REVEALING ANTIMICROBIAL RESISTANCE TRENDS IN THE FOOD CHAIN WITH MACHINE LEARNING TOOLS

Our overall long-term goal is to leverage big data to understand the relationship between antimicrobial resistance (AMR) in livestock, AMR in humans, and antimicrobial use (AMU) regulations. This will enable the creation and continuation of effective AM stewardship policies. To achieve this goal, we must first address barriers to transforming AMR surveillance data into actionable insights. Current AMR analysis methods obscure increases or decreases in resistance because data simplification is required to avoid the MIC and multidrug resistance (MDR) analysis barriers described above. Whenever data is reduced to a simpler format with less variability, statistical power is lost. We hypothesize that novel analytic techniques, which do not require data simplification or transformation, will reveal AMR trends that would be missed with the current AMR analysis standards. Thus, these new methods will provide stronger evidence to support antimicrobial stewardship policies. We will test our hypothesis on cattle-associated Escherichia coli AMR data from NARMS, NCBI Pathogen Detection, and the National Animal Health Laboratory Network (NAHLN). Our proposed work will enable surveillance programs to more effectively detect AMR trends and evaluate the merits of AMR mitigation policies.Objective 1: Quantify the impact of AMU policies on AMR by comparing MIC distributions.Objective 1.1: Impute missing MIC data.Objective 1.2: Create antimicrobialclass resistance indicators.Objective 1.3: Model MIC trends over time with Cox PH regression.Objective 1.4: Compare survival analysis models to the Mann-Kendall test for trend in binary resistance prevalence, which is the NARMS standard.Objective 1.5: Evaluate the impact of MIC uncertainty with sensitivity analyses.Objective 2: Characterize and compare MDR patterns across the food chain by applying machine learning tools to phenotypic and genotypic AMR indicators.Objective 2.1: Jointly analyze phenotypic and genotypic AMR indicators to characterize MDR.Objective 2.2: Compare MDR at each step in the food chain.Objective 2.3: Compare the MDR patterns identified with association mining to a standard MDR analytic approach.