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

To demonstrate the effects of MDI sample allocation, we used it to design a strategy to optimize estimates of R eff and then tested the performance of its sample allocations against those of blood donations, neonatal heel sticks, and uniform sampling. MDI produced higher confidence posterior estimates (Fig. 5J, Supplementary Fig. S7 ). Importantly, because the relative importance of subpopulations in a model may vary based on the hypothetical interventions being modeled (e.g., the re-opening of workplaces would place higher importance on the serological status of working-age adults), MDI sample allocation recommendations may have to be derived for multiple hypothetical interventions and then averaged to design a study from which the largest variety of high-confidence results can be derived. To see how such recommendations would work in practice, we computed MDI recommendations to optimize three scenarios for the contact patterns and demography of the U.S. and India, deriving a balanced sampling recommendation (Fig. 6 ).