Social Network Metric-Based Interventions? Experiments with an Agent-Based Model of the COVID-19 Pandemic in a Metropolitan Region

We present and use an agent-based model to study interventions for suppression, mitigation, and vaccination in coping with the COVID-19 pandemic. Unlike metapopulation models, our agent-based model permits experimenting with micro-level interventions in social interactions at individual sites. We compare common macro-level interventions applicable to everyone (e.g., keep distance, close all schools) to targeted interventions in the social network spanned by households based on specific (potential) transmission rates (e.g., prohibit visiting spreading hubs or bridging ties). We show that, in the simulation environment, micro-level measures of 'locking' of a number of households and 'blocking' access to a number of sites (e.g., workplaces, schools, recreation areas) using social network centrality metrics permits refined control on the positioning on the immunity-mortality curve. In simulation results, social network metric-based vaccination of households offers refined control and reduces the spread saliently better than random vaccination.