Prediction of COVID’19 Outbreak by Using ML-Based Time-Series Forecasting Approach
Advances in Science, Technology and Innovation, ISSN: 2522-8722, Page: 287-294
2021
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Book Chapter Description
The COVID-19 now became a pandemic and rising rapidly and spreading in all parts of the world like fire. India reported its first COVID-19 case on January 30, when a student arrived in Kerala from Wuhan. Thousands of people are acquiring this deadly virus daily and with many people dying from it. The major concern of all the countries is to protect its citizens and try to eradicate this disease as fast as possible. This paper aims to perform exploratory analysis using the concepts of data science on the confirmed cases, total deaths, and total recovered cases of this virus. The research work predicts the spread of the outbreak for the next five days by using time-series forecasting algorithms. This paper deals with learning about how the corona virus is spreading and using that trend to predict for the upcoming days. It would be able to predict to a suitable accuracy which can help the government learn about the statistics of this disease and prepare further for protection against this. The results are discussed at last with prediction and error estimates.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85111809036&origin=inward; http://dx.doi.org/10.1007/978-3-030-66218-9_33; https://link.springer.com/10.1007/978-3-030-66218-9_33; https://link.springer.com/content/pdf/10.1007/978-3-030-66218-9_33; https://dx.doi.org/10.1007/978-3-030-66218-9_33; https://link.springer.com/chapter/10.1007/978-3-030-66218-9_33
Springer Science and Business Media LLC
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