2022 Early Hearing Detection & Intervention Virtual Conference

March 13 - 15, 2022

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5/21/2019 | 9:15 AM - 9:30 AM | MODELING AND PREDICTING THE OCCURRENCES OF ANTIBIOTIC RESISTANCE GENES IN US RIVERS AND STREAMS | 151 ABC

MODELING AND PREDICTING THE OCCURRENCES OF ANTIBIOTIC RESISTANCE GENES IN US RIVERS AND STREAMS

Antimicrobial resistance (AMR) of pathogens is a critical threat to human and animal health. Human activities enhance the spread of AMR and AMR can be detected in streams by PCR targeting antibiotic resistance genes (ARGs). Understanding anthropogenic drivers of this spread is critical to develop effective monitoring and mitigation. Here, we identified potential ecological drivers to model and predict the probability of occurrence of four ARGs (sul1, tetW, blaTEM, and KPC), a gene associated with AMR mobilization (intI1), and two potential pathogenic indicators (E. coli and enterococci). We paired gene occurrences from ~2,000 stream samples across the US (USEPA NRSA) with several watershed attributes, including urbanization and agriculture (StreamCat dataset). We used random forests to model gene occurrences in response to watershed land use intensity. The models correctly predicted ARGs at 70%-82% of sites but varied in their ability to balance type I and II errors. We then predicted ARG occurrences at 1.1 million stream reaches. The resulting maps reflect the relative importance of urbanization, agriculture, or other landscape stressors on the occurrence of each gene and can indicate where focused surveillance and mitigation of AMR may be needed.

Disease
Microbial
Distribution

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Presenters/Authors

Ryan Hill (), US EPA, Pacific Ecological Systems Division, Corvallis, OR, hill.ryan@epa.gov;