A1148
Title: Non-hierarchical group testing algorithms and group viral loads
Authors: Pranta Das - University of Nebraska-Lincoln (United States) [presenting]
Christopher Bilder - University of Nebraska-Lincoln (United States)
Joshua Tebbs - University of South Carolina (United States)
Christopher McMahan - Clemson University (United States)
Abstract: Group testing (also known as pooled testing) is a powerful method used to improve testing efficiency when laboratories screen specimens for infectious diseases. With this method, instead of testing specimens individually, specimens from multiple individuals are combined and tested as a group. These group test results alone or with subsequent retests allow one to determine which individuals are positive or negative for a disease. The COVID-19 pandemic especially highlighted the importance of group testing, with laboratories adopting it worldwide. Traditionally, group testing relies on observing binary responses only. However, during the pandemic, new non-hierarchical-based algorithms were developed that utilize group viral load data. These new algorithms apply lasso-based techniques to estimate the viral load of individual specimens and subsequently predict individual positive/negative outcomes. The purpose is to provide a comprehensive comparison of these new algorithms relative to standard ones. Both gains and losses are quantified by applying algorithms under fair comparison settings.
