A network analysis and support vector regression approaches for visualising and predicting the COVID-19 outbreak in Malaysia
This study aims to (1) correlate and visualise the Coronavirus disease 19 (COVID-19) pandemic spread via Spearman rank coefficients of network analysis (NA) and (2) predict the cumulative number of COVID-19 confirmed and death cases via support vector regression (SVR) based on COVID-19 dataset in Ma...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-11-01
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| Series: | Healthcare Analytics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772442522000338 |
| _version_ | 1811187191882186752 |
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| author | Siti Nurhidayah Sharin Mohamad Khairil Radzali Muhamad Shirwan Abdullah Sani |
| author_facet | Siti Nurhidayah Sharin Mohamad Khairil Radzali Muhamad Shirwan Abdullah Sani |
| author_sort | Siti Nurhidayah Sharin |
| collection | DOAJ |
| description | This study aims to (1) correlate and visualise the Coronavirus disease 19 (COVID-19) pandemic spread via Spearman rank coefficients of network analysis (NA) and (2) predict the cumulative number of COVID-19 confirmed and death cases via support vector regression (SVR) based on COVID-19 dataset in Malaysia between July 2020 to June 2021. The NA indicated increasing connectivity between different states throughout the time frame, revealing the most complex network of COVID-19 transmission in the second quarter of 2021. The SVR model predicted future COVID-19 cases and deaths in Malaysia in the second half of 2021. The study demonstrated that the NA and SVR could provide relatively simple yet valuable artificial intelligence techniques for visualising the degree of connectivity and predicting pandemic risk based on confirmed COVID-19 cases and deaths. The Malaysian health authorities used the NA and SVR model results for preventive measures in highly populated states. |
| first_indexed | 2024-04-11T13:57:55Z |
| format | Article |
| id | doaj.art-64631c6e3cf34904a4314c0160da9caa |
| institution | Directory Open Access Journal |
| issn | 2772-4425 |
| language | English |
| last_indexed | 2024-04-11T13:57:55Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Healthcare Analytics |
| spelling | doaj.art-64631c6e3cf34904a4314c0160da9caa2022-12-22T04:20:11ZengElsevierHealthcare Analytics2772-44252022-11-012100080A network analysis and support vector regression approaches for visualising and predicting the COVID-19 outbreak in MalaysiaSiti Nurhidayah Sharin0Mohamad Khairil Radzali1Muhamad Shirwan Abdullah Sani2Halal Products Research Institute, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, MalaysiaFaculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, MalaysiaInternational Institute for Halal Research and Training, International Islamic University Malaysia, Level 3, KICT Building, 53100 Kuala Lumpur, Malaysia; Konsortium Institut Halal IPT Malaysia, Ministry of Higher Education, Block E8, Complex E, Federal Government Administrative Centre, 62604 Putrajaya, Malaysia; The Catalytixs Solutions, No. 713, Jalan DPP 1/4, Desa Permai Pedas, 71400 Pedas, Negeri Sembilan, Malaysia; Corresponding author at: International Institute for Halal Research and Training, International Islamic University Malaysia, Level 3, KICT Building, 53100 Kuala Lumpur, Malaysia.This study aims to (1) correlate and visualise the Coronavirus disease 19 (COVID-19) pandemic spread via Spearman rank coefficients of network analysis (NA) and (2) predict the cumulative number of COVID-19 confirmed and death cases via support vector regression (SVR) based on COVID-19 dataset in Malaysia between July 2020 to June 2021. The NA indicated increasing connectivity between different states throughout the time frame, revealing the most complex network of COVID-19 transmission in the second quarter of 2021. The SVR model predicted future COVID-19 cases and deaths in Malaysia in the second half of 2021. The study demonstrated that the NA and SVR could provide relatively simple yet valuable artificial intelligence techniques for visualising the degree of connectivity and predicting pandemic risk based on confirmed COVID-19 cases and deaths. The Malaysian health authorities used the NA and SVR model results for preventive measures in highly populated states.http://www.sciencedirect.com/science/article/pii/S2772442522000338CoronavirusCOVID-19Network analysisSupport vector regressionArtificial intelligence |
| spellingShingle | Siti Nurhidayah Sharin Mohamad Khairil Radzali Muhamad Shirwan Abdullah Sani A network analysis and support vector regression approaches for visualising and predicting the COVID-19 outbreak in Malaysia Healthcare Analytics Coronavirus COVID-19 Network analysis Support vector regression Artificial intelligence |
| title | A network analysis and support vector regression approaches for visualising and predicting the COVID-19 outbreak in Malaysia |
| title_full | A network analysis and support vector regression approaches for visualising and predicting the COVID-19 outbreak in Malaysia |
| title_fullStr | A network analysis and support vector regression approaches for visualising and predicting the COVID-19 outbreak in Malaysia |
| title_full_unstemmed | A network analysis and support vector regression approaches for visualising and predicting the COVID-19 outbreak in Malaysia |
| title_short | A network analysis and support vector regression approaches for visualising and predicting the COVID-19 outbreak in Malaysia |
| title_sort | network analysis and support vector regression approaches for visualising and predicting the covid 19 outbreak in malaysia |
| topic | Coronavirus COVID-19 Network analysis Support vector regression Artificial intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S2772442522000338 |
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