  Recent re-opening policies in the US, following a period of social distancing
measures, introduced a significant increase in daily COVID-19 infections,
calling for a roll-back or substantial revisiting of these policies in many
states. The situation is suggestive of difficulties modeling the impact of
partial distancing/re-opening policies on future epidemic spread for purposes
of choosing safe alternatives. More specifically, one needs to understand the
impact of manipulating the availability of social interaction venues (e.g.,
schools, workplaces, and retail establishments) on virus spread. We introduce a
model, inspired by social networks research, that answers the above question.
Our model compartmentalizes interaction venues into categories we call mixing
domains, enabling one to predict COVID-19 contagion trends in different
geographic regions under different what if assumptions on partial re-opening of
individual domains. We apply our model to several highly impacted states
showing (i) how accurately it predicts the extent of current resurgence (from
available policy descriptions), and (ii) what alternatives might be more
effective at mitigating the second wave. We further compare policies that rely
on partial venue closure to policies that espouse wide-spread periodic testing
instead (i.e., in lieu of social distancing). Our models predict that the
benefits of (mandatory) testing out-shadow the benefits of partial venue
closure, suggesting that perhaps more efforts should be directed to such a
mitigation strategy.
