title: Use of Bayesian Belief Network techniques to explore the interaction of biosecurity practices on the probability of porcine disease occurrence in Canada.
abstract: Identification and quantification of pathogen threats need to be a priority for the Canadian swine industry so that resources can be focused where they will be most effective. Here we create a tool based on a Bayesian Belief Network (BBN) to model the interaction between biosecurity practices and the probability of occurrence of four different diseases on Canadian swine farms. The benefits of using this novel approach, in comparison to other methods, is that it enables us to explore both the complex interaction and the relative importance of biosecurity practices on the probability of disease occurrence. In order to build the BBN we used two datasets. The first dataset detailed biosecurity practices employed on 218 commercial swine farms across Canada in 2010. The second dataset detailed animal health status and disease occurrence on 90 of those farms between 2010 and 2012. We used expert judgement to identify 15 biosecurity practices that were considered the most important in mitigating disease occurrence on farms. These included: proximity to other livestock holdings, the health status of purchased stock, manure disposal methods, as well as the procedures for admitting vehicles and staff. Four diseases were included in the BBN: Porcine reproductive and respiratory syndrome (PRRS), (a prevalent endemic aerosol pathogen), Swine influenza (SI) (a viral respiratory aerosol pathogen), Mycoplasma pneumonia (MP) (an endemic respiratory disease spread by close contact and aerosol) and Swine dysentery (SD) (an enteric disease which is re-emerging in North America). This model indicated that the probability of disease occurrence was influenced by a number of manageable biosecurity practices. Increased probability of PRRS and of MP were associated with spilt feed (feed that did not fall directly in a feeding trough), not being disposed of immediately and with manure being brought onto the farm premises and spread on land adjacent to the pigs. Increased probabilities of SI and SD were associated with the farm allowing access to visiting vehicles without cleaning or disinfection. SD was also more likely to occur when the health status of purchased stock was not known. Finally, we discuss how such a model can be used by the Canadian swine industry to quantify disease risks and to determine practices that may reduce the probability of disease occurrence.
authors.affiliation: Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada. Electronic address: rucox@upei.ca.
authors.forename: Ruth
authors.initials: R
authors.lastname: Cox
authors.affiliation: Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada.
authors.forename: Crawford W
authors.initials: CW
authors.lastname: Revie
authors.affiliation: Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada.
authors.forename: Daniel
authors.initials: D
authors.lastname: Hurnik
authors.affiliation: Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada.
authors.forename: Javier
authors.initials: J
authors.lastname: Sanchez
mesh_terms: D000383:Agriculture / Q000379:methods; D000822:Animal Husbandry; D000818:Animals; D001499:Bayes Theorem; D002170:Canada / Q000453:epidemiology; D005260:Female; D008297:Male; D008954:Models, Biological; D011336:Probability; D013552:Swine; D013553:Swine Diseases / Q000453:epidemiology / Q000517:prevention & control*
keywords: Bayesian Belief Network; Biosecurity; Disease probability; Swine farm