  The COVID-19 pandemic has emerged as a global public health crisis. To make
decisions about mitigation strategies and to understand the disease dynamics,
policy makers and epidemiologists must know how the disease is spreading in
their communities. We analyze confirmed infections and deaths over multiple
geographic scales to show that COVID-19's impact is highly unequal: many
subregions have nearly zero infections, and others are hot spots. We attribute
the effect to a Reed-Hughes-like mechanism in which disease arrives at
different times and grows exponentially. Hot spots, however, appear to grow
faster than neighboring subregions and dominate spatially aggregated
statistics, thereby amplifying growth rates. The staggered spread of COVID-19
can also make aggregated growth rates appear higher even when subregions grow
at the same rate. Public policy, economic analysis and epidemic modeling need
to account for potential distortions introduced by spatial aggregation.
