Optimal pooling strategies for respiratory virus testing: A comparative cost-effectiveness analysis

Pooled testing represents a cost-efficient strategy for large-scale respiratory virus screening. However, determining the optimal pool size (OPS) across varying prevalence rates and diagnostic performance metrics remains a critical challenge in respiratory virus surveillance. We evaluated four hierarchical OPS algorithms, all employing the original solution method (OSM), and proposed a modified solution method (MSM) based on objective function optimization. Through Monte Carlo simulations and logit modeling, we generated COVID-19 infection data representative of community transmission patterns. These data were analyzed using a comparative cost-effectiveness framework to assess OSM and MSM approaches. Our analysis across various prevalence rates (0.1-30.0%), sensitivities (0.8-1.0), and specificities (0.97-1.00) revealed Hanel et al.'s algorithm consistently yielded the largest OPS values under OSM. MSM revealed minimal deviations from OSM in most scenarios, though it effectively corrected Kim et al.'s inflated OPS values at high prevalence (~30%) with low sensitivity/specificity. Three algorithms produced comparable OPS configurations, outperforming OSM. Hanel's and Regen's algorithms emerged as the most cost-effective options, with Hanel's method being optimal for low additional costs in second-stage testing and Regen's for high additional costs. MSM significantly reduced inter-algorithm cost differences compared to OSM. This study provides a comprehensive evaluation of OPS determination algorithms in pooled PCR testing for respiratory viruses, demonstrating robust OPS configurations and enhanced cost-effectiveness through MSM implementation. The proposed MSM addresses existing limitations in pooled testing strategies, facilitates efficient resource allocation, and contributes to improved respiratory virus surveillance and pandemic response.