Estimating the cumulative incidence of SARS-CoV-2 with imperfect serological tests: Exploiting cutoff-free approaches
Fig 4
Effect of varying the number of control and case sera used to calibrate the serological test.
(A) An example of the true distribution (solid lines) of the control (grey) and case (orange) sera, the data simulated from those distributions (histograms) and the inferred densities (dashed lines) used in the inference of the cumulative incidence. Here, 150 control and case sera have been simulated and the AUC-ROC value of the test is equal to 0.975. (B) Point estimates of cumulative incidence for various numbers of control and case sera used to calibrate the serological test and three AUC-ROC values. Each violin shows the distribution of the estimated cumulative incidence of 50 in silico serosurveys conducted with cohorts of 10, 000 virtual individuals. The red line shows the true cumulative incidence we assumed in the simulated serosurveys (8%). (C) Size of the 95% confidence intervals of the estimated cumulative incidences. (D) Statistical power versus the number of control and case sera used in the validation data for varying levels of test accuracy (AUC-ROC). Each point in the graph represents the result of 3, 000 in silico serosurveys. (E) The minimal number of virtual individuals necessary to obtain a statistical power of 0.9 over a range of number of control and case sera in the validation data.