  International trade is based on a set of complex relationships between
different countries that can be modelled as an extremely dense network of
interconnected agents. On the one hand, this network might favour the economic
growth of countries, but on the other, it can also favour the diffusion of
diseases, like the COVID-19. In this paper, we study whether, and to what
extent, the topology of the trade network can explain the rate of COVID-19
diffusion and mortality across countries. We compute the countries' centrality
measures and we apply the community detection methodology based on
communicability distance. Then, we use these measures as focal regressors in a
negative binomial regression framework. In doing so, we also compare the effect
of different measures of centrality. Our results show that the number of
infections and fatalities are larger in countries with a higher centrality in
the global trade network.
