  The COVID-19 infection fatality rate (IFR) is the proportion of individuals
infected with SARS-CoV-2 who subsequently die. As COVID-19 disproportionately
affects older individuals, age-specific IFR estimates are imperative to
facilitate comparisons of the impact of COVID-19 between locations and
prioritize distribution of scare resources. However, there lacks a coherent
method to synthesize available data to create estimates of IFR and
seroprevalence that vary continuously with age and adequately reflect
uncertainties inherent in the underlying data. In this paper we introduce a
novel Bayesian hierarchical model to estimate IFR as a continuous function of
age that acknowledges heterogeneity in population age structure across
locations and accounts for uncertainty in the estimates due to seroprevalence
sampling variability and the imperfect serology test assays. Our approach
simultaneously models test assay characteristic, serology, and death data,
where the serology and death data are often available only for binned age
groups. Information is shared across locations through hierarchical modeling to
improve estimation of the parameters with limited data. Modeling data from 26
developing country locations during the first year of the COVID-19 pandemic, we
found seroprevalence did not change dramatically with age, and the IFR at age
60 was above the high-income country benchmark for most locations.
