Owing to underascertainment it is difficult if not impossible to determine the incidence of a given disease based on cases notified to routine public health surveillance. This is especially true for diseases that are often present in mild forms as for example diarrhoea caused by foodborne bacterial infections. This study presents a Bayesian approach for obtaining incidence estimates by use of measurements of serum antibodies against Salmonella from a cross-sectional study. By comparing these measurements with antibody measurements from a follow-up study of infected individuals it was possible to estimate the time since last infection for each individual in the cross-sectional study. These time estimates were then converted into incidence estimates. Information about the incidence of Salmonella infections in Denmark was obtained by using blood samples from 1780 persons. The estimated incidence was about 0.094 infections per person year. This number corresponds to 325 infections per culture-confirmed case captured in the Danish national surveillance system. We present a novel approach, termed as seroincidence, that has potentials to compare the sensitivity of public health surveillance between different populations, countries and over time.