This is a recent publication from the MSHMP team regarding transport patterns within a Midwestern swine system. The full publication is available on the journal’s website.
This project had the following objectives:
Continue reading “Disentangling transport movement patterns of swine trucks”
- characterizing vehicle network before and during the COVID-19 pandemic,
- Understanding vehicle movement: consistency of vehicle movements over time), and time spent at each site
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, we are sharing a report regarding the use of swine shipment data for effective disease surveillance by Drs. Amy Kinsley, Meggan Craft, Andres Perez, and Kim VanderWaal.
- A production system’s vulnerability to disease spread can be greatly reduced when selectively identifying a subset of farms as disease control targets.
What was done:
In this study, we used a network approach to describe annual movement patterns between swine farms in three multi-site production systems (1,063 farms) in the United States.
- degree: number of farms to which a farm ships or receives pigs
- farm’s individual contribution to disease spread via its movements
- mean infection potential (MIP), which measures potential incoming and outgoing infection chains
What was found:
Removing farms based on their mean infection potential substantially reduced the potential for transmission of an infectious pathogen through the network when compared to removing farms at random, as shown by a reduction in the magnitude of R0 attributable to contact pattern.
The MIP was more efficient at identifying targets for disease control compared to degree and farm’s contribution to disease spread.
What does this mean?
By targeting disease interventions towards farms based on their mean infection potential, we can substantially reduce the potential for transmission of an infectious pathogen in the contact network, and performed consistently well across production systems.
Fine-scale temporal movement data is important and is necessary for in-depth understanding of the contact structure in developing more efficient disease