Fig 1.
Example 1: An example of 5 patterns with 5 pools each.
Every small table represents a pattern, and each color represents a pool in the pattern. (a) Pattern 1, (b) Pattern 2, (c) Pattern 3, (d) Pattern 4, and (e) Pattern 5.
Fig 2.
Example 2: An example of a pooling matrix generation process with n = 25.
Individual’s positions are fixed in every colored matrix. The numerical value represents the pool number while the color represents the pattern number. (a) Pattern 1, (b) Pattern 2, (c) Pattern 3, (d) Pattern 4, and (e) Pattern 5.
Table 1.
The model parameters.
Fig 3.
Multiplicity pool testing algorithm.
Fig 4.
Comparison of the test accuracy measures: Specificity and sensitivity, for individual testing and pool testing.
Fig 5.
Comparison of the test accuracy measures: Specificity and sensitivity, for individual testing and pool testing.
Fig 6.
ROC curve for several prevalence levels given Sp = 0.90 and Se = 0.90.
Fig 7.
ROC curve for several manufacturer testing specificity and sensitivity levels given p = 0.05.
Fig 8.
The improvement in the pool testing accuracy, measured by the AUC as a function in Se and Sp.
Fig 9.
AUC heat map for Sp = 0.90 and Se = 0.90.