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An Enhanced SEIR Model for Prediction of COVID-19 with Vaccination Effect
Currently, the spread of COVID-19 is running at a constant pace. The current situation is not so alarming, but every pandemic has a history of three waves. Two waves have been seen, and now expecting the third wave. Compartmental models are one of the methods that predict the severity of a pandemic....
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9145292/ https://www.ncbi.nlm.nih.gov/pubmed/35629315 http://dx.doi.org/10.3390/life12050647 |
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author | Poonia, Ramesh Chandra Saudagar, Abdul Khader Jilani Altameem, Abdullah Alkhathami, Mohammed Khan, Muhammad Badruddin Hasanat, Mozaherul Hoque Abul |
author_facet | Poonia, Ramesh Chandra Saudagar, Abdul Khader Jilani Altameem, Abdullah Alkhathami, Mohammed Khan, Muhammad Badruddin Hasanat, Mozaherul Hoque Abul |
author_sort | Poonia, Ramesh Chandra |
collection | PubMed |
description | Currently, the spread of COVID-19 is running at a constant pace. The current situation is not so alarming, but every pandemic has a history of three waves. Two waves have been seen, and now expecting the third wave. Compartmental models are one of the methods that predict the severity of a pandemic. An enhanced SEIR model is expected to predict the new cases of COVID-19. The proposed model has an additional compartment of vaccination. This proposed model is the SEIRV model that predicts the severity of COVID-19 when the population is vaccinated. The proposed model is simulated with three conditions. The first condition is when social distancing is not incorporated, while the second condition is when social distancing is included. The third one condition is when social distancing is combined when the population is vaccinated. The result shows an epidemic growth rate of about 0.06 per day, and the number of infected people doubles every 10.7 days. Still, with imparting social distancing, the proposed model obtained the value of R(0) is 1.3. Vaccination of infants and kids will be considered as future work. |
format | Online Article Text |
id | pubmed-9145292 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91452922022-05-29 An Enhanced SEIR Model for Prediction of COVID-19 with Vaccination Effect Poonia, Ramesh Chandra Saudagar, Abdul Khader Jilani Altameem, Abdullah Alkhathami, Mohammed Khan, Muhammad Badruddin Hasanat, Mozaherul Hoque Abul Life (Basel) Article Currently, the spread of COVID-19 is running at a constant pace. The current situation is not so alarming, but every pandemic has a history of three waves. Two waves have been seen, and now expecting the third wave. Compartmental models are one of the methods that predict the severity of a pandemic. An enhanced SEIR model is expected to predict the new cases of COVID-19. The proposed model has an additional compartment of vaccination. This proposed model is the SEIRV model that predicts the severity of COVID-19 when the population is vaccinated. The proposed model is simulated with three conditions. The first condition is when social distancing is not incorporated, while the second condition is when social distancing is included. The third one condition is when social distancing is combined when the population is vaccinated. The result shows an epidemic growth rate of about 0.06 per day, and the number of infected people doubles every 10.7 days. Still, with imparting social distancing, the proposed model obtained the value of R(0) is 1.3. Vaccination of infants and kids will be considered as future work. MDPI 2022-04-27 /pmc/articles/PMC9145292/ /pubmed/35629315 http://dx.doi.org/10.3390/life12050647 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Poonia, Ramesh Chandra Saudagar, Abdul Khader Jilani Altameem, Abdullah Alkhathami, Mohammed Khan, Muhammad Badruddin Hasanat, Mozaherul Hoque Abul An Enhanced SEIR Model for Prediction of COVID-19 with Vaccination Effect |
title | An Enhanced SEIR Model for Prediction of COVID-19 with Vaccination Effect |
title_full | An Enhanced SEIR Model for Prediction of COVID-19 with Vaccination Effect |
title_fullStr | An Enhanced SEIR Model for Prediction of COVID-19 with Vaccination Effect |
title_full_unstemmed | An Enhanced SEIR Model for Prediction of COVID-19 with Vaccination Effect |
title_short | An Enhanced SEIR Model for Prediction of COVID-19 with Vaccination Effect |
title_sort | enhanced seir model for prediction of covid-19 with vaccination effect |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9145292/ https://www.ncbi.nlm.nih.gov/pubmed/35629315 http://dx.doi.org/10.3390/life12050647 |
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