ABSTRACT
COVID-19 has infected more than 68 million people worldwide since it was first detected about a year ago. Machine learning time series models have been implemented to forecast COVID-19 infections. In this paper, we develop time series models for the Gulf Cooperation Council (GCC) countries using the public COVID-19 dataset from Johns Hopkins. The dataset set includes the one-year cumulative COVID-19 cases between 22/01/2020 to 22/01/2021. We developed different models for the countries under study based on the spatial distribution of the infection data. Our experimental results show that the developed models can forecast COVID-19 infections with high precision.
- “COVID-19: A pandemic.” [Online]. Available: https://www.who.int/emergencies/diseases/novel-coronavirus-2019. [Accessed: 12-Sep-2020].Google Scholar
- “COVID-19 Coronavirus Pandemic.” [Online]. Available: https://www.worldometers.info/coronavirus/. [Accessed: 09-Dec-2020].Google Scholar
- R. E. Jordan and P. Adab, “Covid-19: risk factors for severe disease and death,” Bmj, 2020.Google ScholarCross Ref
- F. Ahmed, N. Zviedrite, and A. Uzicanin, “Effectiveness of workplace social distancing measures in reducing influenza transmission: a systematic review,” BMC Public Health, vol. 18, 2018.Google ScholarCross Ref
- H. Tandon, P. Ranjan, T. Chakraborty, and V. Suhag, “Coronavirus (COVID-19): ARIMA based time-series analysis to forecast near future,” arXiv Prepr. arXiv2004.07859, 2020.Google Scholar
- H. H. Elmousalami and A. E. Hassanien, “Day level forecasting for Coronavirus Disease (COVID-19) spread: analysis, modeling and recommendations,” arXiv Prepr. arXiv2003.07778, 2020.Google Scholar
- F. Petropoulos and S. Makridakis, “Forecasting the novel coronavirus COVID-19,” PLoS One, vol. 15, no. 3, 2020.Google Scholar
- V. K. R. Chimmula and L. Zhang, “Time series forecasting of COVID-19 transmission in Canada using LSTM networks,” Chaos, Solitons & Fractals, 2020.Google Scholar
- H. Yonar, A. Yonar, M. A. Tekindal, and M. Tekindal, “Modeling and Forecasting for the number of cases of the COVID-19 pandemic with the Curve Estimation Models, the Box-Jenkins and Exponential Smoothing Methods,” EJMO, vol. 4, no. 2, 2020.Google ScholarCross Ref
- X. Jiang, B. Z. Zhao, and C. Jinming, “Statistical Analysis on COVID-19,” Biomed. J. Sci. Tech. Res., 2020.Google Scholar
- A. S. Ahmar and E. B. Del Val, “SutteARIMA: Short-term forecasting method, a case: Covid-19 and stock market in Spain,” Sci. Total Environ., vol. 729, 2020.Google ScholarCross Ref
- A. Hernandez-Matamoros, H. Fujita, T. Hayashi, and H. Perez-Meana, “Forecasting of COVID19 per regions using ARIMA models and polynomial functions,” Appl. Soft Comput., vol. 96, 2020.Google ScholarDigital Library
- M. H. Al-Sharoot and H. K. Alwan, “Using Time Series Models to Predict the Numbers of People Afflicted with (COVID-19) in Iraq, Saudi Arabia and United Arab Emirates,” J. Al-Qadisiyah Comput. Sci. Math., vol. 12, no. 4, 2020.Google Scholar
- K. A. Abuhasel, M. Khadr, and M. M. Alquraish, “Analyzing and forecasting COVID-19 pandemic in the Kingdom of Saudi Arabia using ARIMA and SIR models,” Comput. Intell., 2020.Google ScholarCross Ref
- “Novel Coronavirus (COVID-19) Cases Data.” [Online]. Available: https://data.humdata.org/dataset/novel-coronavirus-2019-ncov-cases. [Accessed: 09-Dec-2020].Google Scholar
- L. Ismail, H. Materwala, T. Znati, S. Turaev, and M. A. Khan, “Tailoring Time Series Models For Forecasting Coronavirus Spread: Case Studies of 187 Countries,” Comput. Struct. Biotechnol. J., 2020.Google ScholarCross Ref
- G. E. Box and D. A. Pierce, “Distribution of residual autocorrelations in autoregressive-integrated moving average time series models,” J. Am. Stat. Assoc., vol. 65, no. 332, pp. 1509–1526, 1970.Google ScholarCross Ref
- C. C. Holt, “Forecasting seasonals and trends by exponentially weighted moving averages,” Int. J. Forecast., vol. 20, no. 1, pp. 5–10, 2004.Google ScholarCross Ref
- E. S. Gardner Jr and E. McKenzie, “Forecasting trends in time series,” Manage. Sci., vol. 31, no. 10, pp. 1237–1246, 1985.Google ScholarDigital Library
- Y.-W. Cheung and K. S. Lai, “Lag order and critical values of the augmented Dickey–Fuller test,” J. Bus. Econ. Stat., vol. 13, no. 3, pp. 277–280, 1995.Google Scholar
- Forecasting COVID-19 Infections in Gulf Cooperation Council (GCC) Countries using Machine Learning
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