Bayesian hidden Markov process model of Salmonella over three generations of flocks in broiler production chain

A bayesian primary production chain model of salmonella in broiler production was developed in 2001 for a Quantitative Risk Assessment [1], QRA. This original discrete time Markov chain model over three generations of flocks (grandparent, parent, and production flocks) was based on data provided by the Finnish Salmonella Control Programme, FSCP. The discrete time stochastic model described the status (true infection state) of each flock at regular time intervals corresponding to the testing times of the FSCP. Although detailed flock specific data were not available, it was possible to use summary data together with auxiliary information about the testing times (ages), number of flock holdings, and the fact that with breeding flocks a positive test result leads to destruction of the flock. Also, informative priors on parameters such as test sensitivity had to be used in a fully Bayesian approach. Based on all available information it was possible to construct a data set in a more detailed manner, regarding life histories, than with the summary data alone. Otherwise, as such, the summary data would be very difficult to use towards estimation of meaningful epidemiological quantities. The model structures closely corresponded to the structure of the production chain, combining models of horizontal infection and vertical infection between flock generations. This leads to a fairly complicated hierarchical Bayesian model with latent variables, accounting for imperfect testing (sensitivity