Sains Malaysiana 50(4)(2021): 1131-1142

http://doi.org/10.17576/jsm-2021-5004-23

Prediction of Epidemic Trends in COVID-19 with Mann-Kendall and Recurrent Forecasting-Singular Spectrum Analysis

(Ramalan Kecenderungan Wabak pada COVID-19 dengan Mann-Kendall dan Ramalan Berulang-Analisis Spektrum Tunggal)

SHAZLYN MILLEANA SHAHARUDIN¹*, SHUHAIDA ISMAIL², MOHD SAIFUL SAMSUDIN³, AZMAN AZID⁴, MOU LEONG TAN⁵ & MUHAMAD AFDAL AHMAD BASRI¹

¹Department of Mathematics, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris, 35900 Tanjung Malim, Perak Darul Ridzuan, Malaysia

²Data Analytics, Sciences & Modelling (DASM), Department of Mathematics and Statistics, Faculty of Applied Sciences and Technology, Universiti Tun Hussein Onn Malaysia, 86400 Batu Pahat, Johor Darul Takzim, Malaysia

³Faculty Business and Entrepreneurship, Universiti Malaysia Kelantan, Kampus Kota, Karung Berkunci 36 Pangkalan Chepa, 16100 Kota Bharu, Kelantan Darul Naim, Malaysia

⁴Faculty of Bioresources and Food Industry, Universiti Sultan Zainal Abidin, Besut Campus, 22200 Besut, Terengganu Darul Iman, Malaysia

⁵Geography Section, School of Humanities, Universiti Sains Malaysia, 11800 Pulau Pinang, Malaysia

Received: 18 June 2020/Accepted: 8 September 2020

ABSTRACT

Novel coronavirus also known as COVID-19 was first discovered in Wuhan, China by end of 2019. Since then, the virus has claimed millions of lives worldwide. In 29^th April 2020, there were more than 5,000 outbreak cases in Malaysia as reported by the Ministry of Health Malaysia (MOHE). This study aims to evaluate the trend analysis of the COVID-19 outbreak using Mann-Kendall test, and predict the future cases of COVID-19 in Malaysia using Recurrent Forecasting-Singular Spectrum Analysis (RF-SSA) model. The RF-SSA model was developed to measure and predict daily COVID-19 cases in Malaysia for the coming 10 days using previously-confirmed cases. A Singular Spectrum Analysis-based forecasting model that discriminates noise in a time series trend is introduced. The RF-SSA model assessment is based on the World Health Organization (WHO) official COVID-19 data to predict the daily confirmed cases after 29^th April until 9^th May, 2020. The preliminary results of Mann-Kendall test showed a declining trend pattern for new cases during Restricted Movement Order (RMO) 3 compared to RMO1, RMO2 and RMO4, with a dramatic increase in the COVID-19 outbreak during RMO1. Overall, the RF-SSA has over-forecasted the cases by 0.36%. This indicates RF-SSA’s competence to predict the impending number of COVID-19 cases. The proposed model predicted that Malaysia would hit single digit in daily confirmed cased of COVID-19 by early-June 2020. These findings have proven the capability of RF-SSA model in apprehending the trend and predict the cases of COVID-19 with high accuracy. Nevertheless, enhanced RF-SSA algorithm should to be developed for higher effectivity in capturing any extreme data changes.

Keywords: COVID-19; forecasting; Mann-Kendall test; recurrent forecasting (RF); singular spectrum analysis (SSA)

