Prospective modeling and estimating the epidemiologically informative match rate within large foodborne pathogen genomic databases

• Published in: BMC research notes Vol. 17; no. 1; pp. 191 - 6
• Main Authors: Yin, Lanlan, Pettengill, James B.
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 09.07.2024; BioMed Central; BMC
• Subjects: Analysis; Biotechnology; Care and treatment; Clinical isolates; Cronobacter; Databases, Genetic; Diagnosis; DNA sequencing; E coli; Epidemiology; Escherichia coli; Escherichia coli - genetics; Escherichia coli - isolation & purification; Food; Food Microbiology; Food poisoning; Food safety; Food sources; Foodborne Diseases - epidemiology; Foodborne Diseases - microbiology; Foodborne pathogen; Foodborne pathogens; Genetic diversity; Genomics; Humans; Hypotheses; Listeria monocytogenes; Listeria monocytogenes - genetics; Listeria monocytogenes - isolation & purification; Metadata; Nucleotide sequencing; Pathogens; Prediction models; Prospective Studies; Public health; Regression analysis; Research Note; Risk factors; Safety and security measures; Salmonella; Salmonella - genetics; Salmonella - isolation & purification; Surveillance; Variables; United States; Surveillance; Foodborne pathogen; Genomics
• ISSN: 1756-0500; 1756-0500
• DOI: 10.1186/s13104-024-06847-z

Much has been written about the utility of genomic databases to public health. Within food safety these databases contain data from two types of isolates-those from patients (i.e., clinical) and those from non-clinical sources (e.g., a food manufacturing environment). A genetic match between isolates from these sources represents a signal of interest. We investigate the match rate within three large genomic databases (Listeria monocytogenes, Escherichia coli, and Salmonella) and the smaller Cronobacter database; the databases are part of the Pathogen Detection project at NCBI (National Center for Biotechnology Information). Currently, the match rate of clinical isolates to non-clinical isolates is 33% for L. monocytogenes, 46% for Salmonella, and 7% for E. coli. These match rates are associated with several database features including the diversity of the organism, the database size, and the proportion of non-clinical BioSamples. Modeling match rate via logistic regression showed relatively good performance. Our prediction model illustrates the importance of populating databases with non-clinical isolates to better identify a match for clinical samples. Such information should help public health officials prioritize surveillance strategies and show the critical need to populate fledgling databases (e.g., Cronobacter sakazakii).