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Estimation of Mortality Rate of COVID-19 in India using SEIRD Model

In India, the number of infections is rapidly increased with a mounting death toll during the second wave of Coronavirus disease (COVID-19). To measure the severity of the said disease, the mortality rate plays an important role. In this research work, the mortality rate of COVID-19 is estimated by...

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Autores principales: Selvamuthu, Dharmaraja, Khichar, Deepak, Kalita, Priyanka, Jain, Vidyottama
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer India 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8450558/
http://dx.doi.org/10.1007/s12597-021-00557-x
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author Selvamuthu, Dharmaraja
Khichar, Deepak
Kalita, Priyanka
Jain, Vidyottama
author_facet Selvamuthu, Dharmaraja
Khichar, Deepak
Kalita, Priyanka
Jain, Vidyottama
author_sort Selvamuthu, Dharmaraja
collection PubMed
description In India, the number of infections is rapidly increased with a mounting death toll during the second wave of Coronavirus disease (COVID-19). To measure the severity of the said disease, the mortality rate plays an important role. In this research work, the mortality rate of COVID-19 is estimated by using the Susceptible-Exposed-Infected-Recovered-Dead (SEIRD) epidemiological model. As the disease contains a significant amount of uncertainty, a fundamental SEIRD model with minimal assumptions is employed. Further, a basic method is proposed to obtain time-dependent estimations of the parameters of the SEIRD model by using historical data. From our proposed model and with the predictive analysis, it is expected that the infection may go rise in the month of May-2021 and the mortality rate could go as high as 1.8%. Such high rates of mortality may be used as a measure to understand the severity of the situation.
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spelling pubmed-84505582021-09-20 Estimation of Mortality Rate of COVID-19 in India using SEIRD Model Selvamuthu, Dharmaraja Khichar, Deepak Kalita, Priyanka Jain, Vidyottama OPSEARCH Case Report In India, the number of infections is rapidly increased with a mounting death toll during the second wave of Coronavirus disease (COVID-19). To measure the severity of the said disease, the mortality rate plays an important role. In this research work, the mortality rate of COVID-19 is estimated by using the Susceptible-Exposed-Infected-Recovered-Dead (SEIRD) epidemiological model. As the disease contains a significant amount of uncertainty, a fundamental SEIRD model with minimal assumptions is employed. Further, a basic method is proposed to obtain time-dependent estimations of the parameters of the SEIRD model by using historical data. From our proposed model and with the predictive analysis, it is expected that the infection may go rise in the month of May-2021 and the mortality rate could go as high as 1.8%. Such high rates of mortality may be used as a measure to understand the severity of the situation. Springer India 2021-09-20 2023 /pmc/articles/PMC8450558/ http://dx.doi.org/10.1007/s12597-021-00557-x Text en © Operational Research Society of India 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Case Report
Selvamuthu, Dharmaraja
Khichar, Deepak
Kalita, Priyanka
Jain, Vidyottama
Estimation of Mortality Rate of COVID-19 in India using SEIRD Model
title Estimation of Mortality Rate of COVID-19 in India using SEIRD Model
title_full Estimation of Mortality Rate of COVID-19 in India using SEIRD Model
title_fullStr Estimation of Mortality Rate of COVID-19 in India using SEIRD Model
title_full_unstemmed Estimation of Mortality Rate of COVID-19 in India using SEIRD Model
title_short Estimation of Mortality Rate of COVID-19 in India using SEIRD Model
title_sort estimation of mortality rate of covid-19 in india using seird model
topic Case Report
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8450558/
http://dx.doi.org/10.1007/s12597-021-00557-x
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