An Expectation-Based Network Scan Statistic for a COVID-19 Early Warning System
This work provides an early-warning system for the Greater London Authority and Transport for London to understand population adherence to COVID-19 guidelines, which is an incremental improvement for public health monitoring.
This paper introduces an expectation-based network scan statistic to help the Greater London Authority and Transport for London monitor population adherence to COVID-19 guidelines by analyzing mobility, transportation, and traffic data. It extends the Network Based Scan Statistic (NBSS) with an expectation-based approach and stochastic processes for time-series forecasting, allowing for quantification of metric uncertainty.
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.