标题： 意大利地区模型的策略及统计评估 显示英文标题

标题： Strategies and statistical evaluation of Italy's regional model for COVID-19 restrictions

摘要： 本研究全面评估了在新冠疫情期间用于通过颜色编码分类系统指导地区流动限制的意大利风险模型。 该研究重点是评估意大利卫生部为此目的选择的变量及其在支持公共卫生决策方面的有效性。 分析采用了一种统计框架，结合数据缩减和回归建模技术以提高可解释性和预测准确性。 应用维度缩减以解决多重共线性并简化复杂的变量结构，同时使用有序回归模型来研究缩减后的变量集与颜色区域分类之间的关系。 使用分类误差指标评估模型性能，为所选变量在解释决策过程中的充分性提供见解。 结果揭示了意大利卫生部选择的变量中存在显著的冗余，表明过多的预测变量可能会损害信息。 为了解决这个问题，本研究提出了改进且稳健的区域分类预测模型，为所提出框架提供可靠的工具，并支持公共卫生决策者。 本研究有助于定量方法的发展，旨在提高统计模型在指导公共卫生政策方面的有效性。 研究结果为在健康危机期间完善数据驱动的决策过程以及提高政策制定者可用信息的质量提供了有价值的见解。 显示英文摘要

摘要： This study presents a comprehensive assessment of the Italian risk model used during the COVID-19 pandemic to guide regional mobility restrictions through a colour-coded classification system. The research focuses on evaluating the variables selected by the Italian Ministry of Health for this purpose and their effectiveness in supporting public health decision-making. The analysis adopts a statistical framework which combines data reduction and regression modelling techniques to enhance interpretability and predictive accuracy. Dimensionality reduction is applied to address multicollinearity and simplify complex variable structures, while an ordinal regression model is employed to investigate the relationship between the reduced set of variables and the colour regional classifications. Model performance is evaluated using classification error metrics, providing insights into the adequacy of the selected variables in explaining the decision-making process. Results reveal significant redundancy within the variables chosen by the Italian Ministry of Health, suggesting that excessive predictors may compromise information. To address this, the study proposes refined and robust predictive models for regional classification, offering a reliable tool of the proposed framework and to support public health decision-makers. This study contributes to the ongoing development of quantitative methodologies aimed at improving the effectiveness of statistical models in guiding public health policies. The findings offer valuable insights for refining data-driven decision-making processes during health crises and improving the quality of information available to policymakers.