  A wide range of approaches have been applied to manage the spread of global
pandemic events such as COVID-19, which have met with varying degrees of
success. Given the large-scale social and economic impact coupled with the
increasing time span of the pandemic, it is important to not only manage the
spread of the disease but also put extra efforts on measures that expedite
resumption of social and economic life. It is therefore important to identify
situations that carry high risk, and act early whenever such situations are
identified. While a large number of mobile applications have been developed,
they are aimed at obtaining information that can be used for contact tracing,
but not at estimating the risk of social situations. In this paper, we
introduce an infection risk score that provides an estimate of the infection
risk arising from human contacts. Using a real-world human contact dataset, we
show that the proposed risk score can provide a realistic estimate of the level
of risk in the population. We also describe how the proposed infection risk
score can be implemented on smartphones. Finally, we identify representative
use cases that can leverage the risk score to minimize infection propagation.
