Assessing the impact of vaccination in a COVID-19 compartmental model

Background: The aim of this research is to understand the role played by vaccination in the dynamics of a given COVID-19 compartmental model. Most of all, how vaccination modifies the stability, sensitivity, and the reproduction number of a susceptible population. Methods: The proposed COVID-19 comp...

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Autores principales:	Ernesto P. Esteban, Lusmeralis Almodovar-Abreu
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Vaccine COVID-19 Compartmental model Coronavirus Optimal vaccine Vaccinated reproductive number Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/7c976b0aec8d400eb51b3622ade29572
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spelling	oai:doaj.org-article:7c976b0aec8d400eb51b3622ade295722021-11-24T04:32:25ZAssessing the impact of vaccination in a COVID-19 compartmental model2352-914810.1016/j.imu.2021.100795https://doaj.org/article/7c976b0aec8d400eb51b3622ade295722021-01-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352914821002641https://doaj.org/toc/2352-9148Background: The aim of this research is to understand the role played by vaccination in the dynamics of a given COVID-19 compartmental model. Most of all, how vaccination modifies the stability, sensitivity, and the reproduction number of a susceptible population. Methods: The proposed COVID-19 compartmental model (SVEIRD) has seven compartments. Namely, susceptible (S), vaccinated (V), exposed (E, infected but not yet infectious), symptomatic infectious (Is), asymptomatic infectious (Ia), recovered (R), and dead by Covid-19 disease (D).We have developed a computational code to mimic the first wave of the coronavirus pandemic in a state like New York (NYS). Findings: First a stability analysis was carried out. Next, a sensitivity analysis showed that the more relevant parameters are birth rate, transmission coefficient, and vaccine failure. We found an alternative procedure to easily calculate the vaccinated reproductive number of the proposed SVEIRD model. Our graphical results allow to make a comparison between unvaccinated (SEIRD) and vaccinated (SVEIRD) populations. In the peak of the first wave, we estimated 21% (2.5%) and 6% (0.8%) of the unvaccinated (vaccinated) susceptible population was symptomatic and asymptomatic, respectively. At 180 days of the NYS pandemic, the model forecast about 25786 deaths by coronavirus. A vaccine with 95% efficacy could reduce the number of deaths from 25786 to 3784. Conclusion: The proposed compartmental model can be used to mimic different possible scenarios of the pandemic not only in NYS, but in any country or region. Further, for an unvaccinated reproductive number R > 1, we showed that the vaccine's efficacy must be greater than the herd immunity to stop the spread of the COVID-19 disease.Ernesto P. EstebanLusmeralis Almodovar-AbreuElsevierarticleVaccineCOVID-19Compartmental modelCoronavirusOptimal vaccineVaccinated reproductive numberComputer applications to medicine. Medical informaticsR858-859.7ENInformatics in Medicine Unlocked, Vol 27, Iss , Pp 100795- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Vaccine COVID-19 Compartmental model Coronavirus Optimal vaccine Vaccinated reproductive number Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Vaccine COVID-19 Compartmental model Coronavirus Optimal vaccine Vaccinated reproductive number Computer applications to medicine. Medical informatics R858-859.7 Ernesto P. Esteban Lusmeralis Almodovar-Abreu Assessing the impact of vaccination in a COVID-19 compartmental model
description	Background: The aim of this research is to understand the role played by vaccination in the dynamics of a given COVID-19 compartmental model. Most of all, how vaccination modifies the stability, sensitivity, and the reproduction number of a susceptible population. Methods: The proposed COVID-19 compartmental model (SVEIRD) has seven compartments. Namely, susceptible (S), vaccinated (V), exposed (E, infected but not yet infectious), symptomatic infectious (Is), asymptomatic infectious (Ia), recovered (R), and dead by Covid-19 disease (D).We have developed a computational code to mimic the first wave of the coronavirus pandemic in a state like New York (NYS). Findings: First a stability analysis was carried out. Next, a sensitivity analysis showed that the more relevant parameters are birth rate, transmission coefficient, and vaccine failure. We found an alternative procedure to easily calculate the vaccinated reproductive number of the proposed SVEIRD model. Our graphical results allow to make a comparison between unvaccinated (SEIRD) and vaccinated (SVEIRD) populations. In the peak of the first wave, we estimated 21% (2.5%) and 6% (0.8%) of the unvaccinated (vaccinated) susceptible population was symptomatic and asymptomatic, respectively. At 180 days of the NYS pandemic, the model forecast about 25786 deaths by coronavirus. A vaccine with 95% efficacy could reduce the number of deaths from 25786 to 3784. Conclusion: The proposed compartmental model can be used to mimic different possible scenarios of the pandemic not only in NYS, but in any country or region. Further, for an unvaccinated reproductive number R > 1, we showed that the vaccine's efficacy must be greater than the herd immunity to stop the spread of the COVID-19 disease.
format	article
author	Ernesto P. Esteban Lusmeralis Almodovar-Abreu
author_facet	Ernesto P. Esteban Lusmeralis Almodovar-Abreu
author_sort	Ernesto P. Esteban
title	Assessing the impact of vaccination in a COVID-19 compartmental model
title_short	Assessing the impact of vaccination in a COVID-19 compartmental model
title_full	Assessing the impact of vaccination in a COVID-19 compartmental model
title_fullStr	Assessing the impact of vaccination in a COVID-19 compartmental model
title_full_unstemmed	Assessing the impact of vaccination in a COVID-19 compartmental model
title_sort	assessing the impact of vaccination in a covid-19 compartmental model
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/7c976b0aec8d400eb51b3622ade29572
work_keys_str_mv	AT ernestopesteban assessingtheimpactofvaccinationinacovid19compartmentalmodel AT lusmeralisalmodovarabreu assessingtheimpactofvaccinationinacovid19compartmentalmodel
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Assessing the impact of vaccination in a COVID-19 compartmental model

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