Estimation Through Array-Based Group Tests

Abstract

Pooling individual samples for batch testing is a common procedure for reducing costs. The recent use of multidimensional array algorithms, due to the emergence of robotic pooling, is an innovative way of pooling. We show that the two-dimensional arraybased group tests can provide accurate estimates for the prevalence rate even for situations in which the traditional estimators, applied to one-dimensional arrays, are not valid. Hence, a computational script was developed to determine which prevalence rate estimate minimizes the sum of the squared deviations between the number of observed and expected rows and columns whose pooled sample had a positive test result.