Synthesized Population Databases: A Geospatial Database of US Poultry Farms.

Area of Science:

• Veterinary Epidemiology
• Geospatial Analysis
• Computational Modeling

Background:

• Infectious diseases in farmed animals have significant economic, social, and public health impacts.
• Agent-based models are increasingly used to study animal-borne disease outbreaks and inform control policies.
• A lack of nationwide geospatial data on actual farm locations and characteristics hinders accurate modeling in the U.S.

Purpose of the Study:

• To present a novel method for creating a synthetic geospatial dataset of poultry farms in the U.S.
• To replicate the spatial distribution, farm types, and bird populations of poultry operations.
• To provide essential data for infectious disease outbreak modeling and response planning.

Main Methods:

• Combined county-aggregated poultry farm counts with land use/land cover, transportation, business, and topographic data.
• Developed a simulation approach to generate synthetic farm locations across the conterminous U.S.
• Evaluated the accuracy of the synthetic data against a random placement alternative.

Main Results:

• The novel method successfully generated a synthetic dataset replicating poultry farm spatial distribution and characteristics.
• Simulation results demonstrated the superiority of this method compared to random farm placement.
• The synthetic dataset provides a valuable resource for infectious disease research.

Conclusions:

• The developed method is a viable approach for creating synthetic livestock farm data.
• This technique can be adapted to simulate other livestock types for infectious disease research.
• Accurate geospatial data is critical for effective animal disease surveillance and response planning.