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)
Other Supplementary Material for this manuscript includes the following:
- Data file S1. 96-sample pooling template.