  Effective biosecurity practices in swine production are key in preventing the
introduction and dissemination of infectious pathogens. Ideally, biosecurity
practices should be chosen by their impact on bio-containment and
bio-exclusion, however quantitative supporting evidence is often unavailable.
Therefore, the development of methodologies capable of quantifying and ranking
biosecurity practices according to their efficacy in reducing risk have the
potential to facilitate better informed choices. Using survey data on
biosecurity practices, farm demographics, and previous outbreaks from 139
herds, a set of machine learning algorithms were trained to classify farms by
porcine reproductive and respiratory syndrome virus status, depending on their
biosecurity practices, to produce a predicted outbreak risk. A novel
interpretable machine learning toolkit, MrIML-biosecurity, was developed to
benchmark farms and production systems by predicted risk, and quantify the
impact of biosecurity practices on disease risk at individual farms.
Quantifying the variable impact on predicted risk 50% of 42 variables were
associated with fomite spread while 31% were associated with local
transmission. Results from machine learning interpretations identified similar
results, finding substantial contribution to predicted outbreak risk from
biosecurity practices relating to: the turnover and number of employees; the
surrounding density of swine premises and pigs; the sharing of trailers;
distance from the public road; and production type. In addition, the
development of individualized biosecurity assessments provides the opportunity
to guide biosecurity implementation on a case-by-case basis. Finally, the
flexibility of the MrIML-biosecurity toolkit gives it potential to be applied
to wider areas of biosecurity benchmarking, to address weaknesses in other
livestock systems and industry relevant diseases.
