A review on machine learning approaches in COVID-19 pandemic prediction and forecasting
Novel COVID-19 Coronavirus disease, namely SARS-CoV-2, is a global pandemic and has spread to more than 200 countries. The sudden rise in the number of cases is causing a tremendous effect on healthcare services worldwide. To assist strategies in containing its spread, machine learning (ML) has been...
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| Main Authors: | , , , |
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| Format: | Article |
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Universiti Putra Malaysia Press
2022
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/99439/ http://dx.doi.org/10.47836/mjmhs.18.s6.14 |
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| Summary: | Novel COVID-19 Coronavirus disease, namely SARS-CoV-2, is a global pandemic and has spread to more than 200 countries. The sudden rise in the number of cases is causing a tremendous effect on healthcare services worldwide. To assist strategies in containing its spread, machine learning (ML) has been employed to effectively track the daily infected and mortality cases as well as to predict the peak growth among the states or/and country-wise. The evidence of ML in tackling previous epidemics has encouraged researchers to reciprocate with this outbreak. In this paper, recent studies that apply various ML models in predicting and forecasting COVID-19 trends have been reviewed. The development in ML has significantly supported health experts with improved prediction and forecasting. By developing prediction models, the world can prepare and mitigate the spread and impact against COVID-19. |
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