Machine Learning Approach for Forecast Analysis of Novel COVID-19 Scenarios in India
The novel coronavirus (nCOV) is a new strain that needs to be hindered from spreading by taking effective preventive measures as swiftly as possible. Timely forecasting of COVID-19 cases can ultimately support in making significant decisions and planning for implementing preventive measures. In this...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9878307/ |
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| author | Ankit Kumar Srivastava Saurabh Mani Tripathi Sachin Kumar Rajvikram Madurai Elavarasan Sivasankar Gangatharan Dinesh Kumar Lucian Mihet-Popa |
| author_facet | Ankit Kumar Srivastava Saurabh Mani Tripathi Sachin Kumar Rajvikram Madurai Elavarasan Sivasankar Gangatharan Dinesh Kumar Lucian Mihet-Popa |
| author_sort | Ankit Kumar Srivastava |
| collection | DOAJ |
| description | The novel coronavirus (nCOV) is a new strain that needs to be hindered from spreading by taking effective preventive measures as swiftly as possible. Timely forecasting of COVID-19 cases can ultimately support in making significant decisions and planning for implementing preventive measures. In this study, three common machine learning (ML) approaches via linear regression (LR), sequential minimal optimization (SMO) regression, and M5P techniques have been discussed and implemented for forecasting novel coronavirus disease-2019 (COVID-19) pandemic scenarios. To demonstrate the forecast accuracy of the aforementioned ML approaches, a preliminary sample-study has been conducted on the first wave of the COVID-19 pandemic scenario for three different countries including the United States of America (USA), Italy, and Australia. Furthermore, the contributions of this study are extended by conducting an in-depth forecast study on COVID-19 pandemic scenarios for the first, second, and third waves in India. An accurate forecasting model has been proposed, which has been constructed on the basis of the results of the aforementioned forecasting models of COVID-19 pandemic scenarios. The findings of the research highlight that LR is a potential approach that outperforms all other forecasting models tested herein in the present COVID-19 pandemic scenario. Finally, the LR approach has been used to forecast the likely onset of the fourth wave of COVID-19 in India. |
| first_indexed | 2024-04-12T19:03:53Z |
| format | Article |
| id | doaj.art-fdd59b06c9c74934bb4f89834e7529f3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T19:03:53Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-fdd59b06c9c74934bb4f89834e7529f32022-12-22T03:20:05ZengIEEEIEEE Access2169-35362022-01-0110951069512410.1109/ACCESS.2022.32048049878307Machine Learning Approach for Forecast Analysis of Novel COVID-19 Scenarios in IndiaAnkit Kumar Srivastava0Saurabh Mani Tripathi1https://orcid.org/0000-0001-6168-5443Sachin Kumar2https://orcid.org/0000-0003-1517-7450Rajvikram Madurai Elavarasan3https://orcid.org/0000-0002-7744-6102Sivasankar Gangatharan4Dinesh Kumar5Lucian Mihet-Popa6https://orcid.org/0000-0002-4556-2774Department of Electrical Engineering, Institute of Engineering and Technology, Dr. Rammanohar Lohia Avadh University, Ayodhya, Uttar Pradesh, IndiaDepartment of Electrical Engineering, Power and Energy Research Centre, Kamla Nehru Institute of Technology, Sultanpur, Uttar Pradesh, IndiaDepartment of Electrical Engineering, Govind Ballabh Pant Institute of Engineering & Technology, Pauri, Garhwal, Uttarakhand, IndiaDepartment of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai, Tamil Nadu, IndiaDepartment of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai, Tamil Nadu, IndiaDepartment of Electrical Engineering, Institute of Engineering and Technology, Dr. Rammanohar Lohia Avadh University, Ayodhya, Uttar Pradesh, IndiaFaculty of Electrical Engineering, Østfold University College, Halden, NorwayThe novel coronavirus (nCOV) is a new strain that needs to be hindered from spreading by taking effective preventive measures as swiftly as possible. Timely forecasting of COVID-19 cases can ultimately support in making significant decisions and planning for implementing preventive measures. In this study, three common machine learning (ML) approaches via linear regression (LR), sequential minimal optimization (SMO) regression, and M5P techniques have been discussed and implemented for forecasting novel coronavirus disease-2019 (COVID-19) pandemic scenarios. To demonstrate the forecast accuracy of the aforementioned ML approaches, a preliminary sample-study has been conducted on the first wave of the COVID-19 pandemic scenario for three different countries including the United States of America (USA), Italy, and Australia. Furthermore, the contributions of this study are extended by conducting an in-depth forecast study on COVID-19 pandemic scenarios for the first, second, and third waves in India. An accurate forecasting model has been proposed, which has been constructed on the basis of the results of the aforementioned forecasting models of COVID-19 pandemic scenarios. The findings of the research highlight that LR is a potential approach that outperforms all other forecasting models tested herein in the present COVID-19 pandemic scenario. Finally, the LR approach has been used to forecast the likely onset of the fourth wave of COVID-19 in India.https://ieeexplore.ieee.org/document/9878307/Death forecastinglinear regression (LR)M5Pmachine learning (ML)novel coronavirus (nCOV)COVID-19 forecasting |
| spellingShingle | Ankit Kumar Srivastava Saurabh Mani Tripathi Sachin Kumar Rajvikram Madurai Elavarasan Sivasankar Gangatharan Dinesh Kumar Lucian Mihet-Popa Machine Learning Approach for Forecast Analysis of Novel COVID-19 Scenarios in India IEEE Access Death forecasting linear regression (LR) M5P machine learning (ML) novel coronavirus (nCOV) COVID-19 forecasting |
| title | Machine Learning Approach for Forecast Analysis of Novel COVID-19 Scenarios in India |
| title_full | Machine Learning Approach for Forecast Analysis of Novel COVID-19 Scenarios in India |
| title_fullStr | Machine Learning Approach for Forecast Analysis of Novel COVID-19 Scenarios in India |
| title_full_unstemmed | Machine Learning Approach for Forecast Analysis of Novel COVID-19 Scenarios in India |
| title_short | Machine Learning Approach for Forecast Analysis of Novel COVID-19 Scenarios in India |
| title_sort | machine learning approach for forecast analysis of novel covid 19 scenarios in india |
| topic | Death forecasting linear regression (LR) M5P machine learning (ML) novel coronavirus (nCOV) COVID-19 forecasting |
| url | https://ieeexplore.ieee.org/document/9878307/ |
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