A Multi-Aspect Agent-Based Model of COVID-19: Disease Dynamics, Contact Tracing Interventions and Shared Space-Driven Contagions

In the quest to better understand the transmission dynamics of COVID-19 and the strategies to control its impact a wide range of simulation models have been developed. Faced with a novel disease with little-known characteristics and unprecedented impacts, the need arises to model multiple aspects with very dissimilar dynamics in a consistent and formal, but also flexible and quick way to study the combined interaction of these aspects. We present an agent-based model combining kinematic movement of agents, interaction between them and their surrounding space, and centralized control over the entire population. To achieve this, we use and extend the retQSS framework to model and simulate particle systems that interact with geometries. We study different contact tracing strategies and their efficacy in reducing infections in a population going through an epidemic process driven mainly by indoor airborne contagion.