Bayesian risk assessment for Salmonella in egg laying flocks under zero apparent prevalence and dynamic test sensitivity

Abstract

A continuous time two-state hidden Markov process model was used to describe prevalence of salmonella infected flocks over laying phase in egg production. The infection status of a flock was treated as a binary hidden variable that can be detected as salmonella positive only by imperfect microbiological testing. Sensitivity of the test depends on the sampling type and analysis method used, but also on the unknown phase of epidemic among the hens within the flock. In a data set obtained from a national control programme under very low prevalence, all tests at all ages may show negative results. However, some temporally varying uncertainty remains about the unknown true prevalence, due to temporal changes in overall test sensitivity. By defining the sensitivity as a function of duration of within flock epidemic, Bayesian modeling was developed for quantitative risk assessment. Using minimal assumptions derived from expert knowledge or plausible scenarios, the effect of dynamically changing test sensitivity was accounted for by integration over the unknown time of infection. The sensitivity model was combined with the hidden Markov process model, conditional to temporal sequence of test results. Computations were performed using OpenBUGS.