Socially structured model for COVID-19 pandemic: design and evaluation of control measures

Abstract

Governments around the world are taking different measures to deal with the novel severe respiratory syndrome coronavirus 2. In this sense, realistic mathematical models are important tools to explore the effect of different control strategies. In this work, we developed an agent-based model for COVID-19 disease dynamics which incorporates a basic social structure to simulate different control strategies in mid-size cities. We evaluated the impact of combinations of social distancing measures, such as contact tracing/case isolation, school closures and partial lockdowns for workplaces on the evolution of hospital beds occupancy. The contact tracing/case isolation modeled in most cases cannot prevent hospital beds saturation by itself. Our results suggest that schools, without strong social distancing measures, may be an important driver of the epidemic. Household and workplace people distribution is also an important factor to consider when studying the impact of control measures.