| Résumé: | This article expands the results of previous research exploring the role of social structure in the volume of infections, in both space and time. The study uses bi-weekly, municipal information over a twelve-month period in Mexico. The analysis is conducted in two phases, the first of which is based on a cross sectional hierarchical model. The second uses Bayesian methods to model the geographical proximity of municipalities to explain the spread of Covid-19. The results suggest that the spatial distribution of Covid-19 infections can be explained by the population’s geographical distribution across municipalities, social interactions, and poverty and inequality levels, rather than the intensity of the pandemic.
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