Reconstructing antibody dynamics to estimate the risk of influenza virus infection

Abstract

For >70 years, a 4-fold or greater rise in antibody titer has been used to confirm influenza virus infections in paired sera, despite recognition that this heuristic can lack sensitivity. Here we analyze with a novel Bayesian model a large cohort of 2353 individuals followed for up to 5 years in Hong Kong to characterize influenza antibody dynamics and develop an algorithm to improve the identification of influenza virus infections. After infection, we estimate that hemagglutination-inhibiting (HAI) titers were boosted by 16-fold on average and subsequently decrease by 14% per year. In six epidemics, the infection risks for adults were 3%–19% while the infection risks for children were 1.6–4.4 times higher than that of younger adults. Every two-fold increase in pre-epidemic HAI titer was associated with 19%–58% protection against infection. Our inferential framework clarifies the contributions of age and pre-epidemic HAI titers to characterize individual infection risk.

Serological classification of influenza infection has classically been based on a four-fold or higher increase in antibody levels, but this approach may not be optimal. Here, the authors develop a Bayesian model to improve identification of infections in serological samples by accounting for individual antibody dynamics.