  Accurate detection of infected individuals is one of the critical steps in
stopping any pandemic. When the underlying infection rate of the disease is
low, testing people in groups, instead of testing each individual in the
population, can be more efficient. In this work, we consider noisy adaptive
group testing design with specific test sensitivity and specificity that select
the optimal group given previous test results based on pre-selected utility
function. As in prior studies on group testing, we model this problem as a
sequential Bayesian Optimal Experimental Design (BOED) to adaptively design the
groups for each test. We analyze the required number of group tests when using
the updated posterior on the infection status and the corresponding Mutual
Information (MI) as our utility function for selecting new groups. More
importantly, we study how the potential bias on the ground-truth noise of group
tests may affect the group testing sample complexity.
