During the ongoing COVID-19 pandemic, there have been burgeoning efforts to develop and deploy smartphone apps to expedite contact tracing and risk notification. Unfortunately, the success of these apps has been limited, partly owing to poor interoperability with manual contact tracing, low adoption rates, and a societally sensitive trade-off between utility and privacy. In this work, we introduce a new privacy-preserving and inclusive system for epidemic risk assessment and notification that aims to address the above limitations. Rather than capturing pairwise encounters between smartphones as done by existing apps, our system captures encounters between inexpensive, zero-maintenance, small devices carried by users, and beacons placed in strategic locations where infection clusters are most likely to originate. Epidemiological simulations using an agent-based model demonstrate several beneficial properties of our system. By achieving bidirectional interoperability with manual contact tracing, our system can help control disease spread already at low adoption. By utilizing the location and environmental information provided by the beacons, our system can provide significantly higher sensitivity and specificity than existing app-based systems. In addition, our simulations also suggest that it is sufficient to deploy beacons in a small fraction of strategic locations for our system to achieve high utility.