2021
DOI: 10.1111/tbed.14369
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Interpretable machine learning applied to on‐farm biosecurity and porcine reproductive and respiratory syndrome virus

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 have the potential to facilitate better informed choic… Show more

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Cited by 15 publications
(8 citation statements)
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“…Additionally, the on‐farm model parameters were oversimplified, since we have based those estimations through historical records of PRRSV outbreaks, in which farms with fewer infections were considered to have better biosecurity levels. Although there are several ways that the current version of PigSpread model can be expanded, the inclusions of specific on‐farm biosecurity practices and infrastructure (e.g., present of cleaning and disinfection stations) could not only improve model calibration but also analyze the role of individualized and combined biosecurity on PRRSV dissemination (Sykes et al., 2021). Another important limitation of our modelling work was the lack of information about the vaccination strategies used by each farm, which could have contributed to the probability of new PRRSV outbreaks (Galvis et al., 2022).…”
Section: Discussionmentioning
confidence: 99%
“…Additionally, the on‐farm model parameters were oversimplified, since we have based those estimations through historical records of PRRSV outbreaks, in which farms with fewer infections were considered to have better biosecurity levels. Although there are several ways that the current version of PigSpread model can be expanded, the inclusions of specific on‐farm biosecurity practices and infrastructure (e.g., present of cleaning and disinfection stations) could not only improve model calibration but also analyze the role of individualized and combined biosecurity on PRRSV dissemination (Sykes et al., 2021). Another important limitation of our modelling work was the lack of information about the vaccination strategies used by each farm, which could have contributed to the probability of new PRRSV outbreaks (Galvis et al., 2022).…”
Section: Discussionmentioning
confidence: 99%
“…Although there are several ways that the current version of PigSpread model can be expanded, the inclusions of specific on-farm biosecurity practices and infrastructure (e.g. present of cleaning and disinfection stations) could not only improve model calibration, but to analyze the role of individualized and combined biosecurity on PRRSV dissemination (Sykes et al, 2021). Another important limitation of our modelling work was the lack of information about the vaccination strategies used by each farm, which could have contributed to the probability of new PRRSV outbreaks (Galvis et al, 2021).…”
Section: Discussionmentioning
confidence: 99%
“…In this paper, in addition to SHAP, we will focus on two other instance-level post-hoc model interpretability approaches, namely breakDown (BD) analysis and Ceteris-Paribus (CP) profiles, that have received little attention in the materials science community. The BD method, similar to the SHAP method, is also based on the variable attribution principle that decomposes the prediction of each individual observation into particular variable contributions 30 , 31 . Unlike the SHAP values, the BD values provide order specific explanations of variables’ contributions in a greedy way 5 .…”
Section: Introductionmentioning
confidence: 99%