Computational Models for Resistome Risk Assessment and Environmental Surveillance

Abstract

Antibiotic resistance (AR) is a major global threat to human health and economic stability. Without effective intervention, AR is projected to cause substantial loss of life and severe global economic burden. Addressing this challenge requires coordinated national and international efforts that consider all pathways through which antibiotic resistance can emerge and spread. Many public health and regulatory organizations advocate for a One Health approach, which integrates human, animal, and environmental health perspectives. Core components of this approach include the monitoring, risk assessment, and mitigation of antibiotic resistance. This dissertation addresses all three of these components through the development and application of computational methods for analyzing antibiotic resistance genes (ARGs) in environmental metagenomic data. In Chapter 2, I presented a risk assessment framework designed to evaluate the potential health risk associated with ARGs at a given location. Using metagenomic sequencing data, this framework computes a resistome risk score that quantifies the level of ARG contamination. The method was shown to be robust across different genome assembly strategies and varying sequencing depths. In Chapter 3, I applied this framework to 1,326 metagenomic samples collected from 12 distinct environmental types. This large-scale analysis disentangles biological (e.g., ARG relative abundance), ecological (e.g., taxonomic diversity), and technical (e.g., sequencing coverage) factors that influence resistome risk scores. The results demonstrate that risk scores are significantly affected by taxonomic diversity and are strongly correlated with anthropogenic markers. In Chapter 4, I introduced ARGfore, a forecasting model designed to predict future ARG abundance based on longitudinal surveillance data. Forecasting ARG trends enables earlier detection of emerging resistance risks and supports proactive mitigation strategies. Finally, in Chapter 5, I described WWTPredictor, a regression-based model that predicts the abundance of metagenomic features—specifically ARGs and microbial taxa—released into the environment from WWTPs based on incoming wastewater data. This model provides a quantitative framework for anticipating environmental discharge risks and supports data-driven decision-making in public health and environmental management.