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Modeling Consequences of COVID-19 and Assessing Its Epidemiological Parameters: A System Dynamics Approach

In 2020, coronavirus (COVID-19) was declared a global pandemic and it remains prevalent today. A necessity to model the transmission of the virus has emerged as a result of COVID-19’s exceedingly contagious characteristics and its rapid propagation throughout the world. Assessing the incidence of in...

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Autores principales: Rehman, Ateekh Ur, Mian, Syed Hammad, Usmani, Yusuf Siraj, Abidi, Mustufa Haider, Mohammed, Muneer Khan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858678/
https://www.ncbi.nlm.nih.gov/pubmed/36673628
http://dx.doi.org/10.3390/healthcare11020260
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author Rehman, Ateekh Ur
Mian, Syed Hammad
Usmani, Yusuf Siraj
Abidi, Mustufa Haider
Mohammed, Muneer Khan
author_facet Rehman, Ateekh Ur
Mian, Syed Hammad
Usmani, Yusuf Siraj
Abidi, Mustufa Haider
Mohammed, Muneer Khan
author_sort Rehman, Ateekh Ur
collection PubMed
description In 2020, coronavirus (COVID-19) was declared a global pandemic and it remains prevalent today. A necessity to model the transmission of the virus has emerged as a result of COVID-19’s exceedingly contagious characteristics and its rapid propagation throughout the world. Assessing the incidence of infection could enable policymakers to identify measures to halt the pandemic and gauge the required capacity of healthcare centers. Therefore, modeling the susceptibility, exposure, infection, and recovery in relation to the COVID-19 pandemic is crucial for the adoption of interventions by regulatory authorities. Fundamental factors, such as the infection rate, mortality rate, and recovery rate, must be considered in order to accurately represent the behavior of the pandemic using mathematical models. The difficulty in creating a mathematical model is in identifying the real model variables. Parameters might vary significantly across models, which can result in variations in the simulation results because projections primarily rely on a particular dataset. The purpose of this work was to establish a susceptible–exposed–infected–recovered (SEIR) model describing the propagation of the COVID-19 outbreak throughout the Kingdom of Saudi Arabia (KSA). The goal of this study was to derive the essential COVID-19 epidemiological factors from actual data. System dynamics modeling and design of experiment approaches were used to determine the most appropriate combination of epidemiological parameters and the influence of COVID-19. This study investigates how epidemiological variables such as seasonal amplitude, social awareness impact, and waning time can be adapted to correctly estimate COVID-19 scenarios such as the number of infected persons on a daily basis in KSA. This model can also be utilized to ascertain how stress (or hospital capacity) affects the percentage of hospitalizations and the number of deaths. Additionally, the results of this study can be used to establish policies or strategies for monitoring or restricting COVID-19 in Saudi Arabia.
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spelling pubmed-98586782023-01-21 Modeling Consequences of COVID-19 and Assessing Its Epidemiological Parameters: A System Dynamics Approach Rehman, Ateekh Ur Mian, Syed Hammad Usmani, Yusuf Siraj Abidi, Mustufa Haider Mohammed, Muneer Khan Healthcare (Basel) Article In 2020, coronavirus (COVID-19) was declared a global pandemic and it remains prevalent today. A necessity to model the transmission of the virus has emerged as a result of COVID-19’s exceedingly contagious characteristics and its rapid propagation throughout the world. Assessing the incidence of infection could enable policymakers to identify measures to halt the pandemic and gauge the required capacity of healthcare centers. Therefore, modeling the susceptibility, exposure, infection, and recovery in relation to the COVID-19 pandemic is crucial for the adoption of interventions by regulatory authorities. Fundamental factors, such as the infection rate, mortality rate, and recovery rate, must be considered in order to accurately represent the behavior of the pandemic using mathematical models. The difficulty in creating a mathematical model is in identifying the real model variables. Parameters might vary significantly across models, which can result in variations in the simulation results because projections primarily rely on a particular dataset. The purpose of this work was to establish a susceptible–exposed–infected–recovered (SEIR) model describing the propagation of the COVID-19 outbreak throughout the Kingdom of Saudi Arabia (KSA). The goal of this study was to derive the essential COVID-19 epidemiological factors from actual data. System dynamics modeling and design of experiment approaches were used to determine the most appropriate combination of epidemiological parameters and the influence of COVID-19. This study investigates how epidemiological variables such as seasonal amplitude, social awareness impact, and waning time can be adapted to correctly estimate COVID-19 scenarios such as the number of infected persons on a daily basis in KSA. This model can also be utilized to ascertain how stress (or hospital capacity) affects the percentage of hospitalizations and the number of deaths. Additionally, the results of this study can be used to establish policies or strategies for monitoring or restricting COVID-19 in Saudi Arabia. MDPI 2023-01-13 /pmc/articles/PMC9858678/ /pubmed/36673628 http://dx.doi.org/10.3390/healthcare11020260 Text en © 2023 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
Rehman, Ateekh Ur
Mian, Syed Hammad
Usmani, Yusuf Siraj
Abidi, Mustufa Haider
Mohammed, Muneer Khan
Modeling Consequences of COVID-19 and Assessing Its Epidemiological Parameters: A System Dynamics Approach
title Modeling Consequences of COVID-19 and Assessing Its Epidemiological Parameters: A System Dynamics Approach
title_full Modeling Consequences of COVID-19 and Assessing Its Epidemiological Parameters: A System Dynamics Approach
title_fullStr Modeling Consequences of COVID-19 and Assessing Its Epidemiological Parameters: A System Dynamics Approach
title_full_unstemmed Modeling Consequences of COVID-19 and Assessing Its Epidemiological Parameters: A System Dynamics Approach
title_short Modeling Consequences of COVID-19 and Assessing Its Epidemiological Parameters: A System Dynamics Approach
title_sort modeling consequences of covid-19 and assessing its epidemiological parameters: a system dynamics approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858678/
https://www.ncbi.nlm.nih.gov/pubmed/36673628
http://dx.doi.org/10.3390/healthcare11020260
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