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Background  Seroepidemiological studies before and after the epidemic wave of H1N1-2009 are useful for estimating population attack rates with a potential to validate early estimates of the reproduction number,  R , in modeling studies.    Methodology/Principal Findings  Since the final epidemic size, the proportion of individuals in a population who become infected during an epidemic, is not the result of a binomial sampling process because infection events are not independent of each other, we propose the use of an asymptotic distribution of the final size to compute approximate 95% confidence intervals of the observed final size. This allows the comparison of the observed final sizes against predictions based on the modeling study ( R  = 1.15, 1.40 and 1.90), which also yields simple formulae for determining sample sizes for future seroepidemiological studies. We examine a total of eleven published seroepidemiological studies of H1N1-2009 that took place after observing the peak incidence in a number of countries. Observed seropositive proportions in six studies appear to be smaller than that predicted from  R  = 1.40; four of the six studies sampled serum less than one month after the reported peak incidence. The comparison of the observed final sizes against  R  = 1.15 and 1.90 reveals that all eleven studies appear not to be significantly deviating from the prediction with  R  = 1.15, but final sizes in nine studies indicate overestimation if the value  R  = 1.90 is used.    Conclusions  Sample sizes of published seroepidemiological studies were too small to assess the validity of model predictions except when  R  = 1.90 was used. We recommend the use of the proposed approach in determining the sample size of post-epidemic seroepidemiological studies, calculating the 95% confidence interval of observed final size, and conducting relevant hypothesis testing instead of the use of methods that rely on a binomial proportion.

Did Modeling Overestimate the Transmission Potential of Pandemic (H1N1-2009)? Sample Size Estimation for Post-Epidemic Seroepidemiological Studies