Vaccination Dynamics of Age-Structured Populations in Higher-Order Social Networks

Voluntary vaccination is essential to protect oneself from infection and suppress the spread of infectious diseases. Voluntary vaccination behavior is influenced by factors, such as age and interaction patterns. Differences in health consciousness and risk perception based on age result in heterogeneity in vaccination behavior among different age groups. Higher-order interactions among individuals of various ages facilitate the dissemination of vaccine-related information, further influencing vaccination intentions. To investigate the impact of individual age and interaction patterns on vaccination behavior, we propose an epidemic-game coevolution model in which age structure and higher-order interactions are considered. Based on the theoretical framework of epidemic-game coevolution, this work calculates the evolutionarily stable strategies and dynamic equilibrium under imitation dynamics in the well-mixed population. Extensive numerical experiments show that infants and the elderly exhibit conservative attitudes toward vaccination, and the vaccination levels of these two groups have no significant impact on the vaccination behavior of other age groups. The vaccination behavior of children is highly active, while the vaccination behavior of adults depends on the relative cost of vaccination. The increase in vaccination levels among children and adults leads to a decrease in vaccination levels in other groups. Furthermore, the infants exhibit the lowest level of vaccination, while the children have the highest vaccination rate. Higher-order interactions significantly enhance vaccination levels among children and adults.

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Publication Details

Journal
IEEE Transactions on Systems Man and Cybernetics Systems
Published
2025-04-22
DOI
https://doi.org/10.1109/tsmc.2025.3559885
Citations
3
Primary Topic
COVID-19 epidemiological studies
Type
article
Field-Weighted Citation Impact
4.10

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article

Vaccination Dynamics of Age-Structured Populations in Higher-Order Social Networks

Yanbing Liu, Yanyi Nie, Wei Wang, Tao Lin
3 citations
IEEE Transactions on Systems Man and Cybernetics Systems
COVID-19 epidemiological studies
4.10
article

Vaccination Dynamics of Age-Structured Populations in Higher-Order Social Networks

Yanbing Liu, Yanyi Nie, Wei Wang, Tao Lin
article en
3 citations

Abstract

Voluntary vaccination is essential to protect oneself from infection and suppress the spread of infectious diseases. Voluntary vaccination behavior is influenced by factors, such as age and interaction patterns. Differences in health consciousness and risk perception based on age result in heterogeneity in vaccination behavior among different age groups. Higher-order interactions among individuals of various ages facilitate the dissemination of vaccine-related information, further influencing vaccination intentions. To investigate the impact of individual age and interaction patterns on vaccination behavior, we propose an epidemic-game coevolution model in which age structure and higher-order interactions are considered. Based on the theoretical framework of epidemic-game coevolution, this work calculates the evolutionarily stable strategies and dynamic equilibrium under imitation dynamics in the well-mixed population. Extensive numerical experiments show that infants and the elderly exhibit conservative attitudes toward vaccination, and the vaccination levels of these two groups have no significant impact on the vaccination behavior of other age groups. The vaccination behavior of children is highly active, while the vaccination behavior of adults depends on the relative cost of vaccination. The increase in vaccination levels among children and adults leads to a decrease in vaccination levels in other groups. Furthermore, the infants exhibit the lowest level of vaccination, while the children have the highest vaccination rate. Higher-order interactions significantly enhance vaccination levels among children and adults.

IEEE Transactions on Systems Man and Cybernetics SystemsVol. 55(7)
Sichuan University (CN), Chengdu University (CN), Chongqing Public Health Medical Center (CN), Chongqing Medical University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 7%
COVID-19 epidemiological studies
4.10
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