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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....

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Autores principales: Poonia, Ramesh Chandra, Saudagar, Abdul Khader Jilani, Altameem, Abdullah, Alkhathami, Mohammed, Khan, Muhammad Badruddin, Hasanat, Mozaherul Hoque Abul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
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.
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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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