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Intelligent computing on time-series data analysis and prediction of COVID-19 pandemics
Covid-19 disease caused by novel coronavirus (SARS-CoV-2) is a highly contagious epidemic that originated in Wuhan, Hubei Province of China in late December 2019. World Health Organization (WHO) declared Covid-19 as a pandemic on 12th March 2020. Researchers and policy makers are designing strategie...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Published by Elsevier B.V.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8364174/ https://www.ncbi.nlm.nih.gov/pubmed/34413555 http://dx.doi.org/10.1016/j.patrec.2021.07.027 |
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author | Dash, Sujata Chakraborty, Chinmay Giri, Sourav K. Pani, Subhendu Kumar |
author_facet | Dash, Sujata Chakraborty, Chinmay Giri, Sourav K. Pani, Subhendu Kumar |
author_sort | Dash, Sujata |
collection | PubMed |
description | Covid-19 disease caused by novel coronavirus (SARS-CoV-2) is a highly contagious epidemic that originated in Wuhan, Hubei Province of China in late December 2019. World Health Organization (WHO) declared Covid-19 as a pandemic on 12th March 2020. Researchers and policy makers are designing strategies to control the pandemic in order to minimize its impact on human health and economy round the clock. The SARS-CoV-2 virus transmits mostly through respiratory droplets and through contaminated surfacesin human body.Securing an appropriate level of safety during the pandemic situation is a highly problematic issue which resulted from the transportation sector which has been hit hard by COVID-19. This paper focuses on developing an intelligent computing model for forecasting the outbreak of COVID-19. The Facebook Prophet model predicts 90 days future values including the peak date of the confirmed cases of COVID-19 for six worst hit countries of the world including India and six high incidence states of India. The model also identifies five significant changepoints in the growth curve of confirmed cases of India which indicate the impact of the interventions imposed by Government of India on the growth rate of the infection. The goodness-of-fit of the model measures 85% MAPE for all six countries and all six states of India. The above computational analysis may be able to throw some light on planning and management of healthcare system and infrastructure. |
format | Online Article Text |
id | pubmed-8364174 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Published by Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-83641742021-08-15 Intelligent computing on time-series data analysis and prediction of COVID-19 pandemics Dash, Sujata Chakraborty, Chinmay Giri, Sourav K. Pani, Subhendu Kumar Pattern Recognit Lett Article Covid-19 disease caused by novel coronavirus (SARS-CoV-2) is a highly contagious epidemic that originated in Wuhan, Hubei Province of China in late December 2019. World Health Organization (WHO) declared Covid-19 as a pandemic on 12th March 2020. Researchers and policy makers are designing strategies to control the pandemic in order to minimize its impact on human health and economy round the clock. The SARS-CoV-2 virus transmits mostly through respiratory droplets and through contaminated surfacesin human body.Securing an appropriate level of safety during the pandemic situation is a highly problematic issue which resulted from the transportation sector which has been hit hard by COVID-19. This paper focuses on developing an intelligent computing model for forecasting the outbreak of COVID-19. The Facebook Prophet model predicts 90 days future values including the peak date of the confirmed cases of COVID-19 for six worst hit countries of the world including India and six high incidence states of India. The model also identifies five significant changepoints in the growth curve of confirmed cases of India which indicate the impact of the interventions imposed by Government of India on the growth rate of the infection. The goodness-of-fit of the model measures 85% MAPE for all six countries and all six states of India. The above computational analysis may be able to throw some light on planning and management of healthcare system and infrastructure. Published by Elsevier B.V. 2021-11 2021-08-14 /pmc/articles/PMC8364174/ /pubmed/34413555 http://dx.doi.org/10.1016/j.patrec.2021.07.027 Text en © 2021 Published by Elsevier B.V. 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 Dash, Sujata Chakraborty, Chinmay Giri, Sourav K. Pani, Subhendu Kumar Intelligent computing on time-series data analysis and prediction of COVID-19 pandemics |
title | Intelligent computing on time-series data analysis and prediction of COVID-19 pandemics |
title_full | Intelligent computing on time-series data analysis and prediction of COVID-19 pandemics |
title_fullStr | Intelligent computing on time-series data analysis and prediction of COVID-19 pandemics |
title_full_unstemmed | Intelligent computing on time-series data analysis and prediction of COVID-19 pandemics |
title_short | Intelligent computing on time-series data analysis and prediction of COVID-19 pandemics |
title_sort | intelligent computing on time-series data analysis and prediction of covid-19 pandemics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8364174/ https://www.ncbi.nlm.nih.gov/pubmed/34413555 http://dx.doi.org/10.1016/j.patrec.2021.07.027 |
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