The paper extends the susceptible–exposed–infective–removed model to handle
heterogeneity introduced by spatially arranged populations, biologically plausible distributional
assumptions and incorporation of observations from additional diagnostic tests. These extensions
are motivated by a desire to analyse disease transmission experiments in a more detailed
fashion than before. Such experiments are performed by veterinarians to gain knowledge about
the dynamics of an infectious disease. By fitting our spatial susceptible–exposed–infective–
removed with diagnostic testing model to data for a specific disease and production environment
a valuable decision support tool is obtained, e.g. when evaluating on-farm control measures.
Partial observability of the epidemic process is an inherent problem when trying to estimate
model parameters from experimental data.We therefore extend existing work on Markov chain
Monte Carlo estimation in partially observable epidemics to the multitype epidemic set-up of
our model. Throughout the paper, data from a Belgian classical swine fever virus transmission
experiment are used as a motivating example.