| Title: | Machine learning models for government to predict COVID-19 outbreak |
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| Authors: | ID Gupta, Rajan (Author)
ID Pandey, Gaurav (Author)
ID Chaudhary, Poonam (Author)
ID Pal, Saibal K. (Author) |
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| Files: |  https://dl.acm.org/doi/10.1145/3411761
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| Language: | English |
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| Work type: | Unknown |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | UNG - University of Nova Gorica
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| Abstract: | The COVID-19 pandemic has become a major threat to the whole world. Analysis of this disease requires major attention by the government in all countries to take necessary steps in reducing the effect of this global pandemic. In this study, outbreak of this disease has been analysed and trained for Indian region till 10th May, 2020, and testing has been done for the number of cases for the next three weeks. Machine learning models such as SEIR model and Regression model have been used for predictions based on the data collected from the official portal of the Government of India in the time period of 30th January, 2020, to 10th May, 2020. The performance of the models was evaluated using RMSLE and achieved 1.52 for SEIR model and 1.75 for the regression model. The RMSLE error rate between SEIR model and Regression model was found to be 2.01. Also, the value of R0, which is the spread of the disease, was calculated to be 2.84. Expected cases are predicted around 175K--200K in the three-week time period of test data, which is very close to the actual numbers. This study will help the government and doctors in preparing their plans for the future. |
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| Keywords: | COVID-19, India, spread exposed infected recovered model, regression model, machine learning, predictions, forecasting |
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| Year of publishing: | 2020 |
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| Number of pages: | str. 1-6 |
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| Numbering: | Vol. 1, no. 4 |
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| PID: | 20.500.12556/RUNG-6391  |
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| COBISS.SI-ID: | 57958147 |
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| UDC: | 004 |
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| ISSN on article: | 2639-0175 |
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| DOI: | 10.1145/3411761 |
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| NUK URN: | URN:SI:UNG:REP:O5X78UXT |
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| Publication date in RUNG: | 01.04.2021 |
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| Views: | 2220 |
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| Downloads: | 83 |
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