Forecasting COVID-19 Confirmed Cases in Ghana: A Model Selection Approach
Abstract
This study seeks to determine an appropriate statistical technique for forecasting the cumulated confirm cases of Coronavirus in Ghana. Cumulated daily data spanning from March 12, 2020, to August 04, 2020, was retrieved from the Center for Systems Science and Engineering at Johns Hopkins University. Four statistical forecasting techniques: Autoregressive Integrated Moving Average, Artificial Neural Network, Exponential smoothing and Autoregressive Fractional Integrated Moving Average were fitted to the COVID-19 series. Their respective forecast accuracy measures were compared to select the appropriate technique for forecasting the COVID-19 cases. Our findings revealed that the ARFIMA technique was a suitable statistical model for predicting COVID-19 cases in Ghana. The "best" model for forecasting is ARFIMA (2, 0.49, 4) which passed all the needed diagnostic tests. An unequal weight was estimated to derive a combined model for all four forecasting techniques. A 149-cumulated daily forecast from the "best" model and the combined model revealed that the number of confirmed COVID-19 cases would increase slightly until the end of this year.
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World Health Organization. (2020). WHO Director-General's remarks at the media briefing on 2019-nCoV on February 11 2020. Retrieved from https://www.who.int/director-general/speeches/detail/who-director-general-s-remarks-at-the-media-briefing-on-2019-ncov-on-11-february-2020
Chan, J. F.-W., Yip, C. C.-Y., To, K. K.-W., Tang, T. H.-C., Wong, S. C.-Y., Leung, K.-H., … Yuen, K.-Y. (2020). Improved Molecular Diagnosis of COVID-19 by the Novel, Highly Sensitive and Specific COVID-19-RdRp/Hel Real-Time Reverse Transcription-PCR Assay Validated In Vitro and with Clinical Specimens. Journal of Clinical Microbiology, 58(5). doi: 10.1128/jcm.00310-20
Fernandes, N. (2020). Economic Effects of Coronavirus Outbreak (COVID-19) on the World Economy. SSRN Electronic Journal. doi: 10.2139/ssrn.3557504
Worldometer. (2020). Covid-19 coronavirus pandemic. Retrieved from https://www.worldometers.info/coronavirus
Johns Hopkins University. (2020). COVID-19 Dashboard by the Center for Systems Science and Engineering at Johns Hopkins University. Retrieved from https://coronavirus.jhu.edu/map.html
McCloskey, B., Zumla, A., Ippolito, G., Blumberg, L., Arbon, P., Cicero, A., … Borodina, M. (2020). Mass gathering events and reducing further global spread of COVID-19: a political and public health dilemma. The Lancet, 395(10230), 1096–1099. doi: 10.1016/s0140-6736(20)30681-4
Papastefanopoulos, V., Linardatos, P., & Kotsiantis, S. (2020). COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population. Applied Sciences, 10(11), 3880. doi: 10.3390/app10113880
Maleki, M., Mahmoudi, M. R., Wraith, D., & Pho, K.-H. (2020). Time series modelling to forecast the confirmed and recovered cases of COVID-19. Travel Medicine and Infectious Disease, 37, 101742. doi: 10.1016/j.tmaid.2020.101742
Khan, F. M., & Gupta, R. (2020). ARIMA and NAR based prediction model for time series analysis of COVID-19 cases in India. Journal of Safety Science and Resilience, 1(1), 12–18. doi: 10.1016/j.jnlssr.2020.06.007
Yonar, H., Yonar, A., Tekindal, M., & Tekindal, M. (2020). 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. Eurasian Journal of Medicine and Oncology, 4(2), 160–165. doi: 10.14744/ejmo.2020.28273
Balah, B., & Djeddou, M. (2020). Forecasting COVID-19 new cases in Algeria using Autoregressive fractionally integrated moving average Models (ARFIMA). doi: 10.1101/2020.05.03.20089615
Holt, C. C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting, 20(1), 5–10. doi: 10.1016/j.ijforecast.2003.09.015
Hyndman, R., Koehler, A., Ord, K., & Snyder, R. (2008). Forecasting with Exponential Smoothing. Springer Series in Statistics. doi: 10.1007/978-3-540-71918-2
Shumway, R. H., & Stoffer, D. S. (2016). Time Series Analysis and Its Applications (4th ed.). Retrieved from https://www.stat.pitt.edu/stoffer/tsa4/tsa4.pdfArticle Metrics
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Copyright (c) 2021 Sampson Twumasi-Ankrah, Michael Owusu, Simon Kojo Appiah, Wilhemina Adoma Pels, Doris Arthur
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