Machine learning analysis of the effects of COVID-19 on migration patterns.

This study investigates the impact of the COVID-19 pandemic on European tourist mobility patterns from 2019 to 2021 by conceptualizing countries as monomers emitting radiation to model and analyze their patterns through the lens of socio-economics and machine learning. By incorporating perturbations into clustering, this work evaluates the stability of mobility flux clustering under variable conditions, offering insights into the dynamics of socio-economic corridors. The results highlight distinct shifts in tourist behavior, with bimodal clustering in 2019 reflecting heterogeneous mobility patterns, whereas unimodal distributions in 2020 and 2021 indicate increased global uniformity, driven by pandemic-induced restrictions and gradual recovery. The PCA and dendrograms of the perturbed clustering reveal that tourist preferences align with GDP, cultural, and linguistic similarities, explaining regional cohesion and fragility. This study demonstrates the fragility of emerging socio-economic corridors like the Red Octopus compared to the resilience of established ones like the Blue Banana. The findings emphasize the importance of targeted policy interventions, such as strengthening transport infrastructure and fostering small and medium-sized enterprises (SMEs), to mitigate disruptions and promote balanced regional development. By integrating perturbations into clustering, this research introduces a strong framework for assessing mobility patterns under realistic variability to enhance economic resilience and anticipate shifts in tourist behavior during global crises.