Short-term forecasting of daily confirmed COVID-19 cases in Malaysia using RF-SSA model
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 deve...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English English English |
| Published: |
Frontiers Media S.A.
2021
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| Subjects: | |
| 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 |
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| Summary: | 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. |
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