Rethinking thresholds for serological evidence of influenza virus infection

• Published in: Influenza and other respiratory viruses 2017-05, Vol.11 (3), p.202-210
• Main Authors: Zhao, Xiahong, Siegel, Karen, Chen, Mark I‐Cheng, Cook, Alex R.
• Format: Article
• Language: English
• Subjects: Adults; Antibodies, Viral - blood; Antibodies, Viral - immunology; Bayesian modelling; Bivariate analysis; Comparative analysis; Computer simulation; Data processing; Diagnostic systems; diagnostic tests; Epidemics; Health aspects; Hemagglutination inhibition; Hemagglutination Inhibition Tests; Herd immunity; Humans; Immunity; Incidence; Infections; Influenza; Influenza A Virus, H1N1 Subtype - genetics; Influenza A Virus, H1N1 Subtype - immunology; Influenza A Virus, H1N1 Subtype - isolation & purification; Influenza viruses; Influenza, Human - blood; Influenza, Human - epidemiology; Influenza, Human - immunology; Influenza, Human - virology; longitudinal multinomial ordinal probit model; Markov analysis; Markov chains; Markov processes; Mathematical models; Monte Carlo method; Monte Carlo simulation; Original; pandemic H1N1; Pandemics; Prospective Studies; Respiratory diseases; serologic tests; Simulation; Singapore - epidemiology; Thresholds; Viruses
• ISSN: 1750-2640; 1750-2659
• DOI: 10.1111/irv.12452

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.