This project would develop a quantitative approach for integration of multiple data sources (e.g. paper icvi, ecvi from various data bases, etc.) into united states animal movement model (usamm)…

The U.S. Department of Agriculture's Animal and Plant Health Inspection Service (APHIS) awarded Colorado State University a $125,000 Cooperative Agreement under the Plant and Animal Disease, Pest Control, and Animal Care program (CFDA 10.025) effective September 1, 2025, through August 31, 2026. This project will develop a quantitative methodology to integrate multiple data sources—including paper Intra- and Extra-Continental Veterinary Information (ICVI/ECVI) records from various databases—into the United States Animal Movement Model (USAMM) predictions while accounting for data source bias. The first year focuses on developing a mathematical and statistical conceptual framework and analytical approach, while the second year will emphasize implementation of the developed methodology.

Linkopings Universitet in Sweden serves as a sub-grantee responsible for developing the conceptual mathematical and statistical approach for integrating multiple data sources into USAMM and proposing an implementation strategy. Linkopings Universitet will deliver a conceptual framework that corrects for bias in data integration and an associated implementation approach, while collaborating with Colorado State University to identify target data types and participate in biweekly project meetings. This collaborative effort leverages established quantitative integration methods from applied statistics and mathematics to address disease surveillance and animal movement tracking capabilities critical to APHIS's agricultural biosecurity mission.

This project would develop a quantitative approach for integration of multiple data sources (e.g. paper icvi, ecvi from various data bases, etc.) into united states animal movement model (usamm) predictions while accounting for data source bias. Methods for the integration of multiple sources of data have been developed in other fields of applied statistics and mathematics and could serve as an initial basis for the integration of data sources into usamm. The first year of this multiyear project will develop a mathematical and statistical conceptual framework and analytical approach to address the problem. The second year will focus on implementing the analytical approach developed in the first year.