Improving our understanding of the ecology of antimicrobial resistance in food production using Bayesian model and machine learning approach

Lead PIs



Abdo Z (primary mentor), Boucher C, Morley PS, Belk KE

Funding Agency

United States Department of Agriculture National Institute of Food in Agriculture, Agriculture in Food Research Initiative, Education and Literacy Initiative Postdoctoral Fellowship (USDA NIFA AFRI ELI)

Grant Number


Funding Period

2016 – 2017

Lab Personnel

Noelle Noyes
Zaid Abdo
Christina Boucher
Paul Morley
Keith Belk


Antimicrobial resistance (AMR) is a pressing public health concern with ramifications for food production, particularly meat and poultry. Our group has recently adopted a next-generation sequencing, metagenomics-based approach to researching AMR in livestock production. This sequencing technology allows us to access all of the bacterial DNA within a given sample, thus enabling investigation of the complex microbial ecosystem in which AMR exists. However, in order to extract meaningful patterns within such sequence data, advanced statistical methods must be applied. The goal of this proposal is to use advanced statistical methods such as hierarchical Bayesian modeling and machine learning to uncover patterns of association between livestock production strategies (such as antimicrobial use practices) and AMR. To this end, we will identify, optimize, validate and apply existing Bayesian and machine learning tools to three metagenomic datasets generated from studies of AMR in livestock production. Outcomes of these activities include provision of open-source statistical analysis methodologies for use by other agriculture scientists grappling with complex research data; as well as identification of important and actionable drivers of AMR in livestock production systems. These outcomes directly fulfill the program area of food safety by providing evidence-based results that can be used to formulate effective AMR mitigation interventions and policies.