  One of the Greater London Authority's (GLA) response to the COVID-19 pandemic
brings together multiple large-scale and heterogeneous datasets capturing
mobility, transportation and traffic activity over the city of London to better
understand 'busyness' and enable targeted interventions and effective
policy-making. As part of Project Odysseus we describe an early-warning system
and introduce an expectation-based scan statistic for networks to help the GLA
and Transport for London, understand the extent to which populations are
following government COVID-19 guidelines. We explicitly treat the case of
geographically fixed time-series data located on a (road) network and primarily
focus on monitoring the dynamics across large regions of the capital.
Additionally, we also focus on the detection and reporting of significant
spatio-temporal regions. Our approach is extending the Network Based Scan
Statistic (NBSS) by making it expectation-based (EBP) and by using stochastic
processes for time-series forecasting, which enables us to quantify metric
uncertainty in both the EBP and NBSS frameworks. We introduce a variant of the
metric used in the EBP model which focuses on identifying space-time regions in
which activity is quieter than expected.
