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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2022
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my.utm.994392023-02-27T04:27:32Z http://eprints.utm.my/id/eprint/99439/ A review on machine learning approaches in COVID-19 pandemic prediction and forecasting Nazirun, Nor Nisha Nadhira Omar, Nashuha Selvaganeson, Koshelya Abdul Wahab, Asnida TK Electrical engineering. Electronics Nuclear engineering 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. Universiti Putra Malaysia Press 2022 Article PeerReviewed Nazirun, Nor Nisha Nadhira and Omar, Nashuha and Selvaganeson, Koshelya and Abdul Wahab, Asnida (2022) A review on machine learning approaches in COVID-19 pandemic prediction and forecasting. Malaysian Journal of Medicine and Health Sciences, 18 . pp. 78-84. ISSN 1675-8544 http://dx.doi.org/10.47836/mjmhs.18.s6.14 DOI : 10.47836/mjmhs.18.s6.14 |
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TK Electrical engineering. Electronics Nuclear engineering Nazirun, Nor Nisha Nadhira Omar, Nashuha Selvaganeson, Koshelya Abdul Wahab, Asnida A review on machine learning approaches in COVID-19 pandemic prediction and forecasting |
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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. |
| format |
Article |
| author |
Nazirun, Nor Nisha Nadhira Omar, Nashuha Selvaganeson, Koshelya Abdul Wahab, Asnida |
| author_facet |
Nazirun, Nor Nisha Nadhira Omar, Nashuha Selvaganeson, Koshelya Abdul Wahab, Asnida |
| author_sort |
Nazirun, Nor Nisha Nadhira |
| title |
A review on machine learning approaches in COVID-19 pandemic prediction and forecasting |
| title_short |
A review on machine learning approaches in COVID-19 pandemic prediction and forecasting |
| title_full |
A review on machine learning approaches in COVID-19 pandemic prediction and forecasting |
| title_fullStr |
A review on machine learning approaches in COVID-19 pandemic prediction and forecasting |
| title_full_unstemmed |
A review on machine learning approaches in COVID-19 pandemic prediction and forecasting |
| title_sort |
review on machine learning approaches in covid-19 pandemic prediction and forecasting |
| publisher |
Universiti Putra Malaysia Press |
| publishDate |
2022 |
| url |
http://eprints.utm.my/id/eprint/99439/ http://dx.doi.org/10.47836/mjmhs.18.s6.14 |
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