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?:abstract
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As the coronavirus disease 2019 (COVID-19) spreads worldwide, epidemiological models have been employed to evaluate possible scenarios and gauge the efficacy of proposed interventions. Considering the complexity of disease transmission dynamics in cities, stochastic epidemic models include uncertainty in their treatment of the problem, allowing the estimation of the probability of an outbreak, the distribution of epidemic magnitudes, and their expected duration. In this sense, we propose a kinetic Monte Carlo epidemic model that focuses on demography and on age-structured mobility data to simulate the evolution of the COVID-19 outbreak in the capital of Brazil, Brasilia, under several scenarios of mobility restriction. We show that the distribution of epidemic outcomes can be divided into short-lived mild outbreaks and longer severe ones. We demonstrate that quarantines have the effect of reducing the probability of a severe outbreak taking place but are unable to mitigate the magnitude of these outbreaks once they happen. Finally, we present the probability of a particular trajectory in the epidemic progression resulting in a massive outbreak as a function of the cumulative number of cases at the end of each quarantine period, allowing for the estimation of the risk associated with relaxing mobility restrictions at a given time.
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