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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...

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Autores principales: Dash, Sujata, Chakraborty, Chinmay, Giri, Sourav K., Pani, Subhendu Kumar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Published by Elsevier B.V. 2021
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.
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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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