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is the (which was not peer-reviewed) The copyright holder for this preprint For four sampling strategies, n = 1000 tests were allocated to age groups with negative tests (grey) and positive tests (colors) as shown, for a test with 93% sensitivity and 97.5% specificity. The MDI strategy shown was designed to optimize estimation of R eff . (E-H) Age-group seroprevalence estimates θ i are shown as boxplots (boxes 90% CIs, whiskers 95% CIs); dots indicate the true values from which data were sampled. Note the decrease uncertainty for boxes with higher sampling rates. (I) Age-group seroprevalences were weighted by population demographics to produce overall seroprevalence estimates, shown as probability densities with 90% credible intervals shaded and highlighted with dashed lines. (J) Age-group seroprevalences were used to estimate R eff under status quo ante contact patterns, shown as probability densities with 90% credible intervals shaded and highlighted with dashed lines. Dashed lines indicate true values from which the data were sampled. Each distribution depicts inference outcomes from a single sent of stochastically sampled data; no averaging is done. Note that although uniform sample allocation produces a more confident estimate of overall seroprevalence, MDI produces a more confident estimate of R eff since it allocates more samples to age groups most relevant to model dynamics.