Supplementary Materials

The PDF file includes:

• Materials and methods
• Fig. S1. Population prevalence (left) or prevalence in sample (right) against maximum likelihood prevalence estimates.
• Fig. S2. Prevalence estimation can depend on training and application period.
• Fig. S3. Sensitivity of sample identification relative to dilution factor and time since peak viral load.
• Fig. S4. Simulated viral loads.
• Fig. S5. Group testing for sample identification during epidemic decline.
• Fig. S6. Effectiveness of optimal testing design under resource constraints at high prevalence.
• Fig. S7. Effectiveness of optimal testing design under resource constraints using sputum data.
• Fig. S8. Evaluation of pooled testing in a sustained, multi-wave epidemic.
• Fig. S9. Evaluation of pooled testing for sample identification in the multi-wave epidemic shown in fig. S8A.
• Fig. S10. Model fits to swab viral loads.
• Fig. S11. Posterior distributions of estimated parameters fitted to swab and sputum data.
• Fig. S12. Markov chain Monte Carlo trace plots from fitting to swab and sputum data.
• Fig. S13. qPCR calibration curve using standard viral RNA copies.
• Table S1. List of all group test designs for sample identification.
• Table S2. Cycle threshold values from qPCR on pooled samples with variable viral load.
• Table S3. Positive sample distribution within validation pools.
• Table S4. Pool design for combinatorial test with 96 samples.
• Table S5. Description of all parameters used in the viral kinetics and transmission models.
• Table S6. RT-qPCR results for pooling validations.
• References (43–45)

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Other Supplementary Material for this manuscript includes the following:

• Data file S1. 96-sample pooling template.