Efficient Detection Of Infected Individuals using Two Stage Testing
This work addresses the challenge of large-scale testing for disease detection, but it appears incremental as it builds on existing group testing methods with a focus on parameter optimization.
The paper tackles the problem of efficiently detecting infected individuals through group testing by analyzing adaptive two-stage schemes and determining optimal test parameters for different randomization types, showing that the expected number of tests varies with randomization and the scheme is robust to parameter errors.
Group testing is an efficient method for testing a large population to detect infected individuals. In this paper, we consider an efficient adaptive two stage group testing scheme. Using a straightforward analysis, we characterize the efficiency of several two stage group testing algorithms. We determine how to pick the parameters of the tests optimally for three schemes with different types of randomization, and show that the performance of two stage testing depends on the type of randomization employed. Seemingly similar randomization procedures lead to different expected number of tests to detect all infected individuals, we determine what kinds of randomization are necessary to achieve optimal performance. We further show that in the optimal setting, our testing scheme is robust to errors in the input parameters.