Geographic Distributions of Top Beef Salmonella Serovars in the U.S.
- 1USDA Agricultural Research Service
Abstract
We live in an era where advances in microbial detection, DNA sequencing technology, and the availability of big data have allowed inroads to the field of predictive microbiology. The tools presently in use, as well as those on the horizon, provide an opportunity to examine emerging infectious diseases in ways previously not possible. We analysed publicly available data to investigate where and why Salmonella is detected on raw beef products. In addition to analysing beef-related serovars across space and time, we utilised a machine-learning approach (Species Distribution Modelling) to identify areas and predictors of environmental suitability for the most frequent Salmonella serovars found in beef. Our results demonstrate that some serovars, such as Dublin, Montevideo, and Muenchen, exhibit strong regional prevalence while others, notably Anatum, are found broadly throughout the country. The Species Distribution Models show concurrence with these regional trends. Predictors that contributed 10% or more to multiple serovar models included ecoregions (7/15 serovars), avian flyways (8/15), the number of local cattle operations (3/15), and other environmental variables. Our work demonstrates that certain Salmonella serovars commonly associated with beef show regional trends that can be predicted.
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BibTeX
@misc{katz2026geographic,
title = {{Geographic Distributions of Top Beef Salmonella Serovars in the U.S.}},
author = {T. S. Katz and T. D. Mortimer and A. T. Siceloff and N. W. Shariat and D. M. Harhay and J. M. Blackstock and T. L. Wheeler},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.17.26363322},
url = {https://www.medrxiv.org/content/10.64898/2026.09.17.26363322v1},
note = {CC0},
}
RIS
TY - UNPB
TI - Geographic Distributions of Top Beef Salmonella Serovars in the U.S.
AU - T. S. Katz
AU - T. D. Mortimer
AU - A. T. Siceloff
AU - N. W. Shariat
AU - D. M. Harhay
AU - J. M. Blackstock
AU - T. L. Wheeler
PY - 2026
DA - 2026/09/17
PB - medRxiv
DO - 10.64898/2026.09.17.26363322
UR - https://www.medrxiv.org/content/10.64898/2026.09.17.26363322v1
AB - We live in an era where advances in microbial detection, DNA sequencing technology, and the availability of big data have allowed inroads to the field of predictive microbiology. The tools presently in use, as well as those on the horizon, provide an opportunity to examine emerging infectious diseases in ways previously not possible. We analysed publicly available data to investigate where and why Salmonella is detected on raw beef products. In addition to analysing beef-related serovars across space and time, we utilised a machine-learning approach (Species Distribution Modelling) to identify areas and predictors of environmental suitability for the most frequent Salmonella serovars found in beef. Our results demonstrate that some serovars, such as Dublin, Montevideo, and Muenchen, exhibit strong regional prevalence while others, notably Anatum, are found broadly throughout the country. The Species Distribution Models show concurrence with these regional trends. Predictors that contributed 10% or more to multiple serovar models included ecoregions (7/15 serovars), avian flyways (8/15), the number of local cattle operations (3/15), and other environmental variables. Our work demonstrates that certain Salmonella serovars commonly associated with beef show regional trends that can be predicted.
ER -