A collaborative work between the University of Minnesota, UC- Davis, and Pipestone Veterinary Services was published this past month in the journal Frontiers in Veterinary Science.
Between-farm animal movement, despite being an essential factor of infectious disease spread is not currently recorded in the US. The objective of this project was to create a model to predict animal movement based on between-site distance, ownership, and production type of the sending and receiving farms. The model was able to predict animal movement in the south-central region of the study area with a high aggregation. It also showed an overlap with the distribution of outbreaks of porcine reproductive and respiratory syndrome (PRRS) in this area.
Abstract: Between-farm animal movement is one of the most important factors influencing the spread of infectious diseases in food animals, including in the US swine industry. Understanding the structural network of contacts in a food animal industry is prerequisite to planning for efficient production strategies and for effective disease control measures. Unfortunately, data regarding between-farm animal movements in the US are not systematically collected and thus, such information is often unavailable. In this paper, we develop a procedure to replicate the structure of a network, making use of partial data available, and subsequently use the model developed to predict animal movements among sites in 34 Minnesota counties. First, we summarized two networks of swine producing facilities in Minnesota, then we used a machine learning technique referred to as random forest, an ensemble of independent classification trees, to estimate the probability of pig movements between farms and/or markets sites located in two counties in Minnesota. The model was calibrated and tested by comparing predicted data and observed data in those two counties for which data were available. Finally, the model was used to predict animal movements in sites located across 34 Minnesota counties. Variables that were important in predicting pig movements included between-site distance, ownership, and production type of the sending and receiving farms and/or markets. Using a weighted-kernel approach to describe spatial variation in the centrality measures of the predicted network, we showed that the south-central region of the study area exhibited high aggregation of predicted pig movements. Our results show an overlap with the distribution of outbreaks of porcine reproductive and respiratory syndrome, which is believed to be transmitted, at least in part, though animal movements. While the correspondence of movements and disease is not a causal test, it suggests that the predicted network may approximate actual movements. Accordingly, the predictions provided here might help to design and implement control strategies in the region. Additionally, the methodology here may be used to estimate contact networks for other livestock systems when only incomplete information regarding animal movements is available.
2 thoughts on “Using Machine Learning to Predict Swine Movements”
This was lovely to reead
Thank you Pierre. We are glad you enjoyed it!