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is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.04.15.20067066 doi: medRxiv preprint blood donations achieved more uncertain, but still reasonable, estimates of overall seroprevalence and R eff as compared to uniform or demographically informed sample sets (Fig. 5) . Here, convenience samples produced higher confidence estimates in the tested subpopulations, but high uncertainty estimates in unsampled populations through our Bayesian modeling framework. In all scenarios, our framework propagated uncertainty appropriately from serological inputs to estimates of overall seroprevalence or R eff . Improved test sensitivity and specificity correspondingly improved estimation and reduced the number of samples that would be required to achieve the same credible interval for a given seroprevalence, and would similarly reduce the sampling needed to equivalent estimation of R eff (Supplementary Figs. S5 and S7).