Stated choice analysis of preferences for COVID-19 vaccines using the Choquet integral.

We investigate preferences for COVID-19 vaccines using data from a stated choice survey conducted in the US in March 2021. To analyse the data, we embed the Choquet integral, a flexible aggregation operator for capturing attribute interactions under monotonicity constraints, into a mixed logit model. We find that effectiveness is the most important vaccine attribute, followed by risk of severe side effects and protection period. The attribute interactions reveal that non-pecuniary vaccine attributes are synergistic. Out-of-pocket costs are independent of effectiveness, incubation period, and mild side effects but exhibit moderate synergistic interactions with other attributes. Vaccine adoption is significantly more likely among individuals who identify as male, have obtained a bachelor's degree or a higher level of education, have a high household income, support the democratic party, had COVID-19, got vaccinated against the flu in winter 2020/21, and have an underlying health condition.

Preferences for using the London Underground during the COVID-19 pandemic.

The COVID-19 pandemic has drastically impacted people's travel behaviour and introduced uncertainty in the demand for public transport. To investigate user preferences for travel by London Underground during the pandemic, we conducted a stated choice experiment among its pre-pandemic users (N = 961). We analysed the collected data using multinomial and latent class logit models. Our discrete choice analysis provides two sets of results. First, we derive the crowding multiplier estimate of travel time valuation (i.e., the ratio of the value of travel time in uncrowded and crowded situations) for London underground users. The results indicate that travel time valuation of Underground users increases by 73% when it operates at technical capacity. Second, we estimate the sensitivity of the preference for the London Underground relative to the epidemic situation (confirmed new COVID-19 cases) and interventions (vaccination rates and mandatory face masks). The sensitivity analysis suggests that making face masks mandatory is a main driver for recovering the demand for the London underground. The latent class model reveals substantial preference heterogeneity. For instance, while the average effect of mandatory face masks is positive, the preferences of 30% of pre-pandemic users for travel by the Underground are negatively affected. The positive effect of mandatory face masks on the likelihood of taking the Underground is less pronounced among males with age below 40 years, and a monthly income below 10,000 GBP. The estimated preference sensitivities and crowding multipliers are relevant for supply-demand management in transit systems and the calibration of advanced epidemiological models.