The effect of the pandemic on complex socio-economic systems: community detection induced by communicability

In this paper, we aim at investigating various aspects of countries' behaviour during the coronavirus pandemic period. By means of a multiplex network, we simultaneously consider government responses based on COVID-19 infections, Stringency Index, international trade and international air mobility data, to detect clusters of countries that showed a similar reaction to the pandemic. We propose a new methodological approach based on the Estrada communicability for identifying communities on a multiplex network, based on a two-step optimization process. At first, we determine the optimal inter-layer weight between levels by minimizing a distance function. Hence, the optimal weight is used to detect the communities on each layer. Our findings show that this new approach to community detection on a multiplex network provides additional insights with respect to the same procedure performed on layers separately. Indeed, clusters in the multiplex network benefit from a higher cohesion, as they are detected taking into account the mutual influence of the other networks.