  The rapid global spread of COVID-19 has led to an unprecedented demand for
effective methods to mitigate the spread of the disease, and various digital
contact tracing (DCT) methods have emerged as a component of the solution. In
order to make informed public health choices, there is a need for tools which
allow evaluation and comparison of DCT methods. We introduce an agent-based
compartmental simulator we call COVI-AgentSim, integrating detailed
consideration of virology, disease progression, social contact networks, and
mobility patterns, based on parameters derived from empirical research. We
verify by comparing to real data that COVI-AgentSim is able to reproduce
realistic COVID-19 spread dynamics, and perform a sensitivity analysis to
verify that the relative performance of contact tracing methods are consistent
across a range of settings. We use COVI-AgentSim to perform cost-benefit
analyses comparing no DCT to: 1) standard binary contact tracing (BCT) that
assigns binary recommendations based on binary test results; and 2) a
rule-based method for feature-based contact tracing (FCT) that assigns a graded
level of recommendation based on diverse individual features. We find all DCT
methods consistently reduce the spread of the disease, and that the advantage
of FCT over BCT is maintained over a wide range of adoption rates.
Feature-based methods of contact tracing avert more disability-adjusted life
years (DALYs) per socioeconomic cost (measured by productive hours lost). Our
results suggest any DCT method can help save lives, support re-opening of
economies, and prevent second-wave outbreaks, and that FCT methods are a
promising direction for enriching BCT using self-reported symptoms, yielding
earlier warning signals and a significantly reduced spread of the virus per
socioeconomic cost.
