Group testing complexity seminar

This was an Iowa State University Department of Statistics seminar presented by Christopher R. Bilder from the University of Nebraska-Lincoln on December 2, 2024.

Title: Group testing complexity

Abstract: Laboratories use group testing (also known as pooled testing) to test high volumes of clinical specimens for pathogens, such as SARS-CoV-2, West Nile virus, and Chlamydia trachomatis. The process works by testing multiple specimens together as an amalgamation (i.e., as a “group”), rather than testing each specimen separately, in an effort to reduce the total number of tests needed. There are many different algorithmic ways to apply group testing. The job of a statistician is to determine which algorithm will be best for a diagnostic testing laboratory to implement given the information available, such as disease prevalence. Algorithms are most often compared by their expected number of tests needed for an application, where a lower value is preferred. Unfortunately, this measure alone does not account for some algorithms having a lower expected number of tests at the expense of being much more complex to implement. For this reason, I propose a new comparison measure that I refer to as the complexity. In my presentation, I present its definition and derive its expression for several common algorithms. I show that some algorithms may be too complex for everyday implementation, while other algorithms should become more widely used. The proposed measure is illustrated for a recent SARS-CoV-2 testing implementation.

This research is supported by Grant R01 AI121351 from the National Institutes of Health.