  During a pandemic, contact tracing is an essential tool to drive down the
infection rate within a population. To accelerate the laborious manual contact
tracing process, digital contact tracing (DCT) tools can track contact events
transparently and privately by using the sensing and signaling capabilities of
the ubiquitous cell phone. However, an effective DCT must not only preserve
user privacy but also augment the existing manual contact tracing process.
Indeed, not every member of a population may own a cell phone or have a DCT app
installed and enabled. We present KHOVID to fulfill the combined goal of manual
contact-tracing interoperability and DCT user privacy. At KHOVID's core is a
privacy-friendly mechanism to encode user trajectories using geolocation data.
Manual contact tracing data can be integrated through the same geolocation
format. The accuracy of the geolocation data from DCT is improved using
Bluetooth proximity detection, and we propose a novel method to encode
Bluetooth ephemeral IDs. This contribution describes the detailed design of
KHOVID; presents a prototype implementation including an app and server
software; and presents a validation based on simulation and field experiments.
We also compare the strengths of KHOVID with other, earlier proposals of DCT.
