Dynamic modeling of the Italians' attitude towards Covid-19

TL;DR

This paper introduces a Bayesian dynamic latent-class regression model to analyze how Italians' attitudes and behaviors towards Covid-19 evolved from April to November 2020, accounting for sampling bias and individual covariates.

Contribution

It proposes a novel Bayesian dynamic modeling approach that captures temporal changes in attitudes and adjusts for sampling bias in survey data.

Findings

Behavioral profiles shifted during lockdown phases

Age, sex, region, and employment influenced attitudes

Proportion of compliance behaviors varied over time

Abstract

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.