Neural Network Models for Group Testing Data

Thomas M. Menino Convention & Exhibition Center 

Group testing is a cost-effective strategy for screening large populations for infectious diseases. Instead of testing individually, biospecimens (e.g., blood, urine, swabs) are pooled and tested together to reduce overall testing costs. In infectious disease screening programs using group testing, it is often desirable to relate individual-level covariates (e.g., age, gender, symptoms) to the underlying infection status. However, this task is challenging because individual infection statuses are unobserved; i.e., they are masked by imperfect testing and potentially the pooling protocol. While regression models that address these issues have been developed, existing approaches generally lack the ability to automatically detect and account for nonlinear associations and higher-order interactions between the covariates and the infection status. To address these limitations, we propose a neural network framework for group testing data that automatically detects and accounts for nonlinear relationships and high-order interactions. We illustrate the practical utility of our proposed approach by using it to analyze Chlamydia group testing data collected by the Iowa Public Health Lab.

group testing


neural network

imbalanced data

diagnostic testing

EM algorithm 

Section on Statistical Learning and Data Science