Evaluation of Salmonella surveillance in pigs using a stochastic simulation model

Abstract

Abstract. Pork is an important source of human Salmonellosis. Therefore efficient Salmonella surveillance programmes are recommended for minimising Salmonella entry into food production. With a stochastic simulation model, a Salmonella control programme (based on meat juice samples) was analysed depending on sample size (30 to 200 animals per finishing farm and year), prevalence in the population (8,13 and 24 %), farm size (400, 1500 and 3000 places per farm), threshold level classifying a farm as positive (20 and 40 %) and sensitivity of the test. The simulation model generates an integrated pig production chain with linkages between the stages farrowing, rearing, fattening and slaughter starting with the purchase of gilts. Salmonella may enter the system via purchase of animals, feedstuffs and other vectors. Crowding effects and pen-to-pen infection were also taken into account. Within each herd a dynamic pattern of Salmonella infection was simulated. The simulation covered a time interval of 24 months. Sensitivity, specificity and the predictive values were used for the evaluation of the different sampling strategies. At the base situation (prevalence 8%, 1500 places per finishing farm, 60 animals sampled per year and a threshold of 40 %) sensitivity was 65.9 and specificity 96.6 %. Increasing the sample size mainly improved the sensitivity (74.2 to 77.0 %). If the prevalence in the population is enhanced, more animals have to be sampled in order to realise the same detection rate. The effect of the farm size was negligible. The pattern of the Salmonella infection within a farm over 24 months demonstrated that a classification of farms supposes a handling time of 12 months.