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

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.

Assessing Vaccination Prioritization Strategies for COVID-19 in South Africa Based on Age-Specific Compartment Model

“…The speed of COVID-19 vaccination is pivotal to rapid epidemic containment, however, vaccine hesitancy is a major barrier to speed up inoculation and improve vaccination coverage ( 38 , 39 ). Therefore, the government needs to provide sustained health education and communication to strengthen individual vaccine willingness ( 40 , 41 ).…”

Studying the co-evolution of information diffusion, vaccination behavior and disease transmission in multilayer networks with local and global effects

“…In addition, some scholars considered that different individuals have different impacts on the perception of information and disease, and may also produce different emotional changes; therefore, they further studied its impact on the disease transmission process with individual heterogeneity and individual emotional changes. [31][32][33][34][35][36][37][38][39] Xu et al [31] considered the effects of individual heterogeneity on disease transmission and analyzed the effects of heterogeneity on the degree of local and global disease perception within layers; the results showed that individual heterogeneity has a large effect on the proportion of disease transmission. Du et al [32] developed a threelayer network model to analyze how social media and human behavior affect disease transmission.…”

“…The most effective protective measure when an epidemic breaks out is vaccination, which is more efficient at reducing the probability of infection and the proportion of disease transmission. [30,[34][35][36][37][40][41][42][43][44] Zuo et al [35] proposed a new SEIR/V-AI model to study the interaction between vaccination behavior and hip-hop, and the results showed that individual sensitivity to information affects individual vaccination behavior. Jain et al [37] analyzed the effects of fear generated by individuals during disease transmission on information diffusion.…”

Exploring epidemic voluntary vaccinating behavior based on information-driven decisions and benefit-cost analysis

“…described the intricate interactions between information diffusion, resource allocation, and epidemic propagation [15] . Most of the above scholars established a network model for the co-spreading of information and epidemic models for certain general forms of the force of infection terms, obtained the basic reproduction number by the next-generation matrix method and derived endemic threshold criteria, the stability of steady state solutions, which are determined through the mean-field calculation, then numerical simulations using a fourth-order Runge-Kutta method, Monte Carlo (MC) method, Microscopic Markov Chain Approach (MMCA) and so on [16] , [17] , [18] .…”