title: Did modeling overestimate the transmission potential of pandemic (H1N1-2009)? Sample size estimation for post-epidemic seroepidemiological studies.
abstract: 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.
  
  
  RESULTS
  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.
authors: PRESTO, Japan Science and Technology Agency, Saitama, Japan. nishiura@hku.hk
authors: Hiroshi
authors: H
authors: Nishiura
authors: Gerardo
authors: G
authors: Chowell
authors: Carlos
authors: C
authors: Castillo-Chavez
mesh_terms: D006801:Humans; D053118:Influenza A Virus, H1N1 Subtype / Q000472:pathogenicity*; D007251:Influenza, Human / Q000453:epidemiology* / Q000635:transmission*; D058873:Pandemics; D018401:Sample Size; D016036:Seroepidemiologic Studies