Comprehensive and spatially accurate poultry population demographic data do not currently exist in the United States; however, these data are critically needed to adequately prepare for, and efficiently respond to and manage disease outbreaks. In response to absence of these data, this study developed a national-level poultry population dataset by using a novel combination of remote sensing and probabilistic modelling methodologies. The Farm Location and Agricultural Production Simulator (FLAPS) (Burdett et al., 2015) was used to provide baseline national-scale data depicting the simulated locations and populations of individual poultry operations. Remote sensing methods (identification using aerial imagery) were used to identify actual locations of buildings having the characteristic size and shape of commercial poultry barns. This approach was applied to 594 U.S. counties with > 100,000 birds in 34 states based on the 2012 U.S. Department of Agriculture (USDA), National Agricultural Statistics Service (NASS), Census of Agriculture (CoA). The two methods were integrated in a hybrid approach to develop an automated machine learning process to locate commercial poultry operations and predict the number and type of poultry for each operation across the coterminous United States. Validation illustrated that the hybrid model had higher locational accuracy and more realistic distribution and density patterns when compared to purely simulated data. The resulting national poultry population dataset has significant potential for application in animal disease spread modelling, surveillance, emergency planning and response, economics, and other fields, providing a versatile asset for further agricultural research.

This paper describes the spatiotemporal distribution and risk factors of African Swine Fever (ASF) in Uganda for the period of 2010 through 2023. The study utilized a comprehensive dataset from monthly reports (2010–2023) from District Veterinary Officers (DVOs), the Ministry of Agriculture, Animal Industry and Fisheries (MAAIF), and the Food and Agriculture Organization, Uganda. Using GPS coordinates, ASF cases were mapped using QGIS to show ASF distribution and spread in Uganda. Moran’s I analysis was used to delineate clusters of ASF. A total of 1521 ASF cases were recorded. The data show that cases of ASF were disseminated throughout the country, with more cases of ASF documented in the central region and border districts (hotspots for ASF), and few cases were reported in Acholi, Karamoja, and Lango, Ankole, West Nile, and Kigezi sub-regions. The time series analysis revealed incidences of ASF disease occurring year-round; notable peak cases were observed in some districts, and districts with ≥30,000 pigs reported higher cases of ASF. The Moran’s I (≥1) analysis showed that ASF is either aggregated (p = 0.01), especially in central districts bordering Tanzania and lake shores, or sporadic in occurrence. The disease was present in 66% of the districts, with ASF occurring throughout the year. More cases were aggregated in central and border districts and districts with large pig populations (≥30,000). Sporadic cases were reported in districts bordering the DRC, Sudan, Kenya, the lake shores, Karamoja, Acholi, and Lango sub-regions.