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Volume 31, Number 4—April 2025
Research

Attribution of Salmonella enterica to Food Sources by Using Whole-Genome Sequencing Data

Erica Billig Rose1, Molly K. Steele1Comments to Author , Beth Tolar, James Pettengill, Michael Batz, Michael Bazaco, Berhanu Tameru, Zhaohui Cui, Rebecca L. Lindsey, Mustafa Simmons, Jess Chen, Drew Posny, Heather Carleton, and Beau B. Bruce
Author affiliation: Centers for Disease Control and Prevention Atlanta, Georgia, USA (E.B. Rose, M.K. Steele, B. Tolar, Z. Cui, R.L. Lindsey, J. Chen, H. Carleton, B.B. Bruce); US Food and Drug Administration, College Park, Maryland, USA (J. Pettengill, M. Batz, M. Bazaco); US Department of Agriculture, Washington, DC, USA (B. Tameru, M. Simmons, D. Posny)

Main Article

Figure 3

Predictions from a random forest model of sources of US human illnesses among patients without reported history of international travel who had whole-genome sequenced Salmonella isolates reported to the Foodborne Diseases Active Surveillance Network, 2014–2017. A) Predictions of sources of human illnesses among all patients (N = 6,470). B) Predictions of food category sources of human illnesses among patients renormalized among isolates with a >0.50 probability of attribution to a single food category (n = 3,686).

Figure 3. Predictions from a random forest model of sources of US human illnesses among patients without reported history of international travel who had whole-genome sequenced Salmonella isolates reported to the Foodborne Diseases Active Surveillance Network, 2014–2017. A) Predictions of sources of human illnesses among all patients (N = 6,470). B) Predictions of food category sources of human illnesses among patients renormalized among isolates with a >0.50 probability of attribution to a single food category (n = 3,686).

Main Article

1These first authors contributed equally to this article.

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