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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8236644/ https://www.ncbi.nlm.nih.gov/pubmed/34195166 http://dx.doi.org/10.3389/fpubh.2021.604093 |
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author | Shaharudin, Shazlyn Milleana Ismail, Shuhaida Hassan, Noor Artika Tan, Mou Leong Sulaiman, Nurul Ainina Filza |
author_facet | Shaharudin, Shazlyn Milleana Ismail, Shuhaida Hassan, Noor Artika Tan, Mou Leong Sulaiman, Nurul Ainina Filza |
author_sort | Shaharudin, Shazlyn Milleana |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-8236644 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82366442021-06-29 Short-Term Forecasting of Daily Confirmed COVID-19 Cases in Malaysia Using RF-SSA Model Shaharudin, Shazlyn Milleana Ismail, Shuhaida Hassan, Noor Artika Tan, Mou Leong Sulaiman, Nurul Ainina Filza Front Public Health 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 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. Frontiers Media S.A. 2021-06-14 /pmc/articles/PMC8236644/ /pubmed/34195166 http://dx.doi.org/10.3389/fpubh.2021.604093 Text en Copyright © 2021 Shaharudin, Ismail, Hassan, Tan and Sulaiman. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Public Health Shaharudin, Shazlyn Milleana Ismail, Shuhaida Hassan, Noor Artika Tan, Mou Leong Sulaiman, Nurul Ainina Filza Short-Term Forecasting of Daily Confirmed COVID-19 Cases in Malaysia Using RF-SSA Model |
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 | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8236644/ https://www.ncbi.nlm.nih.gov/pubmed/34195166 http://dx.doi.org/10.3389/fpubh.2021.604093 |
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