Reconstruction of antibody dynamics and infection histories to evaluate dengue risk.

Abstract

As with many pathogens, most dengue infections are subclinical and therefore unobserved1. Coupled with limited understanding of the dynamical behavior of potential serological markers of infection, this observational problem has wide-ranging implications, including hampering our understanding of individual- and population-level correlates of infection and disease risk and how they change over time, assay interpretation and cohort design. We develop a framework that simultaneously characterizes antibody dynamics and identifies subclinical infections via Bayesian augmentation from detailed cohort data (3,451 individuals with blood draws every 91 days, 143,548 hemagglutination inhibition assay titer measurements)2,3. We identify 1,149 infections (95% CI: 1,135–1,163) that were not detected by active surveillance and estimate that 65% of infections are subclinical. Post infection, individuals develop a stable setpoint antibody load after 1y that places them within or outside a risk window. Individuals with pre-existing titers of ≤1:40 develop hemorrhagic fever 7.4 (95% CI: 2.5–8.2) times as often as naïve individuals compared to 0.0 times for individuals with titers >1:40 (95% CI: 0.0–1.3). PRNT titers ≤1:100 were similarly associated with severe disease. Across the population, variability in the force of infection results in large-scale temporal changes in infection and disease risk that correlate poorly with age.