This is our Friday rubric: every week a new Science Page from the Bob Morrison’s Swine Health Monitoring Project. The previous editions of the science page are available on our website.
This is the second part of the project presented to you last week. The MSHMP team developed a method to track vehicle movement patterns in a system.
Key Points:
Twelve vehicles transporting weanling pigs and culled sows can interconnect a third of the premises within a Midwest swine system of more than 300 farms.
The system is highly inter-connected, with three identified clusters (“communities”)
Each farm can be reached (after passing) through 3 farms within the system-network.
This is our Friday rubric: every week a new Science Page from the Bob Morrison’s Swine Health Monitoring Project. The previous editions of the science page are available on our website.
This week the MSHMP team presents the results of their latest project which proposed to first record vehicle movement patterns to then add that future disease prediction models. This is part 1 of their report. Part number 2 will be on the blog next week.
Key Points
Utilizing movement data to understand network connectivity can provide insights impacting disease control, animal welfare, and other areas.
The number of vehicle movements is constant over season and year, with no high or low seasons indicating a constant level of contacts.
From this subset of transport data, most of the trips are to sow farms (32.5%) and Truck-wash facilities (37.6%).
The average time for vehicle cleaning, disinfection and baking (at the truck-wash facilities) was ~3hrs.
This is our Friday rubric: every week a new Science Page from the Bob Morrison’s Swine Health Monitoring Project. The previous editions of the science page are available on our website.
The movement of live pigs between farms is an important mechanism for disease introduction and spread. Thus, understanding the structure of livestock contacts and studying the routes, volumes, frequency, and the risks associated with animal movement is a prerequisite for effective disease surveillance and control in animal populations.
At the same time, local area spread between neighboring farms is also implicated in the spread of viruses such as porcine epidemic diarrhea virus (PEDV) and porcine reproductive and respiratory syndrome virus (PRRSV).
Even after controlling for hog density and season of the year, we showed that the number of pigs received into neighboring farms was an important predictor of PED infection risk.
Relative importance in predicting risk, according to the Gini index
Key Points
Between farm transmission research in swine has primarily come from small studies rather than large scale datasets.
By looking at environmental/landscape, pig movements, and spatial factors, we studied the likelihood of a farm contracting PED from it’s neighbors.
It was found that the number of pigs received by neighboring farms was an important predictor of PEDV infection risk.
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.
