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

Area of Science:

• * Epidemiology and Public Health
• * Infectious Disease Diagnostics
• * Biostatistics and Health Economics

Background:

• * Pooled testing is a cost-effective method for large-scale respiratory virus screening.
• * Optimal pool size (OPS) determination is challenging due to variable prevalence and test performance.
• * Existing methods for OPS calculation may not be universally optimal across all scenarios.

Purpose of the Study:

• * To evaluate hierarchical optimal pool size (OPS) algorithms for pooled respiratory virus testing.
• * To compare the original solution method (OSM) with a proposed modified solution method (MSM) for OPS determination.
• * To assess the cost-effectiveness of different OPS algorithms under varying epidemiological conditions.

Main Methods:

• * Monte Carlo simulations and logit modeling were used to generate COVID-19 infection data.
• * Four hierarchical OPS algorithms were evaluated using both OSM and MSM.
• * A comparative cost-effectiveness framework analyzed algorithm performance across diverse prevalence, sensitivity, and specificity values.

Main Results:

• * Hanel et al.'s algorithm consistently produced the largest OPS under OSM.
• * MSM showed minimal deviation from OSM but corrected inflated OPS values at high prevalence with low test sensitivity/specificity.
• * Three algorithms demonstrated comparable OPS configurations, outperforming OSM; Hanel's and Regen's algorithms were most cost-effective depending on second-stage testing costs.

Conclusions:

• * The modified solution method (MSM) enhances the cost-effectiveness of pooled testing strategies.
• * MSM effectively addresses limitations of existing OPS algorithms, particularly in challenging scenarios.
• * This study provides robust OPS configurations and supports efficient resource allocation for improved respiratory virus surveillance and pandemic response.