Short-term forecasting of daily confirmed COVID-19 cases in Malaysia using RF-SSA model

• Main Authors: Shaharudin, Shazlyn Milleana, Ismail, Shuhaida, Hassan, Noor Artika, Tan, Mou Leong, Sulaiman, Nurul Ainina Filza
• Format: Article
• Language: English; English; English
• Published: Frontiers Media S.A. 2021
• Subjects: GA Mathematical geography. Cartography; HA154 Statistical data; RA643 Communicable Diseases and Public Health; T57 Applied mathematics. Quantitative methods. Operation research. System analysis
• Online Access: http://irep.iium.edu.my/90510/7/90510_Short-term%20forecasting%20of%20daily%20confirmed%20COVID-19%20cases%20in%20Malaysia%20using%20RF-SSA%20model.pdf; http://irep.iium.edu.my/90510/13/90510_Short-term%20forecasting%20of%20daily%20confirmed%20COVID-19_SCOPUS.pdf; http://irep.iium.edu.my/90510/14/90510_Short-term%20forecasting%20of%20daily%20confirmed%20COVID-19_WOS.pdf; http://irep.iium.edu.my/90510/; https://www.frontiersin.org/journals/public-health

Novel coronavirus (COVID-19) was discovered in Wuhan, China in December 2019, and has affected millions of lives worldwide. On 29th April 2020, Malaysia reported more than 5,000 COVID-19 cases; the second highest in the Southeast Asian region after Singapore. Recently, a forecastingmodel was developed tomeasure and predict COVID- 19 cases in Malaysia on daily basis for the next 10 days using previously-confirmed cases. A Recurrent Forecasting-Singular Spectrum Analysis (RF-SSA) is proposed by establishing L and ET parameters via several tests. The advantage of using this forecasting model is it would discriminate noise in a time series trend and produce significant forecasting results. The RF-SSA model assessment was based on the official COVID-19 data released by the World Health Organization (WHO) to predict daily confirmed cases between 30th April and 31st May, 2020. These results revealed that parameter L = 5 (T/20) for the RF-SSA model was indeed suitable for short-time series outbreak data, while the appropriate number of eigentriples was integral as it influenced the forecasting results. Evidently, the RF-SSA had over-forecasted the cases by 0.36%. This signifies the competence of RF-SSA in predicting the impending number of COVID- 19 cases. Nonetheless, an enhanced RF-SSA algorithm should be developed for higher effectivity of capturing any extreme data changes.