Prediction of epidemic trends in COVID-19 with Mann-Kendall and recurrent forecasting-singular spectrum analysis
Novel coronavirus also known as COVID-19 was first discovered in Wuhan, China by end of 2019. Since then, the virus has claimed millions of lives worldwide. In 29th April 2020, there were more than 5,000 outbreak cases in Malaysia as reported by the Ministry of Health Malaysia (MOHE). This study...
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| Format: | Article |
| Language: | English |
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Penerbit Universiti Kebangsaan Malaysia
2021
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| Online Access: | http://journalarticle.ukm.my/17185/ http://journalarticle.ukm.my/17185/1/23.pdf |
| _version_ | 1848814248946302976 |
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| author | Shazlyn Milleana Shaharudin, Shuhaida Ismail, Mohd Saiful Samsudin, Azman Azid, Mou, Leong Tan Muhamad Afdal Ahmad Basri, |
| author_facet | Shazlyn Milleana Shaharudin, Shuhaida Ismail, Mohd Saiful Samsudin, Azman Azid, Mou, Leong Tan Muhamad Afdal Ahmad Basri, |
| author_sort | Shazlyn Milleana Shaharudin, |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | Novel coronavirus also known as COVID-19 was first discovered in Wuhan, China by end of 2019. Since then, the virus
has claimed millions of lives worldwide. In 29th April 2020, there were more than 5,000 outbreak cases in Malaysia as
reported by the Ministry of Health Malaysia (MOHE). This study aims to evaluate the trend analysis of the COVID-19
outbreak using Mann-Kendall test, and predict the future cases of COVID-19 in Malaysia using Recurrent Forecasting-Singular Spectrum Analysis (RF-SSA) model. The RF-SSA model was developed to measure and predict daily COVID-19
cases in Malaysia for the coming 10 days using previously-confirmed cases. A Singular Spectrum Analysis-based
forecasting model that discriminates noise in a time series trend is introduced. The RF-SSA model assessment is based
on the World Health Organization (WHO) official COVID-19 data to predict the daily confirmed cases after 29th April
until 9th May, 2020. The preliminary results of Mann-Kendall test showed a declining trend pattern for new cases during
Restricted Movement Order (RMO) 3 compared to RMO1, RMO2 and RMO4, with a dramatic increase in the COVID-19
outbreak during RMO1. Overall, the RF-SSA has over-forecasted the cases by 0.36%. This indicates RF-SSA’s competence
to predict the impending number of COVID-19 cases. The proposed model predicted that Malaysia would hit single digit
in daily confirmed cased of COVID-19 by early-June 2020. These findings have proven the capability of RF-SSA model in
apprehending the trend and predict the cases of COVID-19 with high accuracy. Nevertheless, enhanced RF-SSA algorithm
should to be developed for higher effectivity in capturing any extreme data changes. |
| first_indexed | 2025-11-15T00:31:04Z |
| format | Article |
| id | oai:generic.eprints.org:17185 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:31:04Z |
| publishDate | 2021 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:171852021-07-26T03:14:55Z http://journalarticle.ukm.my/17185/ Prediction of epidemic trends in COVID-19 with Mann-Kendall and recurrent forecasting-singular spectrum analysis Shazlyn Milleana Shaharudin, Shuhaida Ismail, Mohd Saiful Samsudin, Azman Azid, Mou, Leong Tan Muhamad Afdal Ahmad Basri, Novel coronavirus also known as COVID-19 was first discovered in Wuhan, China by end of 2019. Since then, the virus has claimed millions of lives worldwide. In 29th April 2020, there were more than 5,000 outbreak cases in Malaysia as reported by the Ministry of Health Malaysia (MOHE). This study aims to evaluate the trend analysis of the COVID-19 outbreak using Mann-Kendall test, and predict the future cases of COVID-19 in Malaysia using Recurrent Forecasting-Singular Spectrum Analysis (RF-SSA) model. The RF-SSA model was developed to measure and predict daily COVID-19 cases in Malaysia for the coming 10 days using previously-confirmed cases. A Singular Spectrum Analysis-based forecasting model that discriminates noise in a time series trend is introduced. The RF-SSA model assessment is based on the World Health Organization (WHO) official COVID-19 data to predict the daily confirmed cases after 29th April until 9th May, 2020. The preliminary results of Mann-Kendall test showed a declining trend pattern for new cases during Restricted Movement Order (RMO) 3 compared to RMO1, RMO2 and RMO4, with a dramatic increase in the COVID-19 outbreak during RMO1. Overall, the RF-SSA has over-forecasted the cases by 0.36%. This indicates RF-SSA’s competence to predict the impending number of COVID-19 cases. The proposed model predicted that Malaysia would hit single digit in daily confirmed cased of COVID-19 by early-June 2020. These findings have proven the capability of RF-SSA model in apprehending the trend and predict the cases of COVID-19 with high accuracy. Nevertheless, enhanced RF-SSA algorithm should to be developed for higher effectivity in capturing any extreme data changes. Penerbit Universiti Kebangsaan Malaysia 2021-04 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/17185/1/23.pdf Shazlyn Milleana Shaharudin, and Shuhaida Ismail, and Mohd Saiful Samsudin, and Azman Azid, and Mou, Leong Tan and Muhamad Afdal Ahmad Basri, (2021) Prediction of epidemic trends in COVID-19 with Mann-Kendall and recurrent forecasting-singular spectrum analysis. Sains Malaysiana, 50 (4). pp. 1131-1142. ISSN 0126-6039 https://www.ukm.my/jsm/malay_journals/jilid50bil4_2021/KandunganJilid50Bil4_2021.html |
| spellingShingle | Shazlyn Milleana Shaharudin, Shuhaida Ismail, Mohd Saiful Samsudin, Azman Azid, Mou, Leong Tan Muhamad Afdal Ahmad Basri, Prediction of epidemic trends in COVID-19 with Mann-Kendall and recurrent forecasting-singular spectrum analysis |
| title | Prediction of epidemic trends in COVID-19 with Mann-Kendall and recurrent forecasting-singular spectrum analysis |
| title_full | Prediction of epidemic trends in COVID-19 with Mann-Kendall and recurrent forecasting-singular spectrum analysis |
| title_fullStr | Prediction of epidemic trends in COVID-19 with Mann-Kendall and recurrent forecasting-singular spectrum analysis |
| title_full_unstemmed | Prediction of epidemic trends in COVID-19 with Mann-Kendall and recurrent forecasting-singular spectrum analysis |
| title_short | Prediction of epidemic trends in COVID-19 with Mann-Kendall and recurrent forecasting-singular spectrum analysis |
| title_sort | prediction of epidemic trends in covid-19 with mann-kendall and recurrent forecasting-singular spectrum analysis |
| url | http://journalarticle.ukm.my/17185/ http://journalarticle.ukm.my/17185/ http://journalarticle.ukm.my/17185/1/23.pdf |