Abstract
Directly measuring evidence of influenza infections is difficult, especially in low-surveillance settings such as sub-Saharan Africa. Using a Bayesian model, we estimated unobserved infection times and underlying antibody responses to influenza A/H3N2, using cross-sectional serum antibody responses to 4 strains in children aged 24–60 months. Among the 242 individuals, we estimated a variable seasonal attack rate and found that most children had ≥1 infection before 2 years of age. Our results are consistent with previously published high attack rates in children. The modeling approach highlights how cross-sectional serological data can be used to estimate epidemiological dynamics.
Highlights
Influenza epidemics cause substantial global burden [1], with individuals infected with multiple viral strains during their lifetime [2, 3]
Grey filled circles indicate the frequency of A/H3N2 infections that were observed in u Senegal from 2013 to 2015. b) the estimated attack rate for the Gambia cohorts with 95% quantile interval, c) the estimated number of infections per age in months, n d) the estimated age at first infection in months. a As well as estimating population-level dynamics, we could compare individual-level infection histories
Discussion t By testing contemporary serum against strains antigenically similar to those circulating during the rip lifetime of a paediatric cohort, and adjusting for cross-reactive antibody dynamics and assay uncertainty using a Bayesian model, we estimated the epidemiology of childhood influenza A/H3N2 infections in The Gambia
Summary
Influenza epidemics cause substantial global burden [1], with individuals infected with multiple viral strains during their lifetime [2, 3]. Combining these haemagglutination inhibition (HI) assays with a Bayesian model of unobserved infections and antibody dynamics [9, 10], we estimated the frequency and timing of infections in the cohort.
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