Vehicle networks identify distinct swine super-spreaders missed by animal movement data alone

The preprint by Machado and colleagues mapped 11 contact types among production farms, separating direct pig movements, multiple vehicle classes, and proximity. Feed truck networks alone reached near-complete connectivity while revealing consistent overlap in the top 50 high-risk farms across vehicle types, up to 89 percent. Finisher farms emerged as key bridges; sow farms were frequent recipients via feed routes, accounting for 8.7 percent of those edges. Single-network reliance on animal shipments would have missed most of these bridges.

Standard livestock surveillance still privileges live-animal tracing. The data demonstrate that this approach captures at most 4-8 percent of the farms flagged by vehicle networks, leaving feed and trailer routes as undetected amplifiers. This matters for pathogens such as PRRS and potential African swine fever incursions, where fomite transmission via trucks can precede clinical detection by weeks. The study therefore supplies a concrete ranking variable for targeted biosecurity audits.

Integration of anonymized vehicle GPS with existing movement databases could shift surveillance from reactive culling to preemptive node removal. Pilot programs in high-density swine states would test whether multi-network scores reduce time to first detection below current baselines within 18 months of deployment.