Abstract
The presence of serum antibodies is a biomarker of past infection. Instead of seroclassification aimed at measuring seroprevalence a population sample of serum antibody levels may be used to estimate the incidence of seroconversion. This article expands an earlier study into seroincidence estimation, employing models of the seroresponse that include probability of escaping infection, as well as nonexponential decay kinetics and different sources of noise. As previously, a constant force of infection is assumed. When the seroconversion rate is low, a substantial fraction of the population may not be old enough to have experienced any seroconversions, causing underestimation of seroconversion rates that may be substantial at young ages. A correction is given that can be shown to remove such age dependent bias. Simulation studies show that the updated models provide accurate estimates of seroconversion rates, but also that the presence of noise, when unaccounted for, may introduce considerable bias, especially at low (< 0.1/yr) seroconversion rates and young ages. The revised serocalculator scripts can be used to update the R package "seroincidence."
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