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The increasing complexity of interrelated systems has made the use ofmultiplex networks an important tool for explaining the nature of relationsbetween elements in the system. In this paper, we aim at investigating variousaspects of countries' behaviour during the coronavirus pandemic period. Bymeans of a multiplex network we consider simultaneously stringency indexvalues, COVID-19 infections and international trade data, in order to detectclusters of countries that showed a similar reaction to the pandemic. Wepropose a new methodological approach based on the Estrada communicability foridentifying communities on a multiplex network, based on a two-stepoptimization. At first, we determine the optimal inter-layer intensity betweenlevels by minimizing a distance function. Hence, the optimal inter-layerintensity is used to detect communities on each layer. Our findings show thatthe community detection on this multiplex network has greater information powerthan classical methods for single-layer networks. Our approach better revealsclusters on each layer with respect to the application of the same approach oneach single-layer. Moreover, detected groups in the multiplex case benefit of ahigher cohesion, leading to identifying on each layer a lower number ofcommunities with respect to the ones obtained in the single-layer cases.

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