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.