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Short-term predictions and prevention strategies for COVID-19: A model-based study
An outbreak of respiratory disease caused by a novel coronavirus is ongoing from December 2019. As of December 14, 2020, it has caused an epidemic outbreak with more than 73 million confirmed infections and above 1.5 million reported deaths worldwide. During this period of an epidemic when human-to-...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8015415/ https://www.ncbi.nlm.nih.gov/pubmed/33828346 http://dx.doi.org/10.1016/j.amc.2021.126251 |
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author | Nadim, Sk Shahid Ghosh, Indrajit Chattopadhyay, Joydev |
author_facet | Nadim, Sk Shahid Ghosh, Indrajit Chattopadhyay, Joydev |
author_sort | Nadim, Sk Shahid |
collection | PubMed |
description | An outbreak of respiratory disease caused by a novel coronavirus is ongoing from December 2019. As of December 14, 2020, it has caused an epidemic outbreak with more than 73 million confirmed infections and above 1.5 million reported deaths worldwide. During this period of an epidemic when human-to-human transmission is established and reported cases of coronavirus disease 2019 (COVID-19) are rising worldwide, investigation of control strategies and forecasting are necessary for health care planning. In this study, we propose and analyze a compartmental epidemic model of COVID-19 to predict and control the outbreak. The basic reproduction number and the control reproduction number are calculated analytically. A detailed stability analysis of the model is performed to observe the dynamics of the system. We calibrated the proposed model to fit daily data from the United Kingdom (UK) where the situation is still alarming. Our findings suggest that independent self-sustaining human-to-human spread ([Formula: see text] [Formula: see text]) is already present. Short-term predictions show that the decreasing trend of new COVID-19 cases is well captured by the model. Further, we found that effective management of quarantined individuals is more effective than management of isolated individuals to reduce the disease burden. Thus, if limited resources are available, then investing on the quarantined individuals will be more fruitful in terms of reduction of cases. |
format | Online Article Text |
id | pubmed-8015415 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-80154152021-04-02 Short-term predictions and prevention strategies for COVID-19: A model-based study Nadim, Sk Shahid Ghosh, Indrajit Chattopadhyay, Joydev Appl Math Comput Article An outbreak of respiratory disease caused by a novel coronavirus is ongoing from December 2019. As of December 14, 2020, it has caused an epidemic outbreak with more than 73 million confirmed infections and above 1.5 million reported deaths worldwide. During this period of an epidemic when human-to-human transmission is established and reported cases of coronavirus disease 2019 (COVID-19) are rising worldwide, investigation of control strategies and forecasting are necessary for health care planning. In this study, we propose and analyze a compartmental epidemic model of COVID-19 to predict and control the outbreak. The basic reproduction number and the control reproduction number are calculated analytically. A detailed stability analysis of the model is performed to observe the dynamics of the system. We calibrated the proposed model to fit daily data from the United Kingdom (UK) where the situation is still alarming. Our findings suggest that independent self-sustaining human-to-human spread ([Formula: see text] [Formula: see text]) is already present. Short-term predictions show that the decreasing trend of new COVID-19 cases is well captured by the model. Further, we found that effective management of quarantined individuals is more effective than management of isolated individuals to reduce the disease burden. Thus, if limited resources are available, then investing on the quarantined individuals will be more fruitful in terms of reduction of cases. Elsevier Inc. 2021-09-01 2021-04-01 /pmc/articles/PMC8015415/ /pubmed/33828346 http://dx.doi.org/10.1016/j.amc.2021.126251 Text en © 2021 Elsevier Inc. All rights reserved. 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 Nadim, Sk Shahid Ghosh, Indrajit Chattopadhyay, Joydev Short-term predictions and prevention strategies for COVID-19: A model-based study |
title | Short-term predictions and prevention strategies for COVID-19: A model-based study |
title_full | Short-term predictions and prevention strategies for COVID-19: A model-based study |
title_fullStr | Short-term predictions and prevention strategies for COVID-19: A model-based study |
title_full_unstemmed | Short-term predictions and prevention strategies for COVID-19: A model-based study |
title_short | Short-term predictions and prevention strategies for COVID-19: A model-based study |
title_sort | short-term predictions and prevention strategies for covid-19: a model-based study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8015415/ https://www.ncbi.nlm.nih.gov/pubmed/33828346 http://dx.doi.org/10.1016/j.amc.2021.126251 |
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