Comparison of Active COVID-19 Cases per Population Using Time-Series Models

Abstract

This research explored the precision of diverse time-series models for COVID-19 epidemic detection in all the thirty-six different states and the Federal Capital Territory (FCT) in Nigeria with the maximum count of daily cumulative of confirmed, recovered and death cases as of 4 November 2020 of COVID-19 and populace of each state. A 14-multi step ahead forecast system for active coronavirus cases was built, analyzed and compared for six (6) different deep learning-stimulated and statistical time-series models using two openly accessible datasets. The results obtained showed that based on RMSE metric, ARIMA model obtained the best values for four of the states (0.002537, 0.001969.12E-058, 5.36E-05 values for Lagos, FCT, Edo and Delta states respectively). While no method is all-encompassing for predicting daily active coronavirus cases for different states in Nigeria, ARIMA model obtains the highest-ranking prediction performance and attained a good position results in other states.