Population-based serological surveys are a key tool in epidemiology to characterize the level
of population immunity and reconstruct the past circulation of pathogens. A variety of
serocatalytic models have been developed to estimate the force of infection (FOI) (i.e., the
rate at which susceptible individuals become infected) from age-stratified seroprevalence
data. However, few tool currently exists to easily implement, combine, and compare these
models. Here, we introduce an R package, Rsero, that implements a series of serocatalytic
models and estimates the FOI from age-stratified seroprevalence data using Bayesian
methods. The package also contains a series of features to perform model comparison and
visualise model fit. We introduce new serocatalytic models of successive outbreaks and
extend existing models of seroreversion to any transmission model. The dieerent features of
the package are illustrated with simulated and real-life data. We show we can identify the
correct epidemiological scenario and recover model parameters in dieerent epidemiological
settings. We also show how the package can support serosurvey study design in a variety of
epidemic situations. This package provides a standard framework to epidemiologists and
modellers to study the dynamics of past pathogen circulation from cross-sectional
serological survey data. It is now being used to characterize the epidemiology of CCHF and
Rift Valley Fever in Cameroon and Senegal.
