Model-guided suggestions for targeted surveillance based on cattle shipments in the U.S.

摘要: Risk-based sampling is an essential component of livestock health surveillance because it targets resources towards sub-populations with a higher risk infection. in U.S. limited the locations high-risk herds are often unknown and data to identify based on shipments unavailable. In this study, we use novel, data-driven network model for cattle (the Animal Movement Model, USAMM) provide suggestions imported into from Mexico. We describe volume where analyze their predicted shipment patterns counties that most likely receive cattle. Our results suggest sent relatively few counties. Surveillance at 10 sample 22-34% while 50 43%-61% These findings assumption USAMM accurately describes not tracked separately remainder herd. However, two additional datasets - Interstate Certificates Veterinary Inspection brand inspection ensure characteristics potential post-import do change annual scale dependent dataset informing our analyses. Overall, these highlight utility inform targeted strategies when complete information