  We analyze repeated cross-sectional survey data collected by the Institute of
Global Health Innovation, to characterize the perception and behavior of the
Italian population during the Covid-19 pandemic, focusing on the period that
spans from April to November 2020. To accomplish this goal, we propose a
Bayesian dynamic latent-class regression model, that accounts for the effect of
sampling bias including survey weights into the likelihood function. According
to the proposed approach, attitudes towards Covid-19 are described via three
ideal behaviors that are fixed over time, corresponding to different degrees of
compliance with spread-preventive measures. The overall tendency toward a
specific profile dynamically changes across survey waves via a latent Gaussian
process regression, that adjusts for subject-specific covariates. We illustrate
the dynamic evolution of Italians' behaviors during the pandemic, providing
insights on how the proportion of ideal behaviors has varied during the phases
of the lockdown, while measuring the effect of age, sex, region and employment
of the respondents on the attitude toward Covid-19.
