Intelligent computing on time-series data analysis and prediction of COVID-19 pandemics
© 2021 Published by Elsevier B.V.
| Veröffentlicht in: | Pattern recognition letters. - 1998. - 151(2021) vom: 27. Nov., Seite 69-75 |
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| Weitere Verfasser: | , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2021
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| Zugriff auf das übergeordnete Werk: | Pattern recognition letters |
| Schlagworte: | Journal Article Additive model Changepoints Facebook Prophet Model High Incidence Infectious Disease Logistic growth function Pandemics |
| Zusammenfassung: | © 2021 Published by Elsevier B.V. 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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| Beschreibung: | Date Revised 17.12.2022 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 0167-8655 |
| DOI: | 10.1016/j.patrec.2021.07.027 |