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Time Series Model for Forecasting the Number of Covid-19 Cases in the United States of America
  • serhat akay,
  • huriye akay
serhat akay
University of Health Sciences Izmir Bozyaka Education and Research Hospital
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huriye akay
University of Health Sciences Izmir Bozyaka Education and Research Hospital
Author Profile

Abstract

Background: Coronavirus disease-19 (Covid-19) had an unprecendented effect on both nations and health systems. Time series modeling using Auto-Regressive Integrated Moving Averages (ARIMA) models have been used to forecast variables extensively in statistics and econometrics. Objectives: The aim is to predict the total number of Covid-19 cases in the United States of America using ARIMA models of time-series analysis. Methods: We used time series analysis to build an ARIMA model of the total number of cases from January 21, 2020 to August 7, 2020 and used the model to predict cases in the following 7 days, from August 8, 2020 to August 14, 2020. Hyndman and Khandakar algorithm was used to select components of ARIMA models. Percentage error was used to evaluate forecasting accuracy. Results: During the model building period, 4,883,646 cases were diagnosed and during 14 days of validation period additional 313,502 new cases were added. ARIMA model with (p,d,q) components of (5,2,1) was used for forecasting. The mean percentage error of forecast was 0.09% and forecast accuracy was high in the following week. Conclusion: ARIMA models can ve used to forecast the total number of cases of Covid-19 patients in the upcoming first week.