Introduction: For pathogens such as influenza that cause many subclinical cases, serologic data can be used to estimate attack rates and the severity of an epidemic in near real time. Current methods for analysing serologic data tend to rely on use of a simple threshold or comparison of titres between pre- and post-epidemic, which may not accurately reflect actual infection rates.
Methods: We propose a method for quantifying infection rates using paired sera and bivariate probit models to evaluate the accuracy of thresholds currently used for influenza epidemics with low and high existing herd immunity levels, and a subsequent non-influenza period. Pre- and post-epidemic sera were taken from a cohort of adults in Singapore (n=838). Bivariate probit models with latent titre levels were fit to the joint distribution of haemagglutination-inhibition assay-determined antibody titres using Markov chain Monte Carlo simulation.
Results: Estimated attack rates were 15% (95% credible interval: 12%-19%) for the first H1N1 pandemic wave. For a large outbreak due to a new strain, a threshold of 1:20 and a twofold rise (if pared sera is available) would result in a more accurate estimate of incidence.
Conclusion: The approach presented here offers the basis for a reconsideration of methods used to assess diagnostic tests by both reconsidering the thresholds used and by analysing serological data with a novel statistical model.
Keywords: Bayesian modelling; diagnostic tests; influenza; longitudinal multinomial ordinal probit model; pandemic H1N1; serologic tests.
© 2017 The Authors. Influenza and Other Respiratory Viruses Published by John Wiley & Sons Ltd.