Vaccine uptake and constrained decision making: The case of Covid-19

Abstract

Policy makers require support in conceptualizing and assessing the impact that vaccination policies can have on the proportion of the population being vaccinated against COVID-19. To this purpose, we propose a behavioural economics-based framework to model vaccination choices. We calibrate our model using up-to-date surveys on people attitudes toward vaccination as well as estimates of COVID-19 infection and mortality rates and vaccine efficacy for the UK population. Our findings show that vaccine campaigns hardly reach herd immunity if the sceptics have real-time information on the proportion of the population being vaccinated and the negationists do not change their attitudes toward vaccination. Based on our results, we discuss the main implications of the model's application in the context of nudging and voluntariness versus mandatory rule-based policies.

Keywords: COVID-19; Decision making; Health beliefs; Nudging; Public health; United Kingdom; Vaccine campaign.

Fig. 1

The net benefit of the vaccination as a function of the share of immune people in UK. Notes: The horizontal axis shows the number of vaccinated people in the first period; the vertical axis shows the utility function of rational people. Lines are shown for positive values of the utility function of people aged 80+, 60–79, 40–59, 20–39, based on parameters as in Table 2; people aged 0–19 are not shown as they never have positive utility.

Fig. 2

The net benefit of the vaccination as a function of the updating coefficient. Notes: The horizontal axis shows the updating coefficient referring to the vaccine safety (α = 1 means p_H = 10^−3; α = 0 means p_H = 10⁻⁶); the vertical axis shows the utility function of skeptic people. Lines are shown for positive values of the utility function of people aged 80+, 60–79, 40–59, 20–39, based on parameters as in Table 3; people aged 0–19 are not shown as they never have positive utility.

Fig. 3

The net benefit of the vaccination as a function of the updating coefficient. Notes: The horizontal axis shows the updating coefficient referring to the vaccine safety (β = 1 means fully efficacy, i.e., p_EF = 100%;^; β = 0 means no efficacy, i.e., p_EF = 0%); the vertical axis shows the utility function of skeptic people. Lines are shown for positive values of the utility function of people aged 80+, 60–79, 40–59, 20–39, based on parameters as in Table 4; people aged 0–19 are not shown as they never have positive utility for efficacies below 100%.