Accounting for Uncertainty in Participant Age in Serocatalytic Models

Serological surveys measure the presence of antibodies in a population to infer past exposure to an infectious pathogen. If study participants' ages are known, serocatalytic models can be used to retrace the historical transmission strength of a pathogen within that population, quantified by the force of infection (FOI). These models rely on age information as a key variable since infection risks are interpreted in relation to how long individuals have been at risk. However, due to data constraints, participants' ages may be provided only within "age bins". A common approach is then to assign individuals' ages to midpoints of their respective age bins, ignoring uncertainty in this quantity. In this study, we quantify the bias introduced by this midpoint approach and develop a Bayesian framework that explicitly accounts for uncertainty in age. By comparing inference under constant, age-dependent, and time-dependent FOI scenarios, we show that the proposed binned model yields more reliable FOI estimates without sacrificing computational complexity, whereas the midpoint approach can underestimate FOI under constant transmission and introduce further biases as age bin width increases. These improvements support the interpretation of serological data and inform public health decisions, such as estimating disease burden and identifying targeted vaccination groups.