A methodology for deriving the sensitivity of pooled testing, based on viral load progression and pooling dilution

Estimating test sensitivity for pooled specimens remains a challenge in diagnostic and surveillance planning. While pooled testing is widely used to increase testing throughput, current data on sensitivity values are sparse and lack functional relationships with pool size. Prior research has shown that sensitivity decreases with increasing pool size due to dilution effects. However, no prior work had resolved how to compute this relationship using viral load progression and dilution. This gap motivated the development of a mathematical framework that integrates biological and statistical principles. Existing methods rely on empirical data, which are limited in scope and specificity. This paper introduces a novel approach that combines mathematical modeling with clinical data. Understanding how pool size affects sensitivity is essential for optimizing testing strategies. This study addresses the need for a generalizable model that can be applied to various assays and pathogens.

This study aimed to create a methodology for calculating pooled test sensitivity that accounts for both viral load progression and pooling dilution. The goal was to provide a reliable framework for optimizing testing pool design in surveillance and screening programs. The researchers focused on HIV nucleic acid amplification testing (NAT) as a case study. They sought to derive sensitivity values for different pool sizes using a combination of mathematical modeling and empirical data. The motivation was to improve the accuracy of prevalence estimation in resource-limited settings. By integrating viral load progression models with pooling dilution effects, the study aimed to produce a computationally efficient and generalizable approach. The proposed method allows for sensitivity estimation without requiring extensive clinical data for each pool size. This approach supports more efficient and cost-effective testing strategies in public health.

The methodology combines mathematical modeling with clinical and assay-specific data. It uses a model of viral load progression in HIV-infected individuals to estimate RNA concentrations over time. Pooling dilution is modeled as a function of pool size and the number of infected specimens. The conditional sensitivity is calculated based on the number of infected specimens in a pool. The law of total probability is used to integrate over all possible combinations of infected and uninfected specimens. Higher-dimensional integrals are applied to derive pooled test sensitivity values. Model parameters are calibrated using published efficacy data for the HIV ULTRIO Plus NAT Assay. The researchers also use clinical data on RNA load progression to refine the model. An approximation function is developed to simplify sensitivity calculations for practical use.

The methodology successfully derives pooled test sensitivity values for various pool sizes. For the HIV ULTRIO Plus NAT Assay, the model estimates sensitivity with high accuracy. The approximation function closely matches the full mathematical model results. The model is validated using clinical data on viral RNA load progression in HIV patients. The optimal testing pool design for HIV prevalence estimation in Sub-Saharan Africa is derived using this methodology. The proposed pool design outperforms a benchmark design in terms of efficiency. Sensitivity decreases predictably with increasing pool size due to dilution effects. The model is computationally tractable and can be adapted to other infections and assays.

The proposed methodology provides an accurate and generalizable approach to computing pooled test sensitivity. It integrates viral load progression and pooling dilution into a unified framework. The model is validated using clinical and assay-specific data for HIV NAT. The approximation function simplifies sensitivity calculations without sacrificing accuracy. The optimal pool design for HIV prevalence estimation demonstrates the practical value of this approach. The methodology is computationally efficient and can be adapted to other infections. The study shows that sensitivity decreases with pool size due to dilution effects. The model supports improved testing strategies for surveillance and screening in public health.