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 (https://www.r-project.org/) and C++ (https://isocpp.org/) 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.