ABSTRAK

Koronavirus baru juga dikenali sebagai COVID-19 telah dilaporkan pertama kali di Wuhan, China pada akhir 2019. Sejak itu, virus tersebut telah meragut berjuta-juta nyawa di seluruh dunia. Pada 29 April 2020, terdapat lebih daripada 5,000 kes wabak di Malaysia seperti yang dilaporkan oleh Kementerian Kesihatan Malaysia (KKM). Kajian ini bertujuan untuk menilai analisis tren wabak COVID-19 menggunakan ujian Mann-Kendall dan meramalkan kes COVID-19 yang akan datang di Malaysia menggunakan model Ramalan Berulang-Analisis Spektrum Tunggal (RF-SSA). Model RF-SSA dibangunkan untuk mengukur dan meramalkan kes COVID-19 harian di Malaysia selama 10 hari akan datang dengan menggunakan kes yang telah disahkan sebelumnya. Model ramalan berdasarkan Analisis Spektrum Tunggal yang membezakan kebisingan dalam aliran siri masa diperkenalkan. Penilaian model RF-SSA berdasarkan data rasmi COVID-19 oleh Organisasi Kesihatan Dunia (WHO) untuk meramalkan kes-kes yang diselesaikan setiap hari selepas 29 April hingga 9 Mei 2020. Hasil awal ujian Mann-Kendall menunjukkan penurunan corak tren untuk kes baru semasa Perintah Kawalan Pergerakan (RMO) 3 berbanding RMO1, RMO2 dan RMO4, dengan peningkatan mendadak wabak COVID-19 semasa RMO1. Secara keseluruhan, RF-SSA telah meramalkan kes sebanyak 0.36%. Ini menunjukkan kecekapan RF-SSA untuk meramalkan jumlah kes COVID-19 yang akan datang. Model yang dicadangkan juga meramalkan bahawa Malaysia akan mencapai angka satu digit dalam kes COVID-19 yang disahkan setiap hari pada awal Jun 2020. Penemuan ini telah membuktikan kemampuan model RF-SSA dalam menangkap tren dan meramalkan kes COVID-19 dengan ketepatan tinggi. Walaupun begitu, algoritma RF-SSA harus dipertingkatkan untuk keberkesanan yang lebih tinggi dalam menangkap perubahan data yang melampau.

Kata kunci: Analisis spektrum tunggal (SSA); COVID-19; ramalan; ramalan berulang (RF); ujian Mann-Kendall

REFERENCES

Abdullah, S., Mansor, A.A., Napi, N.N.L.M., Mansor, W.N.W., Ahmed, A.N., Ismail, M. & Ramly, Z.T.A. 2020. Air quality status during 2020 Malaysia Movement Control Order (RMO) due to 2019 novel coronavirus (2019-nCoV) pandemic. Science of The Total Environment 729: 139022.

Alexandrov, T., Golyandina, N. & Spirov, A. 2008. Singular spectrum analysis of gene expression profiles of early drosophila embryo: Exponential-in-distance patterns. Research Letters in Signal Processing 2008: Article ID. 825758.

Alonso, F.J., Salgado, D.R., Cuadrado, J. & Pintado, P. 2009. Automatic smoothing of raw kinematics signals using SSA and cluster analysis. Euromech Solid Mechanics Conference Lisbon. pp. 1-9. 

Boehmke, B. & Greenwell, B 2019. Hands-On Machine Learning with R. Broken Sound. Parkway NW: Taylor & Francis. pp. 1-15.

Bouza-Deaño, R., Ternero-Rodríguez, M. & Fernández-Espinosa, A.J. 2008. Trend study and assessment of surface water quality in the Ebro River (Spain). Journal of Hydrology 361(3-4): 227-239.

Carvalho, M.D. & Rua, A. 2014. Real-Time Nowcasting the US Output GAP: Singular Spectrum Analysis at Work. Portugal: Banco De Portugal.

Chau, K.W. & Wu, C.L. 2010. Hybrid model coupled with singular spectrum analysis for daily rainfall prediction. Journal of Hydroinformatics 12(4): 458-473.

Danilov, D. 1997. The Caterpillar method for time series forecasting. In Principal Components of Time Series: The Caterpillar Method. Russian: University of St. Petersburg.

Gilbert, R.O. 1987. Statistical Methods for Environmental Pollution Monitoring. New York: John Wiley & Sons. pp. 23-52.

Hamzah, F.M., Saimi, F.M. & Jaafar, O. 2017. Identifying the monotonic trend in climate change parameter in Kluang and Senai, Johor, Malaysia. Sains Malaysiana 46(10): 1735-1741.

Hassani, H. 2007. Singular spectrum analysis: Methodology and comparison. Journal of Data Science 5: 239-257. 

Hassani, H. & Zhigljavsky, A. 2009. Singular spectrum analysis: Methodology and application to economics data. Journal of Systems Science and Complexity 22(3): 372-394.

Hossein, H., Mahdi, K. & Masoud, Y. 2017. An improved SSA forecasting result based on a filtered recurrent forecasting algorithm. Statistics/Theory of Signals 355(9): 1026-1036.

Kannan, S., Ali, P.S.S., Sheeza, A. & Hemalatha, K. 2020. COVID-19 (Novel Coronavirus 2019)-recent trends. European Review for Medical and Pharmacological Sciences 24(4): 2006-2011.

Kendall, M.G. 1975. Rank Correlation Measures. London: Charles Griffin. pp. 11-36.

