International Trade Network: Country centrality and COVID-19 pandemic
Roberto Antonietti, Paolo Falbo, Fulvio Fontini, Rosanna Grassi, Giorgio Rizzini
TL;DR
This study examines whether country centrality within the World Trade Network helps explain COVID-19 diffusion and mortality during the first wave. It combines Estrada communicability-based community detection with multiple centrality metrics and tests their predictive power using a pooled negative binomial regression, controlling for GDP per capita, population, age structure, health capacity, and climate. The results show that higher centrality increases both infections and deaths, with local clustering and mesoscale community measures providing the strongest associations, and that the trade network's mesoscale structure remained resilient during the pandemic. The findings imply that trade-linked network topology can inform public health policy and pandemic preparedness by identifying high-risk country-positionings in the global economy.
Abstract
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.