## 6 Discussion and Concluding Remarks  
The four-step-based correlation and feature selection analysis conducted in this study differs from the existing literature contributions for the following main issues simultaneously supported by a quantitative approach:  
* Dynamic database collecting categorical and continuous attributes data from multiple sources of different typologies (population demographics, geography, health, government, community mobility, traffic, patients on hospitals, COVID infections);
* Time-based database that supports comparative analyses on different periods and waves of the COVID-19 pandemic;
* Focus on a selection of homogeneous and comparable countries in order to support comparative analyses;
* Correlation-based analysis and feature ranking analyses;
* Database availability for further research. This repository could also host new attributes coming from other additional sources, e.g., related to climate indicators or vaccines distributions;
* Adoption of an open-source data mining and machine learning toolbox;
* The result of the analysis confirms an essential role of the travel restriction and social distancing among the most adopted measures of governments to mitigate the effects of the pandemic;
* Findings in this study could assist the governmental policymaking in the near future thanks to a comparative approach that involves a wide period of observation and multiple homogeneous countries;
* The focus on non-pharmaceutical measures during periods in absence of a mass spread of vaccines makes these analyses useful to support the decision-making process in future pandemics when vaccines are still not available. The sum of the ranking positions for each feature and each country given the ranking analyses illustrated in Tables 3 and 4 on the whole set of data (see the so called \"ALL\" type analysis) are reported in Appendix D. For example, given the school closing feature, the global score (see the \"global ranking score\" in Appendix D) is 25, which is the sum of rank 3 for Italy, rank 4 for France, rank 16 for Germany, and rank 1 for the UK and USA. This is an additional list of summary results coming from the feature ranking analysis:  
* When the target endpoint is the new confirmed cases, school closing (1), workplaces closing (2), workplaces (3), parks (4), and residential (5) are the most significant attributes for the selected response. This group of features changes passing to the new deaths endpoint: facial coverings (1), driving (2), stay at home requirements (3), residential (4), and workplaces closing (5). Residential and workplaces closing are two most significant attributes for both endpoints. * School closing is part of the selection of most five significant attributes in three of the five countries for the new confirmed endpoint. * Facial coverings is part of the selection of most five significant attributes in four of the five countries for the new confirmed endpoint. * Given the target new confirmed cases and the set of five most significant attributes, two are Oxford government responses (school closing and workplaces closing) and three are google mobility features (workplaces, parks, and residential). * Given the target new deaths and the set of five most significant attributes, two are Oxford government responses (facial coverings and stay at home requirements), one is an Apple mobility feature (driving) and two are Google mobility features (residential and workplaces closing). This study's time-based approach can facilitate new quantitative analyses of available and historical datasets capturing daily new records. It is imperative to conduct criticality analyses and select relevant features that consider the combined effects of various decisions, such as different government strategies, on specific endpoints. Future research should aim to explore the effectiveness of prediction models for forecasting, operating on the extensive set of categorical and continuous attributes, features, and endpoints.