVID-19 cases will reach the peak at the end of September 2020 and if the outbreak is not controlled, the studied countries may face inflated numbers and severe shortage of medical facilities that may worsen the outbreak. The paper concludes by few important recommendations about comprehensive and necessary actions that the government and other policymakers of these countries should take in order to control spread of the virus.en© 2005 – ongoing JATIT & LLSForecastingCOVID-19 pandemicCoronavirus disease 2019 (COVID-19)Machine learningData scienceComputational intelligenceArticle