title: Bayesian zero-inflated predictive modelling of herd-level Salmonella prevalence for risk-based surveillance.
abstract: The 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.
authors.affiliation: EpiCentre, Institute of Veterinary, Animal and Biomedical Sciences, Massey University, Palmerston North, New Zealand (place where work was carried out). j.benschop@massey.ac.nz
authors.forename: J
authors.initials: J
authors.lastname: Benschop
authors.forename: S
authors.initials: S
authors.lastname: Spencer
authors.forename: L
authors.initials: L
authors.lastname: Alban
authors.forename: M
authors.initials: M
authors.lastname: Stevenson
authors.forename: N
authors.initials: N
authors.lastname: French
mesh_terms: D000003:Abattoirs; D000818:Animals; D001499:Bayes Theorem; D003718:Denmark / Q000453:epidemiology; D006801:Humans; D008460:Meat / Q000382:microbiology; D015233:Models, Statistical; D011237:Predictive Value of Tests; D018570:Risk Assessment; D012475:Salmonella / Q000302:isolation & purification*; D012478:Salmonella Food Poisoning / Q000517:prevention & control*; D012481:Salmonella Infections, Animal / Q000175:diagnosis / Q000191:economics / Q000453:epidemiology* / Q000382:microbiology / Q000517:prevention & control; D012680:Sensitivity and Specificity; D018571:Sentinel Surveillance / Q000662:veterinary; D016036:Seroepidemiologic Studies; D013552:Swine; D013553:Swine Diseases / Q000175:diagnosis / Q000191:economics / Q000453:epidemiology* / Q000382:microbiology / Q000517:prevention & control