Related Experiment Video
Updated: Jun 7, 2025
Noninvasive, In-pen Approach Test for Laboratory-housed Pigs
Published on: June 5, 2019
A network evaluation of human and animal movement data across multiple swine farm systems in North America
Tara Prezioso1, Alicia Boakes2, Jeff Wrathall2
1University of Illinois Urbana-Champaign Department of Pathobiology, USA.
Introduction:
The U.S. swine industry is vulnerable to the rapid spread of disease due to systemic structural issues. While animal movement networks are used to identify disease spread risks and design response plans, human movement between farms were rarely accounted for. Human movements, when integrated with animal movement models, create a different, more inclusive, and accurate network structure when compared to animal movements alone.
Methods:
One year of propriety farm visit data was analyzed and consisted of anonymized property IDs, location, and user/truck IDs, along with visit dates, property, vehicle, and entry types from three swine management companies. A static directed network was created using the igraph package in R for all movements, with separate sub-networks for each entry type (animal, human, and subsets of vehicle types). Network statistics for each sub-network were compared.
Results:
The full network included 455 properties, 11 property types, 9 vehicle types, 12 entry types, and 320001 edges (trips between properties). The longest path length was 10 in the animal movement network but decreased to 5 for the full and human movement network, while the average path length decreased from 3.2 to 2.2. Edge density increased from 0.03 to 0.09 for the human network and 0.1 for the full network. For all network properties examined, the full and human movement networks demonstrated higher connectivity than the animal network. A heavy right skew in the degree distributions indicates a 'hub' structure (scale-free-like network) and the shorter path lengths indicates a small-world network topology.
Discussion:
The full network is very well connected, more so than expected based on animal movement alone. Hubs may indicate points of disease susceptibility and 'super-spreader' properties. The high connectivity shows that swine farm networks may be more susceptible to spread of an introduced disease than expected from previous analyses.
Conclusions:
Monitoring human, as well as animal movement, provides for a more complete and accurate understanding of swine farm biosecurity risks.
