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http://dx.doi.org/10.6109/jkiice.2021.25.12.1756

Prediction of Covid-19 confirmed number of cases using ARIMA model  

Kim, Jae-Ho (Department of Computer Science, The University of Suwon)
Kim, Jang-Young (Department of Computer Science, The University of Suwon)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.12, 2021 , pp. 1756-1761 More about this Journal
Abstract
Although the COVID-19 outbreak that occurred in Wuhan, Hubei around December 2019, seemed to be gradually decreasing, it was gradually increasing as of November 2020 and June 2021, and estimated confirmed cases were 192 million worldwide and approximately 184 thousand in South Korea. The Central Disaster and Safety Countermeasures Headquarters have been taking strong countermeasures by implementing level 4 social distancing. However, as the highly infectious COVID-19 variants, such as Delta mutation, have been on the rise, the number of daily confirmed cases in Korea has increased to 1,800. Therefore, the number of cumulative confirmed COVID-19 cases is predicted using ARIMA algorithms to emphasize the severity of COVID-19. In the process, differences are used to remove trends and seasonality, and p, d, and q values are determined and forecasted in ARIMA using MA, AR, autocorrelation functions, and partial autocorrelation functions. Finally, forecast and actual values are compared to evaluate how well it was forecasted.
Keywords
Covid-19; Moving average(MA); Auto-regressive(AR); Auto regressive moving average(ARMA); Auto regressive integrated moving average(ARIMA);
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1 S. Siami-Namini, N. Tavakoli, and A. S. Namin, "A comparison of ARIMA and LSTM in forecasting time series," 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1394-1401, Dec. 2018.
2 Korea Centers for Disease Control and Prevention, COVID pademic [Internet]. Available: http://ncov.mohw.go.kr/tcmBoardView.do?brdId=&brdGubun=&dataGubun=&ncvContSeq=366394&contSeq=366394&board_id=140&gubun=BDJ.
3 Centers for Disease Control and Provention. SARS-CoV-2 Variant Classifications and Definitions [Internet]. Available: https://www.cdc.gov/coronavirus/2019-ncov/variants/variant-info.html.
4 Our World in data, Statistics and ResearchCoronavirus (COVID-19) Vaccinations [Internet]. Available: https://ourworldindata.org/covid-vaccinations.
5 Coronavirus Infectious Disease-19 (COVID-19), social distancing step 4 [Internet]. Available: http://ncov.mohw.go.kr/shBoardView.do?brdId=6&brdGubun=64&ncvContSeq=5619.
6 D. H. Kim, M. W. Kim, B. J. Lee, K. T. Kim, and H Y Youn, "Data Flow Prediction Scheme using ARIMA Model," Proceedings of the 2018 Korean Society for Computer and Information Sciences Summer Conference, vol. 26, no. 2, pp. 141-142, 2018.
7 D. Serge and L. Sophie, "Characterization of the partial autocorrelation function of nonstationary time series," Journal of Multivariate Analysis, vol. 87, no. 1, pp. 46-59, Oct. 2003.   DOI
8 P. Giovanni, P. Sonia, and C. Patrizia, "Dynamic Linear Models with R," Springer Science & Business Media, pp. 31-84, May. 2009.
9 Kaggle data set. covid19-data-from-john-hopkins-university [Internet]. Available: https://www.kaggle.com/antgoldbloom/covid19-data-from-john-hopkins-university.