  COVID-19 is highly transmissible and containing outbreaks requires a rapid
and effective response. Because infection may be spread by people who are
pre-symptomatic or asymptomatic, substantial undetected transmission is likely
to occur before clinical cases are diagnosed. Thus, when outbreaks occur there
is a need to anticipate which populations and locations are at heightened risk
of exposure. In this work, we evaluate the utility of aggregate human mobility
data for estimating the geographic distribution of transmission risk. We
present a simple procedure for producing spatial transmission risk assessments
from near-real-time population mobility data. We validate our estimates against
three well-documented COVID-19 outbreak scenarios in Australia. Two of these
were well-defined transmission clusters and one was a community transmission
scenario. Our results indicate that mobility data can be a good predictor of
geographic patterns of exposure risk from transmission centres, particularly in
scenarios involving workplaces or other environments associated with habitual
travel patterns. For community transmission scenarios, our results demonstrate
that mobility data adds the most value to risk predictions when case counts are
low and spatially clustered. Our method could assist health systems in the
allocation of testing resources, and potentially guide the implementation of
geographically-targeted restrictions on movement and social interaction.
