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A central feature of an emerging infectious disease in a pandemic scenario isthe spread through geographical scales and the impacts on different locationsaccording to the adopted mitigation protocols. We investigated a stochasticepidemic model with the metapopulation approach in which patches representmunicipalities. Contagion follows a stochastic compartmental model formunicipalities; the latter, in turn, interact with each other through recurrentmobility. As a case of study, we consider the epidemic of COVID-19 in Brazilperforming data-driven simulations. Properties of the simulated epidemic curveshave very broad distributions across different geographical locations andscales, from states, passing through intermediate and immediate regions down tomunicipality levels. Correlations between delay of the epidemic outbreak anddistance from the respective capital cities were predicted to be strong inseveral states and weak in others, signaling influences of multiple epidemicfoci propagating towards the inland cities. Responses of different regions to asame mitigation protocol can vary enormously implying that the policies ofcombating the epidemics must be engineered according to the region' specificitybut integrated with the overall situation. Real series of reported casesconfirm the qualitative scenarios predicted in simulations. Even though werestricted our study to Brazil, the prospects and model can be extended toother geographical organizations with heterogeneous demographic distributions.

Outbreak diversity in epidemic waves propagating through distinct geographical scales