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Discrimination of influenza infection (A/2009 H1N1) from prior exposure by antibody protein microarray analysis

  • Dennis te Beest
  • , Erwin de Bruin
  • , Sandra Imholz
  • , Jacco Wallinga
  • , Peter Teunis
  • , Marion Koopmans
  • , Michiel van Boven

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

Reliable discrimination of recent influenza A infection from previous exposure using hemagglutination inhibition (HI) or virus neutralization tests is currently not feasible. This is due to low sensitivity of the tests and the interference of antibody responses generated by previous infections. Here we investigate the diagnostic characteristics of a newly developed antibody (HA1) protein microarray using data from cross-sectional serological studies carried out before and after the pandemic of 2009. The data are analysed by mixture models, providing a probabilistic classification of sera (susceptible, prior-exposed, recently infected). Estimated sensitivity and specificity for identifying A/2009 infections are low using HI (66% and 51%), and high when using A/2009 microarray data alone or together with A/1918 microarray data (96% and 95%). As a heuristic, a high A/2009 to A/1918 antibody ratio (>1.05) is indicative of recent infection, while a low ratio is indicative of a pre-existing response, even if the A/2009 titer is high. We conclude that highly sensitive and specific classification of individual sera is possible using the protein microarray, thereby enabling precise estimation of age-specific infection attack rates in the population even if sample sizes are small.

Original languageEnglish
Article numbere113021
JournalPLoS ONE
Volume9
Issue number11
DOIs
Publication statusPublished - 2014
Externally publishedYes

Keywords

  • Age Factors
  • Antibodies, Viral/immunology
  • Diagnosis, Differential
  • Hemagglutination Inhibition Tests
  • Humans
  • Influenza A Virus, H1N1 Subtype
  • Influenza, Human/diagnosis
  • Markov Chains
  • Microarray Analysis
  • Models, Biological
  • Monte Carlo Method
  • Netherlands/epidemiology

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