  As countries look towards re-opening of economic activities amidst the
ongoing COVID-19 pandemic, ensuring public health has been challenging. While
contact tracing only aims to track past activities of infected users, one path
to safe reopening is to develop reliable spatiotemporal risk scores to indicate
the propensity of the disease. Existing works which aim to develop risk scores
either rely on compartmental model-based reproduction numbers (which assume
uniform population mixing) or develop coarse-grain spatial scores based on
reproduction number (R0) and macro-level density-based mobility statistics.
Instead, in this paper, we develop a Hawkes process-based technique to assign
relatively fine-grain spatial and temporal risk scores by leveraging
high-resolution mobility data based on cell-phone originated location signals.
While COVID-19 risk scores also depend on a number of factors specific to an
individual, including demography and existing medical conditions, the primary
mode of disease transmission is via physical proximity and contact. Therefore,
we focus on developing risk scores based on location density and mobility
behaviour. We demonstrate the efficacy of the developed risk scores via
simulation based on real-world mobility data. Our results show that fine-grain
spatiotemporal risk scores based on high-resolution mobility data can provide
useful insights and facilitate safe re-opening.
