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Social distancing and isolation have been introduced widely to counter theCOVID-19 pandemic. However, more moderate contact reduction policies becomedesirable owing to adverse social, psychological, and economic consequences ofa complete or near-complete lockdown. Adopting a social network approach, weevaluate the effectiveness of three targeted distancing strategies designed to'keep the curve flat' and aid compliance in a post-lockdown world. These arelimiting interaction to a few repeated contacts, seeking similarity acrosscontacts, and strengthening communities via triadic strategies. We simulatestochastic infection curves that incorporate core elements from infectionmodels, ideal-type social network models, and statistical relational eventmodels. We demonstrate that strategic reduction of contact can stronglyincrease the efficiency of social distancing measures, introducing thepossibility of allowing some social contact while keeping risks low. Thisapproach provides nuanced insights to policy makers for effective socialdistancing that can mitigate negative consequences of social isolation.

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