An Expectation-Based Network Scan Statistic for a COVID-19 Early Warning System

• URL: http://arxiv.org/abs/2012.07574v1
• Date: Tue, 8 Dec 2020 19:35:17 GMT
• Title: An Expectation-Based Network Scan Statistic for a COVID-19 Early Warning System
• Authors: Chance Haycock, Edward Thorpe-Woods, James Walsh, Patrick O'Hara, Oscar Giles, Neil Dhir, Theodoros Damoulas
• Abstract summary: One of the Greater London Authority's (GLA) response to the COVID-19 pandemic brings together multiple large-scale and heterogeneous datasets. We describe an early-warning system and introduce an expectation-based scan statistic for networks to help the GLA and Transport for London.
• Score: 8.634409966628322
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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