  During the COVID-19 pandemic, governments have tried to keep their
territories safe by isolating themselves from others, limiting non-essential
travel and imposing mandatory quarantines for travelers. While large-scale
quarantine has been the most successful short-term policy, it is unsustainable
over long periods as it exerts enormous costs on societies. As a result,
governments which have been able to partially control the spread of the disease
have been deciding to reopen businesses. However, the WHO has warned about the
risks of re-opening prematurely, as is playing out in some countries such as
Spain, France and various states in the US such as California, Florida,
Arizona, and Texas. Thus, it is urgent to consider a flexible policy that
limits transmission without requiring large scale and damaging quarantines.
Here, we have designed a multi-level quarantine process based on the mobility
patterns of individuals and the severity of COVID-19 contagion in the US. By
identifying the natural boundaries of social mobility, policymakers can impose
travel restrictions that are minimally disruptive to social and economic
activity. The dynamics of social fragmentation during the COVID-19 outbreak are
analyzed by applying the Louvain method with modularity optimization to weekly
mobility networks. In a multi-scale community detection process, using the
locations of confirmed cases, natural break points as well as high risk areas
for contagion are identified. At the smaller scales, for communities with a
higher number of confirmed cases, contact tracing and associated quarantine
policies is increasingly important and can be informed by the community
structure.
