Social network-based distancing strategies to flatten the COVID 19 curve in a post-lockdown world

Block, Per; Hoffman, Marion; Raabe, Isabel J.; Dowd, Jennifer Beam; Rahal, Charles; Kashyap, Ridhi; Mills, Melinda C.

The COVID-19 pandemic highlights the importance of effective non-pharmaceutical public health interventions. While social distancing and isolation has been introduced widely, more moderate contact reduction policies could become desirable owing to adverse social, psychological, and economic consequences of a complete or near-complete lockdown. Adopting a novel social network approach, we evaluate the effectiveness of three targeted distancing strategies designed to 'keep the curve flat' and aid compliance in a post-lockdown world. We simulate stochastic infection curves that incorporate core elements from infection models, ideal-type social network models, and statistical relational event models. Our models demonstrate that while social distancing measures clearly do flatten the curve, strategic reduction of contact can strongly increase their efficiency, introducing the possibility of allowing some social contact while keeping risks low. Limiting interaction to a few repeated contacts emerges as the most effective strategy. Maintaining similarity across contacts and the strengthening of communities via triadic strategies are also highly effective. This approach provides empirical evidence which adds nuanced policy advice for effective social distancing that can mitigate adverse consequences of social isolation.