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Predicting the time period of extension of lockdown due to increase in rate of COVID-19 cases in India using machine learning

The research paper proposes a methodology to predict the extension of lockdown in order to eradicate COVID-19 from India. All the concepts related to Coronavirus, its history, prevention and cure is explained in the research paper. Concept used to predict the number of active cases, deaths and recov...

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Autores principales: Wadhwa, Parth, Aishwarya, Tripathi, Amrendra, Singh, Prabhishek, Diwakar, Manoj, Kumar, Neeraj
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7455153/
https://www.ncbi.nlm.nih.gov/pubmed/32904353
http://dx.doi.org/10.1016/j.matpr.2020.08.509
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author Wadhwa, Parth
Aishwarya
Tripathi, Amrendra
Singh, Prabhishek
Diwakar, Manoj
Kumar, Neeraj
author_facet Wadhwa, Parth
Aishwarya
Tripathi, Amrendra
Singh, Prabhishek
Diwakar, Manoj
Kumar, Neeraj
author_sort Wadhwa, Parth
collection PubMed
description The research paper proposes a methodology to predict the extension of lockdown in order to eradicate COVID-19 from India. All the concepts related to Coronavirus, its history, prevention and cure is explained in the research paper. Concept used to predict the number of active cases, deaths and recovery is Linear Regression which is an application of machine learning. Extension of lockdown is predicted on the basis of predicted number of active cases, deaths and recovery all over India. To predict the number of active cases, deaths and recovery, date wise analysis of current data was done and necessary parameters like daily recovery, daily deaths, increase rate of covid-19 cases were included. Graphical representation of each analysis and prediction was done in order to make predicted results more understandable. The combined analysis was performed at the end which included the final result of total cases of coronavirus in India. Combined analysis included the no. of cases from start of COVID-19 to the predicted end of cases all over India.
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spelling pubmed-74551532020-08-31 Predicting the time period of extension of lockdown due to increase in rate of COVID-19 cases in India using machine learning Wadhwa, Parth Aishwarya Tripathi, Amrendra Singh, Prabhishek Diwakar, Manoj Kumar, Neeraj Mater Today Proc Article The research paper proposes a methodology to predict the extension of lockdown in order to eradicate COVID-19 from India. All the concepts related to Coronavirus, its history, prevention and cure is explained in the research paper. Concept used to predict the number of active cases, deaths and recovery is Linear Regression which is an application of machine learning. Extension of lockdown is predicted on the basis of predicted number of active cases, deaths and recovery all over India. To predict the number of active cases, deaths and recovery, date wise analysis of current data was done and necessary parameters like daily recovery, daily deaths, increase rate of covid-19 cases were included. Graphical representation of each analysis and prediction was done in order to make predicted results more understandable. The combined analysis was performed at the end which included the final result of total cases of coronavirus in India. Combined analysis included the no. of cases from start of COVID-19 to the predicted end of cases all over India. Elsevier Ltd. 2021 2020-08-28 /pmc/articles/PMC7455153/ /pubmed/32904353 http://dx.doi.org/10.1016/j.matpr.2020.08.509 Text en © 2020 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Newer Trends and Innovation in Mechanical Engineering: Materials Science. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Wadhwa, Parth
Aishwarya
Tripathi, Amrendra
Singh, Prabhishek
Diwakar, Manoj
Kumar, Neeraj
Predicting the time period of extension of lockdown due to increase in rate of COVID-19 cases in India using machine learning
title Predicting the time period of extension of lockdown due to increase in rate of COVID-19 cases in India using machine learning
title_full Predicting the time period of extension of lockdown due to increase in rate of COVID-19 cases in India using machine learning
title_fullStr Predicting the time period of extension of lockdown due to increase in rate of COVID-19 cases in India using machine learning
title_full_unstemmed Predicting the time period of extension of lockdown due to increase in rate of COVID-19 cases in India using machine learning
title_short Predicting the time period of extension of lockdown due to increase in rate of COVID-19 cases in India using machine learning
title_sort predicting the time period of extension of lockdown due to increase in rate of covid-19 cases in india using machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7455153/
https://www.ncbi.nlm.nih.gov/pubmed/32904353
http://dx.doi.org/10.1016/j.matpr.2020.08.509
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