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 forecasting model was develo...
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Frontiers Media S.A.
2021-06-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2021.604093/full |
| _version_ | 1818666606454112256 |
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| author | Shazlyn Milleana Shaharudin Shuhaida Ismail Noor Artika Hassan Mou Leong Tan Nurul Ainina Filza Sulaiman |
| author_facet | Shazlyn Milleana Shaharudin Shuhaida Ismail Noor Artika Hassan Mou Leong Tan Nurul Ainina Filza Sulaiman |
| author_sort | Shazlyn Milleana Shaharudin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T06:07:12Z |
| format | Article |
| id | doaj.art-0befe61a7e084360b56e7c3a638c21b3 |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-12-17T06:07:12Z |
| publishDate | 2021-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-0befe61a7e084360b56e7c3a638c21b32022-12-21T22:00:44ZengFrontiers Media S.A.Frontiers in Public Health2296-25652021-06-01910.3389/fpubh.2021.604093604093Short-Term Forecasting of Daily Confirmed COVID-19 Cases in Malaysia Using RF-SSA ModelShazlyn Milleana Shaharudin0Shuhaida Ismail1Noor Artika Hassan2Mou Leong Tan3Nurul Ainina Filza Sulaiman4Department of Mathematics, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris, Tanjung Malim, MalaysiaData Analytics, Sciences & Modelling (DASM), Department of Mathematics & Statistics, Faculty of Applied Sciences and Technology, Universiti Tun Hussein Onn Malaysia, Parit Raja, MalaysiaDepartment of Community Medicine, Kulliyyah of Medicine, International Islamic University Malaysia, Kuantan, MalaysiaGeoinformatic Unit, Geography Section, School of Humanities, Universiti Sains Malaysia, Gelugor, MalaysiaDepartment of Mathematics, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris, Tanjung Malim, MalaysiaNovel 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.https://www.frontiersin.org/articles/10.3389/fpubh.2021.604093/fullCOVID-19eigentriplesforecastingrecurrent forecastingsingular spectrum analysistrend |
| spellingShingle | Shazlyn Milleana Shaharudin Shuhaida Ismail Noor Artika Hassan Mou Leong Tan Nurul Ainina Filza Sulaiman Short-Term Forecasting of Daily Confirmed COVID-19 Cases in Malaysia Using RF-SSA Model Frontiers in Public Health COVID-19 eigentriples forecasting recurrent forecasting singular spectrum analysis trend |
| title | Short-Term Forecasting of Daily Confirmed COVID-19 Cases in Malaysia Using RF-SSA Model |
| title_full | Short-Term Forecasting of Daily Confirmed COVID-19 Cases in Malaysia Using RF-SSA Model |
| title_fullStr | Short-Term Forecasting of Daily Confirmed COVID-19 Cases in Malaysia Using RF-SSA Model |
| title_full_unstemmed | Short-Term Forecasting of Daily Confirmed COVID-19 Cases in Malaysia Using RF-SSA Model |
| title_short | Short-Term Forecasting of Daily Confirmed COVID-19 Cases in Malaysia Using RF-SSA Model |
| title_sort | short term forecasting of daily confirmed covid 19 cases in malaysia using rf ssa model |
| topic | COVID-19 eigentriples forecasting recurrent forecasting singular spectrum analysis trend |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2021.604093/full |
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