A Data-Driven Risk-Based Enterprise for Operational Decision Support
Published in Department of Homeland Security - Center of Excellence on Cross-Border Threat Screening and Supply Chain Defense, 2021-2023 , 2021
Transboundary pest and disease threats (TPDT’s) represent omnipresent threats to U.S. commercial crop and animal production. While many data and information resources are available to contribute to building a complete assessment of the ongoing TPDT risks faced by U.S. agriculture, work is needed to assemble, validate and utilize these resources to improve decisions about how best to identify high-risk shipments before they cross the border. To do this we will develop, document, and validate a proof-of-concept artificial intelligence tool that systematically improves the risk forecasting decisions needed to improve interdiction efforts that safeguard U.S. borders against the natural, accidental, or intentional entry and spread of TPDTs arriving in passenger luggage, commercial imports and mail shipments.
This project seeks to mitigate the risks of TPDT’s by developing data integration and forecasting methods that improve decisions about the presence of possible TPDT’s before they reach our ports of entry. This project will develop and use artificial intelligence algorithms that learn, adapt, and evolve to improve decisions to aimed at detecting suspect imports and, therefore, preventing the introduction of TPDT’s in the U.S.
Valdivia-Granda, W.A. (PI), de Queiroz, A.R. (co-PI), A Data-Driven Risk-Based Enterprise for Operational Decision Support, Funded by Department of Homeland Security Center of Excellence on Cross-Border Threat Screening and Supply Chain Defense, 2021-2023
Analysis Framework - A Data-Driven Risk-Based Enterprise for Operational Decision Support