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.
翻译:我们分析了全球健康创新研究所收集的多次跨部门调查数据,以描述意大利民众在Covid-19大流行期间的认知和行为,重点是2020年4月至11月的时期。为了实现这一目标,我们提议采用贝叶斯动态潜值级回归模型,以说明抽样偏差的影响,包括调查对概率功能的加权。根据拟议方法,对Covid-19的态度是通过三种理想行为描述的,这三种理想行为是长期固定的,与对扩散预防措施的不同遵守程度相对应的。通过潜伏高斯进程回归对各调查波进行具体动态变化的总体趋势,这种变化将调整特定主题的共变情况。我们介绍了意大利人在该大流行期间的行为动态演变,就理想行为在锁定阶段的不同比例提供了深刻见解,同时测量了受访者年龄、性别、区域和就业对Covid-19大流行态度的影响。