Campus Units
Veterinary Diagnostic and Production Animal Medicine
Document Type
Article
Publication Version
Accepted Manuscript
Publication Date
8-20-2019
Journal or Book Title
Preventive Veterinary Medicine
First Page
104749
DOI
10.1016/j.prevetmed.2019.104749
Abstract
Investments in biosecurity practices are made by producers to reduce the likelihood of introducing pathogens such as porcine reproductive and respiratory syndrome virus (PRRSv). The assessment of biosecurity practices in breeding herds is usually done through surveys. The objective of this study was to evaluate the use of machine-learning (ML) algorithms to identify key biosecurity practices and factors associated with breeding herds self-reporting (yes or no) a PRRS outbreak in the past 5 years. In addition, we explored the use of the positive predictive value (PPV) of these models as an indicator of risk for PRRSv introduction by comparing PPV and the frequency of PRRS outbreaks reported by the herds in the last 5 years. Data from a case control study that assessed biosecurity practices and factors using a survey in 84 breeding herds in U.S. from 14 production systems were used. Two methods were developed, method A identified 20 variables and accurately classified farms that had reported a PRRS outbreak in the previous 5 years 76% of the time. Method B identified six variables which 5 of these had already been selected by model A, although model B outperformed the former model with an accuracy of 80%. Selected variables were related to the frequency of risk events in the farm, swine density around the farm, farm characteristics, and operational connections to other farms. The PPVs for methods A and B were highly correlated to the frequency of PRRSv outbreaks reported by the farms in the last 5 years (Pearson r = 0.71 and 0.77, respectively). Our proposed methodology has the potential to facilitate producer’s and veterinarian’s decisions while enhancing biosecurity, benchmarking key biosecurity practices and factors, identifying sites at relatively higher risk of PRRSv introduction to better manage the risk of pathogen introduction.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Copyright Owner
Elsevier B.V.
Copyright Date
2019
Language
en
File Format
application/pdf
Recommended Citation
Silva, Gustavo S.; Machado, Gustavo; Baker, Kimberlee L.; Holtkamp, Derald J.; and Linhares, Daniel C.L., "Machine-learning algorithms to identify key biosecurity practices and factors associated with breeding herds reporting PRRS outbreak" (2019). Veterinary Diagnostic and Production Animal Medicine Publications. 139.
https://lib.dr.iastate.edu/vdpam_pubs/139
Included in
Large or Food Animal and Equine Medicine Commons, Veterinary Preventive Medicine, Epidemiology, and Public Health Commons
Comments
This is a manuscript of an article published as Silva, Gustavo S., Gustavo Machado, Kimberlee L. Baker, Derald J. Holtkamp, and Daniel CL Linhares. "Machine-learning algorithms to identify key biosecurity practices and factors associated with breeding herds reporting PRRS outbreak." Preventive Veterinary Medicine (2019): 104749. DOI:10.1016/j.prevetmed.2019.104749. Posted with permission.