A network analysis and support vector regression approaches for visualising and predicting the COVID-19 outbreak in Malaysia

• Published in: Healthcare analytics (New York, N.Y.) N.Y.), 2022-11, Vol.2, p.100080-100080, Article 100080
• Main Authors: Sharin, Siti Nurhidayah, Radzali, Mohamad Khairil, Sani, Muhamad Shirwan Abdullah
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Coronavirus; COVID-19; Network analysis; Support vector regression
• ISSN: 2772-4425; 2772-4425
• DOI: 10.1016/j.health.2022.100080

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. •This study correlated and visualises pandemic spread via Spearman rank coefficients of network analysis (NA).•The study also predicted the cumulative number of confirmed and death cases via support vector regression (SVR).•The NA indicated increasing connectivity between different states in Malaysia.•The SVR model predicted future cases and deaths in Malaysia.•The Malaysian health authorities used the NA and SVR results for preventive measures in populated states.