By Lorraine Stevenson-Hall
Our farm networks are more connected than we think, and people movement may be a bigger risk for animal disease transmission than we realize. It’s common knowledge that animal movement creates a risk of spreading disease through animal-to-animal contact, but the human factor is potentially equally or even more important.
Dr. Tara Prezioso, DVM MPH at University of Illinois Department of Pathobiology, is working with a full year’s worth of movement data from three large swine systems. Anonymized data was provided by Farm Health Guardian for this research. The data represents movements in and out of 455 properties over a 12-month period and includes over 500,000 visits. The properties include finishing sites, sow barns, feed mills and truck washes. The visit types include people, livestock and deadstock trucks, feed trucks and service vehicles.
The goal is to develop a predictive analytics model to see where disease spread is likely to occur. It is expected that this will be a valuable tool with the potential to stop animal disease before it happens and prevent its spread.
Four groups of data, or subnetworks were created and compared:
- Full network (all movements included)
- Human network (employees, visitors, maintenance, etc.)
- Animal network (only livestock and deadstock truck movements)
- Truck network (only truck visits for which there is no human contact e.g. feed delivery)
“Monitoring human movement is just as or more important than monitoring animal movement alone.”
Prezioso aimed to see whether including the human movements into the network significantly changed the statistics and therefore how disease would spread. She theorized that a swine farm network including human movement will identify risk structures not present in animal movement networks alone. Her hypothesis: “Monitoring human movement is just as or more important than monitoring animal movement alone”.
A low average path length can mean that even less connected properties in the network are more vulnerable to disease.
Prezioso observed that there were more trips between farms than expected. Average path length corresponds to the average number of trips (or visits to other properties) between any two properties. The lower the average path length, the more connected the network is. Based on her analysis, the longest path length of the full network is five, with an average path length of 2.202. A low average path length can mean that even less connected properties in the network are more vulnerable to disease.
The diagram below provides a visual of the high connection between networks, where each color represents groups of similar properties based on a mathematical algorithm, called communities. The grey lines represent trips between properties.
Prezioso aims to continue her work over the next couple of years with the goal to develop a predictive analytics model to forecast with reasonable accuracy when and where a disease will break. This would be a very valuable tool for the swine sector in the fight against infectious disease.
