Bayesian Zero‐Inflated Predictive Modelling of Herd‐Level Salmonella Prevalence for Risk‐Based Surveillance

Abstract

Impacts• Implementing reduction strategies for surveillance systems that were developed to protect public health is challenging. The apparently contradictory requirements for continued consumer confidence in food supply, and cost reduction for industry need to be met.• We use zero-inflated modelling to demonstrate how targeting farms in space and by risk factors has the potential to result in more efficient ways of conducting surveillance.• This allows assessment of the factors that affect herd infection status (introduction) and those that affect the sero-prevalence in infected herds (persistence and spread). Partitioning risk factors is valuable for directing practical risk-mitigating advice. SummaryThe national control programme for Salmonella in Danish swine herds introduced in 1993 has led to a large decrease in pork-associated human cases of salmonellosis. The pork industry is increasingly focused on the cost-effectiveness of surveillance while maintaining consumer confidence in the pork food supply. Using national control programme data from 2003 and 2004, we developed a zero-inflated binomial model to predict which farms were most at risk of Salmonella. We preferentially sampled these high-risk farms using two sampling schemes based on model predictions resulting from a farm's covariate pattern and its random effect. Zero-inflated binomial modelling allows assessment of similarities and differences between factors that affect herd infection status (introduction), and those that affect the seroprevalence in infected herds (persistence and spread). Both large (producing greater than 5000 pigs per annum), and small herds (producing less than 2000 pigs per annum) were at significantly higher risk for infection and subsequent seroprevalence, when compared with medium sized herds (producing between 2000 and 5000 pigs per annum). When compared with herds being located elsewhere, being located in the south of Jutland significantly decreased the risk of herd infection, but increased the risk of a pig from an infected herd being seropositive. The model suggested that many of the herds where Salmonella was not detected were infected, but at a low prevalence. Using cost and sensitivity, we compared the results of our model based sampling schemes with those under the standard sampling scheme, based on herd size, and the recently introduced risk-based approach. Model-based results were less sensitive but show significant cost savings. Further model refinements, sampling schemes and the methods to evaluate their performance are important areas for future work, and these should continue to occur in direct consultation with Danish authorities.