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Table 4 Selected outcomes from applying 1- and 2-test diagnostic strategy models to four HCV epidemic scenarios. The examples here have been extracted from Additional file 2

From: One or two serological assay testing strategy for diagnosis of HBV and HCV infection? The use of predictive modelling

Population: 10,000        
  Strategy Anti-HCV Test Kit True Positive False Positive False Negative PPV number of assay B tests
Scenario 1 Prevalence 40%: 1-test 2 3920 60 80 0.985  
2-test 2 → 5 3842 1 158 1.000 3980
1-test 3 3980 120 20 0.971  
2-test 3 → 4 3383 1 617 1.000 4100
Scenario 2 Prevalence 10%: 1-test 2 980 90 20 0.916  
2-test 2 → 5 960 2 40 0.998 1070
1-test 3 995 180 5 0.847  
2-test 3 → 4 846 2 154 0.998 1175
Scenario 3 Prevalence 2%: 1-test 2 196 98 4 0.667  
2-test 2 → 5 192 2 8 0.990 294
1-test 3 199 196 1 0.504  
2-test 3 → 4 169 2 31 0.989 395
Scenario 4 Prevalence 0.4% 1-test 2 39 100 1 0.282  
2-test 2 → 5 38 2 2 0.951 139
1-test 3 40 199 0 0.167  
2-test 3 → 4 34 2 6 0.944 239
Test Kit Performance Characteristics:
Test Kits Sensitivity Specificity   Test Kits Sensitivity Specificity  
2 98.0% 99.0%   4 85.0% 99.0%  
3 99.5% 98.0%   5 98.0% 98.0%  
Notes on Two-Test Strategies:
Assay B performance is considered independent of Assay A  
Outcome of 2-test strategy: A + B+ = pos and A-, A + B- = neg (Fig. 2)  
2-test strategies overall performance: Assay A Assay B Sensitivity Specificity
     2 5 96.04% 99.98%
     3 4 84.58% 99.98%