Abstract
Seroepidemiological studies aim to understand population-level exposure and immunity to infectious diseases. Their results are normally presented as binary outcomes describing the presence or absence of pathogen-specific antibody, despite the fact that many assays measure continuous quantities. A population’s natural distribution of antibody titers to an endemic infectious disease may include information on multiple serological states – naiveté, recent infection, non-recent infection, childhood infection – depending on the disease in question and the acquisition and waning patterns of immunity. In this study, we investigate 20,152 general-population serum samples from southern Vietnam collected between 2009 and 2013 from which we report antibody titers to the influenza virus HA1 protein using a continuous titer measurement from a protein microarray assay. We describe the distributions of antibody titers to subtypes 2009 H1N1 and H3N2. Using a model selection approach to fit mixture distributions, we show that 2009 H1N1 antibody titers fall into four titer subgroups and that H3N2 titers fall into three subgroups. For H1N1, our interpretation is that the two highest-titer subgroups correspond to recent and historical infection, which is consistent with 2009 pandemic attack rates. Similar interpretations are available for H3N2, but right-censoring of titers makes these interpretations difficult to validate.
Highlights
Seroepidemiological studies aim to understand population-level exposure and immunity to infectious diseases
We found that H1N1 antibody titer distribution are best classified into four titer groups, that H3N2 is best classified into three groups, and that censoring may have prevented a complete classification of H3N2 titers
Using a large collection of serum samples and a continuous measurement of antibody titer, we were able to describe the natural distribution of antibody titers to the 2009 H1N1 and H3N2 subtypes of influenza virus
Summary
Seroepidemiological studies aim to understand population-level exposure and immunity to infectious diseases. To correctly translate a population’s antibody titer distribution to its epidemic history, accurate measures of both these rates are necessary To validate that this reconstruction has been done correctly, a large cohort with long-term follow-up and precise antibody measurements would be required. To begin investigating what an antibody distribution can tell us about a population’s epidemic history, we initiated a large-scale time-structured serological survey[1, 2] and an observational clinical study that includes repeat patient follow-ups to measure rates of antibody waning[3]; the results of the serological survey are presented here. The cutoff value for seropositivity is typically calibrated from a group of patients with confirmed acute infection, by collecting convalescent serum samples a few weeks or a few months after symptoms onset This means that the correct application of the cut-off value is the identification of recent symptomatic infections rather than any past infections. Non-binary analyses of serological data are present in the literature for a range of pathogens[10,11,12,13,14,15,16,17,18] including influenza virus[19, 20], but very few of these studies are able to look at non-vaccinated populations and none have the scale and precision presented here
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