Issue
USDA’s Plant Protection and Quarantine (PPQ) division is accountable for reducing the pest threat to U.S. agriculture in a way that does not unduly restrict commerce. To accomplish this, PPQ is moving to better methods for determining not only what to inspect, but how to inspect it. Fundamentally, the issue is that USDA and DHS have limited resources available to inspect the vast amounts of people and cargo that enter the U.S. through ports of entry. The problem is to allocate these resources to conduct inspections as efficiently as possible to minimize the damages caused by pests imported into the U.S.
Objectives
(1) Assess the performance of existing random sampling protocols using historical data from inspections. (2) Formulate a mathematical decision support model that optimizes inspections at ports of entry based on pest arrival data and estimated economic damages caused by pests that enter the U.S. (3) Demonstrate how the model can be employed to efficiently allocate inspection resources and sample arriving units using data from real-world case studies.
Value Propositions
The project produced a mathematical decision support framework that can be used to optimally allocate scarce inspection resources and sample units that arrive at U.S. ports of entry. AQIM published a journal paper in the magazine titled “Risk Analysis” and provided their findings used to inform our government partners on a newly adapted recommendation to the AQIM inspection process. Mathematical tools, software, and results produced in this project will help the USDA and DHS minimize the damages caused by pests imported into the U.S. This allows DHS and USDA to more accurately estimate pest arrival rates through the random sampling they conduct in the AQIM program.
| Project Lead | The University of Texas at Austin |
| Research Team | PI: Benjamin D. Leibowicz, PhD University of Texas – Austin Co-PI: John J. Hasenbein, PhD University of Texas – Austin |
| Budget | $250,000 |
| Duration | Dec. 2022 – Dec. 2024 |