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The Singular Spectrum Analysis (SSA) is a powerful non-parametric time series analysis that has demonstrated its capability in forecasting different time series in various disciplines. SSA falls in the framework of data-driven modelling of dynamical system which does not rely on any underlying assumption except the inherent dynamics which are captured over time. The capabilities of SSA are mainly afforded by its direct connection to the singular value decomposition (SVD). It is generally accepted that SVD-based methods are very affective for the noise reduction in deterministic time series and consequently for forecasting, as well as for extracting trends and structures. Despite its strength, several shortcomings of SSA in the analysis of COVID-19 time series have been reported in the literature. The aim of this paper is to determine the scope of this limitation and we confine our investigation in the analysis and forecasting of COVID-19 Pandemic in Malaysia. We scrutinize the results fromthe SSA analysis of the number of daily confirmed cases to gain further insight into the intrinsic trends of the pandemic. Groupings of the singular spectra that contributes to different features of the pandemic time series are identified using analysis of the singular value spectrum, periodogram analysis and analysis of the weighted correlation matrix. It was revealed that under stationary conditions, the principal eigentriple is sufficient to produce reliable forecast. However, in non-stationary conditions, for example during a movement control order, it is useful to also study the minor eigentriples which could contain transient dynamics that may persist. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19 disease, which has become pandemic since December 2019. In the recent months, among five countries in the Southeast Asia, Malaysia has the highest per-capita daily new cases and daily new deaths. A mathematical modelling approach using a Singular Spectrum Analysis (SSA) technique was used to generate data-driven 30-days ahead forecasts for the number of daily cases in the states and federal territories in Malaysia at four consecutive time points between 27 July 2021 and 26 August 2021. Each forecast was produced using SSA prediction model of the current major trend at each time point. The objective is to understand the transition dynamics of COVID-19 in each state by analysing the direction of change of the major trends during the period of study. The states and federal territories in Malaysia were grouped in four categories based on the nature of the transition. Overall, it was found that the COVID-19 spread has progressed unevenly across states and federal territories. Major regions like Selangor, Kuala Lumpur, Putrajaya and Negeri Sembilan were in Group 3 (fast decrease in infectivity) and Labuan was in Group 4 (possible eradication of infectivity). Other states e.g. Pulau Pinang, Sabah, Sarawak, Kelantan and Johor were categorised in Group 1 (very high infectivity levels) with Perak, Kedah, Pahang, Terengganu and Melaka were classified in Group 2 (high infectivity levels). It is also cautioned that SSA provides a promising avenue for forecasting the transition dynamics of COVID-19; however, the reliability of this technique depends on the availability of good quality data. [ABSTRACT FROM AUTHOR]

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 forecasting model was developed to measure 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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Shaharudin, Ismail, Hassan, Tan and Sulaiman.)