Analyzing the COVID-19 vaccination behavior based on epidemic model with awareness-information.
Infect Genet Evol
; 98: 105218, 2022 03.
Article
in English
| MEDLINE | ID: covidwho-1641527
ABSTRACT
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.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Patient Acceptance of Health Care
/
Vaccination
/
COVID-19 Vaccines
/
SARS-CoV-2
/
COVID-19
Type of study:
Observational study
/
Prognostic study
Topics:
Vaccines
Limits:
Humans
Country/Region as subject:
North America
Language:
English
Journal:
Infect Genet Evol
Journal subject:
Biology
/
Communicable Diseases
/
Genetics
Year:
2022
Document Type:
Article
Affiliation country:
J.meegid.2022.105218
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