  The COVID-19 pandemic has proved to be one of the most disruptive public
health emergencies in recent memory. Among non-pharmaceutical interventions,
social distancing and lockdown measures are some of the most common tools
employed by governments around the world to combat the disease. While
mathematical models of COVID-19 are ubiquitous, few have leveraged network
theory in a general way to explain the mechanics of social distancing. In this
paper, we build on existing network models for heterogeneous, clustered
networks with random link activation/deletion dynamics to put forth realistic
mechanisms of social distancing using piecewise constant activation/deletion
rates. We find our models are capable of rich qualitative behavior, and offer
meaningful insight with relatively few intervention parameters. In particular,
we find that the severity of social distancing interventions and when they
begin have more impact than how long it takes for the interventions to take
full effect.
