Covid-19 Superspreading Events Network Analysis from Agent-Based Model with Mobility Restriction

COVID-19 pandemic is an immediate major public health concern. The search for the understanding of the disease spreading made science around the world turn to epidemiological studies, especially compartmental models. However, an interesting approach in epidemiological modeling nowadays is to use network models, which allow us to consider a heterogeneous population and to evaluate the role of superspreaders in this population. In this work, we implemented an agent-based model using probabilistic cellular automata to simulate SIR (Susceptible-Infected-Recovered) dynamics and COVID-19 infection parameters. The agents execute a random walk along the sites and have a probability to get the infection when share the same site of an infected one. To evaluate the spreading, we built the transmission network and measured the degree distribution, betweenness and closeness centrality. The results displayed for different levels of mobility restriction show that the degree reduces as the mobility reduces, but there is an increase of betweenness and closeness for some network nodes. Among other measures, as testing and tracing contacts, this study can bring important insights for the analysis of the disease dynamics and the role of superspreading events, contributing to the understanding of how to manage the mobility during a highly infectious pandemic as COVID-19.