Prediction‐driven pooled testing methods: Application to HIV treatment monitoring in Rakai, Uganda

Abstract

Chronic medical conditions often necessitate regular testing for proper treatment. Regular testing of all afflicted individuals may not be feasible due to limited resources, as is true with HIV monitoring in resource-limited settings. Pooled testing methods have been developed in order to allow regular testing for all while reducing resource burden. However, the most commonly used methods do not make use of covariate information predictive of treatment failure, which could improve performance. We propose and evaluate four prediction-driven pooled testing methods that incorporate covariate information to improve pooled testing performance. We then compare these methods in the HIV treatment management setting to current methods with respect to testing efficiency, sensitivity, and number of testing rounds using simulated data and data collected in Rakai, Uganda. Results show that the prediction-driven methods increase efficiency by up to 20% compared with current methods while maintaining equivalent sensitivity and reducing number of testing rounds by up to 70%. When predictions were incorrect, the performance of prediction-based matrix methods remained robust. The best performing method using our motivating data from Rakai was a prediction-driven hybrid method, maintaining sensitivity over 96% and efficiency over 75% in likely scenarios. If these methods perform similarly in the field, they may contribute to improving mortality and reducing transmission in resource-limited settings. Although we evaluate our proposed pooling methods in the HIV treatment setting, they can be applied to any setting that necessitates testing of a quantitative biomarker for a threshold-based decision. K E Y W O R D Smatrix-based testing, mini pool, pooled testing, prediction-based testing, treatment failure INTRODUCTIONProper management of treatable, chronic medical conditions often relies on measurement of one or more biomarkers to assess the effectiveness of current treatment and disease progression. Based on regular blood testing, a patient withThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Dorfman pooling enhances SARS-CoV-2 large-scale community testing efficiency

“…Subsequently, pooled testing has been used successfully to screen large populations for infectious diseases. Pooled testing has been utilized in the laboratory testing of HIV, tuberculosis, hepatitis B and C, as well as the screening of blood donations to the American Red Cross [7][8][9]. Its utility has been demonstrated as a way to analyze large numbers of samples with greater efficiency than individual testing allows.…”