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

TL;DR

This paper evaluates Italy's COVID-19 regional risk model, analyzing variable effectiveness and proposing refined predictive models to improve decision-making during health crises.

Contribution

It introduces a statistical framework combining data reduction and regression to assess and enhance Italy's COVID-19 regional classification model.

Findings

Significant redundancy found in the original variables

Refined models improve predictive accuracy and interpretability

Recommendations for better data-driven policy support

Abstract

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.