Timescales of influenza A/H3N2 antibody dynamics

Human immunity influences the evolution and impact of influenza strains. Because individuals are infected with multiple influenza strains during their lifetime, and each virus can generate a cross-reactive antibody response, it is challenging to quantify the processes that shape observed immune responses or to reliably detect recent infection from serological samples. Using a Bayesian model of antibody dynamics at multiple timescales, we explain complex cross-reactive antibody landscapes by inferring participants’ histories of infection with serological data from cross-sectional and longitudinal studies of influenza A/H3N2 in southern China and Vietnam. We find that individual-level influenza antibody profiles can be explained by a short-lived, broadly cross-reactive response that decays within a year to leave a smaller long-term response acting against a narrower range of strains. We also demonstrate that accounting for dynamic immune responses alongside infection history can provide a more accurate alternative to traditional definitions of seroconversion for the estimation of infection attack rates. Our work provides a general model for quantifying aspects of influenza immunity acting at multiple timescales based on contemporary serological data and suggests a two-armed immune response to influenza infection consistent with competitive dynamics between B cell populations. This approach to analysing multiple timescales for antigenic responses could also be applied to other multistrain pathogens such as dengue and related flaviviruses.Author summary: It is challenging to determine the true extent of influenza infection and immunity within a population, because a person’s immune response to a specific influenza strain depends both on past infections with that strain as well as immunity generated by related influenza strains. To untangle these processes, we developed a mathematical model that considered individual histories of influenza infection and immune dynamics acting at multiple timescales. We combined this model with surveys of antibody levels in different individuals, showing how antibody dynamics are influenced by a short-lived, broadly cross-reactive response against a wide range of strains that wanes over time to leave a long-term response against a narrower collection of strains. By accounting for such short- and long-term responses, we also found that it was possible to obtain better estimates of the frequency of influenza infection. These methods could help to guide the design of studies to estimate key aspects of influenza immune dynamics or to estimate historical infection rates and would also be applicable to other pathogens with multiple strains.