Simulation of pooled-sample analysis strategies for COVID-19 mass testing.

ABSTRACT

OBJECTIVE: To evaluate two pooled-sample analysis strategies (a routine high-throughput approach and a novel context-sensitive approach) for mass testing during the coronavirus disease 2019 (COVID-19) pandemic, with an emphasis on the number of tests required to screen a population. METHODS: We used Monte Carlo simulations to compare the two testing strategies for different infection prevalences and pooled group sizes. With the routine high-throughput approach, heterogeneous sample pools are formed randomly for polymerase chain reaction (PCR) analysis. With the novel context-sensitive approach, PCR analysis is performed on pooled samples from homogeneous groups of similar people that have been purposively formed in the field. In both approaches, all samples contributing to pools that tested positive are subsequently analysed individually. FINDINGS: Both pooled-sample strategies would save substantial resources compared to individual analysis during surge testing and enhanced epidemic surveillance. The context-sensitive approach offers the greatest savings for instance, 58-89% fewer tests would be required for a pooled group size of 3 to 25 samples in a population of 150 000 with an infection prevalence of 1% or 5%. Correspondingly, the routine high-throughput strategy would require 24-80% fewer tests than individual testing. CONCLUSION: Pooled-sample PCR screening could save resources during COVID-19 mass testing. In particular, the novel context-sensitive approach, which uses pooled samples from homogeneous population groups, could substantially reduce the number of tests required to screen a population. Pooled-sample approaches could help countries sustain population screening over extended periods of time and thereby help contain foreseeable second-wave outbreaks.