ArticleHealthcare analytics (New York, N.Y.)2022
A network analysis and support vector regression approaches for visualising and predicting the COVID-19 outbreak in Malaysia.
Article in Healthcare analytics (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Impact, use, and implications of artificial intelligence in public health decision making by elected officials: a scoping review.Frontiers in public health · 2026Article
- Machine Learning Techniques Applied to COVID-19 Prediction: A Systematic Literature Review.Bioengineering (Basel, Switzerland) · 2025Review
- A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges.Sensors (Basel, Switzerland) · 2023Review
- A Bayesian predictive analytics model for improving long range epidemic forecasting during an infection wave.Healthcare analytics (New York, N.Y.) · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
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.
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Registered trials
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