Open AccessComparison of Active COVID-19 Cases Per Population Using Time-Series Models
Publication year - 2022
Publication title -
international journal of e-health and medical communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.151
H-Index - 12
eISSN - 1947-3168
pISSN - 1947-315X
DOI - 10.4018/ijehmc.20220701oa06
Subject(s) - covid-19 , autoregressive integrated moving average , statistics , series (stratigraphy) , ranking (information retrieval) , metric (unit) , time series , population , mathematics , geography , demography , econometrics , computer science , medicine , artificial intelligence , engineering , operations management , virology , sociology , paleontology , disease , pathology , outbreak , infectious disease (medical specialty) , biology
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.