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Projecting the Pandemic Trajectory through Modeling the Transmission Dynamics of COVID-19

The course of the COVID-19 pandemic has given rise to many disease trends at various population scales, ranging from local to global. Understanding these trends and the epidemiological phenomena that lead to the changing dynamics associated with disease progression is critical for public health offi...

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Autores principales: Vakil, Vahideh, Trappe, Wade
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9032231/
https://www.ncbi.nlm.nih.gov/pubmed/35457409
http://dx.doi.org/10.3390/ijerph19084541
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author Vakil, Vahideh
Trappe, Wade
author_facet Vakil, Vahideh
Trappe, Wade
author_sort Vakil, Vahideh
collection PubMed
description The course of the COVID-19 pandemic has given rise to many disease trends at various population scales, ranging from local to global. Understanding these trends and the epidemiological phenomena that lead to the changing dynamics associated with disease progression is critical for public health officials and the global community to rein in further spread of this and other virulent diseases. Classic epidemiological modeling based on dynamical systems are powerful tools used for modeling and understanding diseases, but often necessitate modifications to the classic compartmental models to reflect empirical observations. In this paper, we present a collection of extensions to the classic SIRS model to support public health decisions associated with viral pandemics. Specifically, we present models that reflect different levels of disease severity among infected individuals, capture the effect of vaccination on different population groups, capture the effect of different vaccines with different levels of effectiveness, and model the impact of a vaccine with varying number of doses. Further, our mathematical models support the investigation of a pandemic’s trend under the emergence of new variants and the associated reduction in vaccine effectiveness. Our models are supported through numerical simulations, which we use to illustrate phenomena that have been observed in the COVID-19 pandemic. Our findings also confirm observations that the mild infectious group accounts for the majority of infected individuals, and that prompt immunization results in weaker pandemic waves across all levels of infection as well as a lower number of disease-caused deaths. Finally, using our models, we demonstrate that, when dealing with a single variant and having access to a highly effective vaccine, a three-dose vaccine has a strong ability to reduce the infectious population. However, when a new variant with higher transmissibility and lower vaccine efficiency emerges, it becomes the dominant circulating variant, as was observed in the recent emergence of the Omicron variant.
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spelling pubmed-90322312022-04-23 Projecting the Pandemic Trajectory through Modeling the Transmission Dynamics of COVID-19 Vakil, Vahideh Trappe, Wade Int J Environ Res Public Health Article The course of the COVID-19 pandemic has given rise to many disease trends at various population scales, ranging from local to global. Understanding these trends and the epidemiological phenomena that lead to the changing dynamics associated with disease progression is critical for public health officials and the global community to rein in further spread of this and other virulent diseases. Classic epidemiological modeling based on dynamical systems are powerful tools used for modeling and understanding diseases, but often necessitate modifications to the classic compartmental models to reflect empirical observations. In this paper, we present a collection of extensions to the classic SIRS model to support public health decisions associated with viral pandemics. Specifically, we present models that reflect different levels of disease severity among infected individuals, capture the effect of vaccination on different population groups, capture the effect of different vaccines with different levels of effectiveness, and model the impact of a vaccine with varying number of doses. Further, our mathematical models support the investigation of a pandemic’s trend under the emergence of new variants and the associated reduction in vaccine effectiveness. Our models are supported through numerical simulations, which we use to illustrate phenomena that have been observed in the COVID-19 pandemic. Our findings also confirm observations that the mild infectious group accounts for the majority of infected individuals, and that prompt immunization results in weaker pandemic waves across all levels of infection as well as a lower number of disease-caused deaths. Finally, using our models, we demonstrate that, when dealing with a single variant and having access to a highly effective vaccine, a three-dose vaccine has a strong ability to reduce the infectious population. However, when a new variant with higher transmissibility and lower vaccine efficiency emerges, it becomes the dominant circulating variant, as was observed in the recent emergence of the Omicron variant. MDPI 2022-04-09 /pmc/articles/PMC9032231/ /pubmed/35457409 http://dx.doi.org/10.3390/ijerph19084541 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
Vakil, Vahideh
Trappe, Wade
Projecting the Pandemic Trajectory through Modeling the Transmission Dynamics of COVID-19
title Projecting the Pandemic Trajectory through Modeling the Transmission Dynamics of COVID-19
title_full Projecting the Pandemic Trajectory through Modeling the Transmission Dynamics of COVID-19
title_fullStr Projecting the Pandemic Trajectory through Modeling the Transmission Dynamics of COVID-19
title_full_unstemmed Projecting the Pandemic Trajectory through Modeling the Transmission Dynamics of COVID-19
title_short Projecting the Pandemic Trajectory through Modeling the Transmission Dynamics of COVID-19
title_sort projecting the pandemic trajectory through modeling the transmission dynamics of covid-19
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9032231/
https://www.ncbi.nlm.nih.gov/pubmed/35457409
http://dx.doi.org/10.3390/ijerph19084541
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