Abstract
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.
Highlights
Immunity against influenza A can influence the severity of disease [1, 2], the effectiveness of vaccination strategies [3], and the emergence of novel strains [4, 5]
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
We developed a mathematical model that considered individual histories of influenza infection and immune dynamics acting at multiple timescales
Summary
Immunity against influenza A can influence the severity of disease [1, 2], the effectiveness of vaccination strategies [3], and the emergence of novel strains [4, 5]. There is evidence that influenza infection leads to ‘back-boosting’, generating a transient, broadly cross-reactive response against historical strains [10,11,12]. It has been suggested that influenza responses are influenced by antigenic seniority, with strains seen earlier in life shaping subsequent antibody responses [13]. This is a refinement on the earlier concept of ‘original antigenic sin’, whereby the largest antibody response is maintained against the first infection of a lifetime [14]
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