Estimating SARS-CoV-2 seroprevalence and epidemiological parameters with uncertainty from serological surveys

Daniel B Larremore; Bailey K Fosdick; Kate M Bubar; Sam Zhang; Stephen M Kissler; C. Jessica E. Metcalf; Caroline Buckee; Yonatan Grad

is the (which was not peer-reviewed) The copyright holder for this preprint Figure S2 : Uncertainty of overall seroprevalence estimates from convenience and formal sampling strategies. Uncertainty, represented by the width of 90% credible intervals, is presented as ± seroprevalence percentage points, based on a serological tests with (A,B) 93.8% sensitivity and 95.6% specificity, matching the claims of a Cellex test, (C,D) 97.2% sensitivity and 100% specificity, matching the claims of an Aytu IgG test, (E,F) 100% sensitivity and specificity, representing an ideal test. complementing the results for a test with 93% sensitivity and 97.5% specificity shown in the main text (Fig. 3) . (A,C,E) Curves show the decrease in average CI widths for 15% seroprevalence, illustrating the advantages of using uniform and MDI samples over convenience samples. (B,D,F) Heatmaps show average CI widths for various total sample counts and overall seroprevalence. Convenience samples derived from newborn blood spots or U.S. blood donors improve with additional sampling but retain baseline uncertainty due to demographics not covered by the convenience sample. For the estimation of overall seroprevalence, uniform sampling is marginally superior to this example of the model and demographic informed (MDI) sampling strategy, which was designed to optimize estimation of R eff . Each point or pixel is averaged over 250 stochastic draws from the specified seroprevalence with the indicated sensitivity and specificity.