Abstract
Effective biosecurity practices in swine production are key in preventing the introduction and dissemination of infectious pathogens. Ideally, on-farm 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 disease risk has the potential to facilitate better-informed choices of biosecurity practices. 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 and farm demographics, 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. By 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 haul trailers, distance from the public road and farm production type. In addition, the development of individualized biosecurity assessments provides the opportunity to better guide biosecurity implementation on a case-by-case basis. Finally, the flexibility of the MrIML-biosecurity toolkit gives it the potential to be applied to wider areas of biosecurity benchmarking, to address biosecurity weaknesses in other livestock systems and industry-relevant diseases.
Highlights
Porcine reproductive and respiratory syndrome virus (PRRSV) is the most economically relevant endemic disease for the North American swine industry (Neumann et al, 2005; Holtkamp et al, 2013; Pileri and Mateu, 2016), and widely endemic in Europe (Zimmerman et al, 2019; Renken et al, 2021)
We developed and applied an interpretable machine learning methodology to assess the impact of on-farm biosecurity practices on the predicted risk of PRRSV outbreaks
Descriptive results regarding the distribution of farms and PRRSV outbreaks, and a summary of the biosecurity practices and farm demographics can be found in Supplementary Figures S38, S39 and S40, and Supplementary Tables S1-S3, respectively
Summary
Porcine reproductive and respiratory syndrome virus (PRRSV) is the most economically relevant endemic disease for the North American swine industry (Neumann et al, 2005; Holtkamp et al, 2013; Pileri and Mateu, 2016), and widely endemic in Europe (Zimmerman et al, 2019; Renken et al, 2021). Recent efforts directed at regionally controlling or eradicating PRRSV in the U.S have not been fully successful, which may be attributed, in part, to low enrollment in such projects and a lack of understanding of regional pig dynamics (Corzo et al, 2010; ValdesDonoso et al, 2016). Countries such as Sweden, Norway, and Switzerland have successfully controlled the virus through total depopulation/repopulation strategies, and other European countries such as Denmark controlling PRRSV with a combination of biosecurity measures and immunization strategies (Baekbo and Kristensen, 2015; Rathkjen and Dall, 2017). When choosing biosecurity practices, producers and veterinarians commonly balance their effectiveness against pathogen transmission with cost; the most effective practices are not necessarily the most economically efficient (i.e., depopulation, farm closure) (Corzo et al, 2010; Pileri and Mateu, 2016; Nathues et al, 2018; Jurado et al, 2019; Silva et al, 2019)
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