Individual and Population Trajectories of Influenza Antibody Titers Over Multiple Seasons in a Tropical Country

Abstract

Seasonal influenza epidemics occur year-round in the tropics, complicating the planning of vaccination programs. We built an individual-level longitudinal model of baseline antibody levels, time of infection, and the subsequent rise and decay of antibodies postinfection using influenza A(H1N1)pdm09 data from 2 sources in Singapore: 1) a noncommunity cohort with real-time polymerase chain reaction–confirmed infections and at least 1 serological sample collected from each participant between May and October 2009 (n = 118) and 2) a community cohort with up to 6 serological samples collected between May 2009 and October 2010 (n = 760). The model was hierarchical, to account for interval censoring and interindividual variation. Model parameters were estimated via a reversible jump Markov chain Monte Carlo algorithm using custom-designed R () and C++ () code. After infection, antibody levels peaked at 4–7 weeks, with a half-life of 26.5 weeks, followed by a slower decrease up to 1 year to approximately preinfection levels. After the third wave, the seropositivity rate and the population-level antibody titer dropped to the same level as they were at the end of the first pandemic wave. The results of this analysis are consistent with the hypothesis that the population-level effect of individuals’ waxing and waning antibodies influences influenza seasonality in the tropics.

Cost-Effectiveness Analysis for Influenza Vaccination Coverage and Timing in Tropical and Subtropical Climate Settings: A Modeling Study

“…We adopted a previously published model of antibody titers developed from a cohort with multiple serological samples across 3 postpandemic waves. 22 Antibody titers were modeled on a logarithmic scale (1 for 1:10, 2 for 1:20, etc), with titers rising and falling postinfection. The validity of this model has been previously demonstrated.…”

“…The validity of this model has been previously demonstrated. 22 The titer for individual i at time t was modeled by a normally distributed variable, Z it wN (m it , s 2 ) The mean titer m it can be modeled as m it = B i if individual i is neither infected nor vaccinated and B i 1R i f (t2T i ,k i ,q i ) otherwise, where B i is the baseline titer level, R i is the additional titer due to infection or influenza vaccination at the time of peak rise after infection, and f ðx;…”

“…We assumed the parameters (B I , k I , q I , R i ) follow independent log-normal distributions, where the means and standard deviations are estimated from Zhao et al 22 Contact patterns by age groups estimated by Prem et al 23 are used to adjust the risk of transmission between individuals. This was based on data from population-based contact diaries in 8 European countries and projected to 144 other countries using a Bayesian hierarchical model with Markov chain Monte Carlo simulation.…”

Age-specific differences in the dynamics of protective immunity to influenza

“…The observed titers are fold-dilutions in the range [<1:10, 1:10, 1:20 ... , 1:5120]. Consistent with other models [16,12,14], we define a log titer (logH) for any titer h,…”

“…The HI titer corresponding to 50% protection against infection, commonly cited as 40 [10,11], may vary by influenza A subtype and host age [12,13], although measurement error, long intervals between titer measurements, and variable titer changes after infection complicate inferences. Recent models have made progress by incorporating measurement error [14,15], representing infections as latent states [14,16,17], and using titers to historic strains to measure the intervals between infections [14], attack rates [15,16], and the breadth of the response over time [14,17]. But the relatively short periods of observation in these studies have made it difficult to estimate some basic quantities in the response to infection, namely, how long protection lasts, and whether antibody titers adequately reflect the strength of protection against infection in individuals over time.…”