A behavioural modelling approach to assess the impact of COVID-19 vaccine hesitancy

Abstract

In this paper we introduce a compartmental epidemic model describing the transmission of the COVID-19 disease in presence of non-mandatory vaccination. The model takes into account the hesitancy and refusal of vaccination. To this aim, we employ the information index, which mimics the idea that individuals take their decision on vaccination based not only on the present but also on the past information about the spread of the disease. Theoretical analysis and simulations show clearly as a voluntary vaccination can certainly reduce the impact of the disease but it is unable to eliminate it. We also show how the information-related parameters affect the dynamics of the disease. In particular, the hesitancy and refusal of vaccination is better contained in case of large information coverage and small memory characteristic time. Finally, the possible influence of seasonality is also investigated.

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

“… Parameter Meanings Baseline value Reference Λ Net inflow of susceptibles 242 Assumed β Transmission rate 0. 468 · 10 −4 [44] γ Fraction of exposed class who become infective 0.1818 [45] k Factor of vaccine ineffectiveness 0.05 Estimated λ Rate of the information-dependent vaccination 0.2 [36] ε Recovery rate for infected individual 0.278 [46] g Limited extent of the global information influence on the susceptible individuals 0.5 [36] θ Fraction of individuals who are vaccinated 0.011 [32] ρ Rate of losing vaccine immunity 0.005 Estimated d Natural death rate 1.07 · 10 −2 [47] T the average information delays 30 Estimated a Fraction of information coverage 0.8 [48] B the sensitivity of vaccinating behavior to changes in prevalence 0.1 Estimated μ Decay rate of information due to the decreasing quality of the information 1/3 [48] …”

Bayesian uncertainty quantification to identify population level vaccine hesitancy behaviours

“…This function is intended to model the way in which reported case and vaccination data may increase the probability of a individual seeking vaccination. It is well established that the incidence of a disease in a community will tend to increase the likelihood of an individual to seek vaccination (Buonomo, 2020;Buonomo et al, 2022). If there are no vaccination safety concerns in the community, any delays in vaccine uptake may be due to complacency in regions where there was initially few COVID-19 outbreaks (such as Australia and New Zealand).…”

“…Unfortunately, there have been few mathematical modelling studies that consider this dynamic. Some examples explore the impacts of information on the behaviour related to vaccine hesitancy (Buonomo, 2020;Oduro et al, 2021) and strategies to counter mis-information (Buonomo et al, 2022;Lau et al, 2021;Sinclair et al, 2022).…”

Mathematical modelling of COVID-19 transmission dynamics with vaccination: A case study in Ethiopia

“…Another problem in attaining herd immunity in the population is vaccine hesitancy in case vaccination is not mandatory, in which people are the last to decide either to get vaccinated or not. A behavioural modelling approach was used to assess the impact of hesitancy and refusal of vaccine on the dynamics of the COVID-19 [5]. In this paper, the authors showed hesitancy and refusal of vaccination is better contained in case of large information coverage and small memory characteristics.…”

“…Epidemiological modelers started to incorporate this additional intervention to see the dynamical properties of the disease and sort out some important policy directions to the public health authorities. In this aspect, there are a number of studies, from which [9, 17, 5] can be mentioned. A mathematical model with comorbidity and an optimal control-based framework to decrease COVID-19 was studied in [9].…”