Malaysian National Security Council (NSC). 2020. Movement Control Order (RMO). https://www.mkn.gov.my/web/ms/COVID-19/. Accessed on 13 May 2020.

Mann, H.B. 1945. Nonparametric tests against trend. Journal of the Econometric Society 13(3): 245-259.

Malaysia Ministry of Health Malaysia (MOH). 2020. Press Statement Updates on The Coronavirus Disease 2019 (COVID-19) Situation in Malaysia. https://www.moh.gov.my/index.php/pages/view/2019-ncov-wuhan-kenyataan-akhbar          Accessed on 13 May 2020.

Ministry of Health Malaysia. 2019. Coronavirus Website. http:/covid19.moh.gov.my/.

Mondal, R.A., Kundu, S. & Mukhopadhyay, A. 2012. Rainfall trend analysis by Mann-Kendall test: A case study of North-Eastern part of Cuttack district, Orissa. International Journal of Geology 2: 70-78. 

Muhammad Rezal Kamel Ariffin, Kathiresan Gopal, Isthrinayagy Krishnarajah, Iszuanie Syafidza Che Ilias, Mohd Bakri Adam, Noraishah Mohammad Sham, Jayanthi Arasan, Nur Haizum Abd Rahman & Nur Sumirah Mohd Dom. 2020. Malaysian COVID-19 Outbreak Data Analysis and Prediction. Institute for Mathematical Research. http://einspem.upm.edu.my/covid19maths/file/Report_001%20v13.pdf.

Nishiura, H., Kobayashi, T., Yang, Y., Hayashi, K., Miyama, T., Kinoshita, R., Linton, N.M., Jung, S.M., Yuan, B., Suzuki, A. & Akhmetzhanov, A. 2020. The rate of under ascertainment of novel coronavirus (2019-nCoV) infection: Estimation using Japanese passengers data on evacuation flights. Journal of Clinical Medicine 9(2): 1-3.

Rodriguez-Aragon, L.J. & Zhiglkavsky, A. 2010. Singular spectrum analysis for image processing. Statistics and Its Interface 3(3): 419-426.

Samsudin, M.S., Khalit, S.I., Juahir, H., Nasir, M., Fahmi, M., Kamarudin, M.K.A. & Lananan, F. 2017. Application of Mann-Kendall in analyzing water quality data trend at Perlis River, Malaysia. International Journal on Advanced Science, Engineering and Information Technology 7(1): 78-85.

Sen, P.K. 1968. Estimates of the regression coefficient based on Kendall’s tau. Journal of the American Statistical Association 63(324): 1379-1389.

Shaharudin, S.M., Ahmad, N., Mohamed, N.S. & Aziz, N. 2020. Performance analysis and validation of modified singular spectrum analysis based on simulation torrential rainfall data. International Journal on Advanced Science Engineering Information Technology 10(4): 1450-1456.

Shaharudin, S.M., Ahmad, N. & Zainuddin, N.H. 2019. Modified singular spectrum analysis in identifying rainfall trend over Peninsular Malaysia. Indonesian Journal of Electrical Engineering and Computer Science 15(1): 283-293.

Shaharudin, S.M., Ahmad, N. & Yusof, F. 2015. Effect of window length with singular spectrum analysis in extracting the trend signal of rainfall data. AIP Proceedings 1643(1): 321-326.

Suhartono, Ashari, D.E., Prastyo, D.D., Kuswanto, H. & Lee, M.H. 2019. Deep neural network for forecasting inflow and outflow in Indonesia. Sains Malaysiana 48(8): 1787-1798.

Tang, B., Wang, X., Li, Q., Bragazzi, N.L., Tang, S., Xiao, Y. & Wu, J. 2020. Estimation of the transmission risk of the 2019-nCoV and its implication for public health interventions. Journal of Clinical Medicine 9(2): 462-465.

Thompson, R.N. 2020. Novel coronavirus outbreak in Wuhan, China, 2020: Intense surveillance is vital for preventing sustained transmission in new locations. Journal of Clinical Medicine 9(2): 498-505.

Zhao, S., Musa, S.S., Lin, Q., Ran, J., Yang, G., Wang, W., Lou, Y., Yang, L., Gao, D., He, D. & Wang, M.H. 2020. Estimating the unreported number of novel coronavirus (2019-nCoV) cases in China in the first half of January 2020: A data-driven modelling analysis of the early outbreak. Journal of Clinical Medicine 9(2): 388-394.

*Corresponding author; email: shazlyn@fsmt.upsi.edu.my