Algorithmic Problems in Contact Tracing

Algorithmic Problems in Contact Tracing (via Zoom)

Abstract: Contact tracing is a key tool for managing epidemic diseases like HIV, tuberculosis, and COVID-19. Manual investigations by human contact tracers remain a dominant way in which this is carried out. This process is limited by the number of contact tracers available, who are often overburdened during an outbreak or epidemic. As a result, a crucial decision in any contact tracing strategy is, given a set of contacts, which person should a tracer trace next? In this talk, we develop a formal model that articulates these questions and provides a framework for comparing contact tracing strategies. Through analyzing our model, we give provably optimal prioritization policies via a clean connection to a tool from operations research called a “branching bandit”. Examining these policies gives qualitative insight into trade-offs in contact tracing applications.

This talk is based on joint work with Jon Kleinberg.

Bio: Michela Meister is a third-year Ph.D. student in computer science at Cornell University, where she is advised by Jon Kleinberg. She is interested broadly in algorithms and machine learning and more specifically in how human experts with limited information can make better decisions under uncertainty.