Efficient Detection Of Infected Individuals using Two Stage Testing

TL;DR

This paper introduces an adaptive two-stage group testing scheme for efficiently identifying infected individuals in large populations, analyzing different randomization methods to optimize test performance and robustness.

Contribution

It characterizes the efficiency of various two-stage group testing algorithms, optimizing parameters for different randomization schemes and analyzing their impact on performance and error robustness.

Findings

Optimal randomization schemes improve testing efficiency.

Performance varies significantly with different randomization methods.

The proposed scheme is robust to input parameter errors.

Abstract

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.