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Analyzing the COVID-19 vaccination behavior based on epidemic model with awareness-information

BACKGROUND: The widespread use of effective COVID-19 vaccines could prevent substantial morbidity and mortality. Individual decision behavior about whether or not to be vaccinated plays an important role in achieving adequate vaccination coverage and herd immunity. METHODS: This research proposes a...

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Autores principales: Zuo, Chao, Zhu, Fenping, Meng, Zeyang, Ling, Yuting, Zheng, Yuzhi, Zhao, Xueke
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Authors. Published by Elsevier B.V. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8770258/
https://www.ncbi.nlm.nih.gov/pubmed/35066164
http://dx.doi.org/10.1016/j.meegid.2022.105218
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author Zuo, Chao
Zhu, Fenping
Meng, Zeyang
Ling, Yuting
Zheng, Yuzhi
Zhao, Xueke
author_facet Zuo, Chao
Zhu, Fenping
Meng, Zeyang
Ling, Yuting
Zheng, Yuzhi
Zhao, Xueke
author_sort Zuo, Chao
collection PubMed
description BACKGROUND: The widespread use of effective COVID-19 vaccines could prevent substantial morbidity and mortality. Individual decision behavior about whether or not to be vaccinated plays an important role in achieving adequate vaccination coverage and herd immunity. METHODS: This research proposes a new susceptible–vaccinated–exposed–infected–recovered with awareness-information (SEIR/V-AI) model to study the interaction between vaccination and information dissemination. Information creation rate and information sensitivity are introduced to understand the individual decision behavior of COVID-19 vaccination. We then analyze the dynamical evolution of the system and validate the analysis by numerical simulation. RESULTS: The decision behavior of COVID-19 vaccination in China and the United States are analyzed. The results showed the coefficient of information creation and the information sensitivity affect vaccination behavior of individuals. CONCLUSIONS: The information-driven vaccination is an effective way to curb the COVID-19 spreading. Besides, to solve vaccine hesitancy and free-ride, the government needs to disseminate accurate information about vaccines safety to alleviate public concerns, and provide the widespread public educational campaigns and communication to guide individuals to act in group interests rather than self-interest and reduce the temptation to free-riding, which often results from individuals who are inadequately informed about vaccines and thus blindly imitate free-riding behavior.
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spelling pubmed-87702582022-01-20 Analyzing the COVID-19 vaccination behavior based on epidemic model with awareness-information Zuo, Chao Zhu, Fenping Meng, Zeyang Ling, Yuting Zheng, Yuzhi Zhao, Xueke Infect Genet Evol Article BACKGROUND: The widespread use of effective COVID-19 vaccines could prevent substantial morbidity and mortality. Individual decision behavior about whether or not to be vaccinated plays an important role in achieving adequate vaccination coverage and herd immunity. METHODS: This research proposes a new susceptible–vaccinated–exposed–infected–recovered with awareness-information (SEIR/V-AI) model to study the interaction between vaccination and information dissemination. Information creation rate and information sensitivity are introduced to understand the individual decision behavior of COVID-19 vaccination. We then analyze the dynamical evolution of the system and validate the analysis by numerical simulation. RESULTS: The decision behavior of COVID-19 vaccination in China and the United States are analyzed. The results showed the coefficient of information creation and the information sensitivity affect vaccination behavior of individuals. CONCLUSIONS: The information-driven vaccination is an effective way to curb the COVID-19 spreading. Besides, to solve vaccine hesitancy and free-ride, the government needs to disseminate accurate information about vaccines safety to alleviate public concerns, and provide the widespread public educational campaigns and communication to guide individuals to act in group interests rather than self-interest and reduce the temptation to free-riding, which often results from individuals who are inadequately informed about vaccines and thus blindly imitate free-riding behavior. The Authors. Published by Elsevier B.V. 2022-03 2022-01-20 /pmc/articles/PMC8770258/ /pubmed/35066164 http://dx.doi.org/10.1016/j.meegid.2022.105218 Text en © 2022 The Authors Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Zuo, Chao
Zhu, Fenping
Meng, Zeyang
Ling, Yuting
Zheng, Yuzhi
Zhao, Xueke
Analyzing the COVID-19 vaccination behavior based on epidemic model with awareness-information
title Analyzing the COVID-19 vaccination behavior based on epidemic model with awareness-information
title_full Analyzing the COVID-19 vaccination behavior based on epidemic model with awareness-information
title_fullStr Analyzing the COVID-19 vaccination behavior based on epidemic model with awareness-information
title_full_unstemmed Analyzing the COVID-19 vaccination behavior based on epidemic model with awareness-information
title_short Analyzing the COVID-19 vaccination behavior based on epidemic model with awareness-information
title_sort analyzing the covid-19 vaccination behavior based on epidemic model with awareness-information
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8770258/
https://www.ncbi.nlm.nih.gov/pubmed/35066164
http://dx.doi.org/10.1016/j.meegid.2022.105218
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