Modeling the resilience of social networks to lockdowns regarding the dynamics of meetings

Modern societies are facing more numerous and diverse hazards than ever before, and at an ever-increasing pace. One way to face such catastrophes and dampen their potential harm is to enhance the resilience of social systems. Achieving such a goal requires a better understanding of the mechanisms underlying social resilience. We tackle this issue by simulating dynamic social networks based on the Jin–Girvan–Newman model, and investigate how quickly such networks recover after a lockdown. We first find that the recovery time increases with the strictness of the lockdown, but quickly saturates. Next, we study how the recovery time depends on characteristics of the network. The recovery time is independent of the network size, decreases fast with the rate of random meetings (inverse dependence), and increases with the rate of meeting break-ups. The dependence of the recovery time on the rate of social meetings depends on the maximum number of meetings occurring per time step. Our results suggest that more open, trustful and cohesive communities are more resilient.