## 1 Introduction and Literature Review
Such contributions also compare different models and algorithms used to measure the relevance of a specific attribute to a target response, such as a selected endpoint [19, 20]. Further details about the chosen algorithm for this study are discussed in Section 5. The correlation analysis and the feature selection are usually the first steps towards a prediction study. Some scientific papers have presented predictive studies using techniques such as time series smoothing, neural networks, and random forest [2, 8, 21, 22, 23, 24]. However, these studies were often conducted on specific countries and limited to the first wave of the COVID-19 disease. For instance, one study [24] forecasted epidemiological trends of the COVID-19 pandemic for 16 countries, including the USA, Brazil, India, Mexico, South Africa, and Italy. More recent studies have focused on subsequent waves of COVID-19 and the mass distribution of vaccines [4, 25, 26]. These studies have utilized time-series analyses, auto-regression techniques, Monte Carlo agent-based modeling [25], diffusion modeling [27], artificial neural network modeling [26], and the SIR compartmental model [28]. Additionally, there are explicit models that estimate the total number of deathsand the cumulative number of deaths due to the COVID-19 virus in the United States [29]. Furthermore, [PERSON]. [30] estimated the COVID-19 death toll, considering the time-dependent effects of pandemic restrictions and changes related to COVID-19 in various regions and cities in the United States. Along with the global pandemic spread, the role of vaccines in tackling epidemic waves is assessed in different countries. [PERSON]. [31] mined citizens' willingness or reluctance to be vaccinated in seven European countries. The dynamics of the pre- and post-vaccine waves in Rio de Janeiro city using techniques such as the Poincare plot, approximate entropy, second-order difference plot, and central tendency measures are illustrated in [32]. [PERSON] et al. [33] employed Fourier and similarity analyses to examine mortality patterns within the frequency domain. By using a composite pandemic severity index and hierarchical clustering and by subdividing the pandemic into fifteen phases, [PERSON]. [34] identified similar trajectories of pandemic severity among all German counties. Major interest is paid to citizens' behavioral and habits change during and after COVID-19 in different countries. [PERSON] et al. [35] investigated the discrepancy between infection prevention intentions and citizen behaviors using Bayesian probability revision. Perceived risk and psychological factors in response to the pandemic's waves are also correlated with the citizens' retail shopping abandonment in [36]. The shift to alternative transportation modes in urban and long-range mobility is explored in light of the spread of the pandemics and the perceived risk [37; 38; 39], but not enough investigated as an anti-epidemic lever of intervention. In addition to studies on individual behavior, research has also focused on the effectiveness of government-imposed restrictions in controlling the pandemic's spread. [PERSON] et al. [40] used the stringency index to evaluate the level of restriction policies in Korea, proposing a more country-specific measure, the Korea stringency index (KSI), to capture the nuanced impact of these policies. Similarly, [PERSON] et al. [41] utilized the Oxford stringency index (SI) across multiple countries to assess how government responses correlated with the severity of COVID-19 outcomes, further reinforcing the critical role of policy measures in pandemic control. These studies complement the investigation of behavioral changes by demonstrating the direct influence of government restrictions on population mobility and pandemic progression. To the authors' knowledge, no studies have already correlated the pre-vaccine evolution of COVID-19 across waves in different countries, with controlled and uncontrolled features belonging to a multitude of domains.