Sample pooling approaches simulated under resource scarcity, lapses in testing capacity, and rapid processing demands for surveillance testing: a data-driven performance comparison

Sample pooling is a critical strategy to meet increased testing demand and conserve resources in surveillance testing. Much of its effectiveness depends on how well optimized the pool size is to the prevalence of infection in the sampled population, which can be difficult to anticipate in many circumstances. Multiple methods exist to better optimize pooling, with unique trade-offs. Pooling optimization methods were simulated to examine trade-offs between surveillance priorities and operational characteristics using SARS-CoV-2 surveillance data and workflows generated by the Virginia Tech Molecular Diagnostics Laboratory under varying capacity conditions. All in-house validation procedures were designed and established exclusively under CLIA to ensure full control of the analytical framework and to accurately reflect true capacity constraints. We used binary surveillance data to run Monte Carlo simulations (MCS) comparing conservative and large fixed pools, historical prevalence optimization (HPO), prevalence estimation testing (PET), truly optimized pooling, and individual testing. Median test counts from the MCS fed a discrete-event simulation (DES) that assessed processing time at different lab capacities under surveillance and outbreak conditions. We then used the combined performance results to build a classification tree to guide method selection under different testing priorities and constraints. MCS results indicated that small pools (4 samples), HPO, and PET resulted in test counts that were not statistically different from truly optimized pooling ( p > 0.05). The DES showed that pooling methods generally performed comparably to individual testing in processing time at low laboratory capacity, but individual testing became faster as capacity increased. Across capacity conditions, individual testing processed fewer than 500 daily samples more quickly, yet it demanded more hands-on time than pooling. Large-scale surveillance favored pooled methods, which were quicker under most conditions, while outbreak scenarios often favored individual testing when capacity wasn’t highly limited. Machine learning analysis highlighted surveillance priorities and sample intake as key determinants in selecting the best pooling optimization method for the given circumstance. This study demonstrates the importance of maintaining multiple pooling optimization approaches and adapting strategies to match evolving demands and potential constraints in surveillance testing. Not applicable.