Date of Award
Spring 2026
Document Type
Open Access Thesis
Department
Environmental Health Sciences
First Advisor
Robert Sean Norman
Abstract
This thesis presents three interconnected studies advancing the methodology and application of environmental microbial surveillance, spanning freshwater resistome characterization, automated molecular analysis tool development, and multi-pathogen wastewater-based epidemiology. Chapter 2 investigated the impact of rainfall on antibiotic resistance gene (ARG) dynamics in Lake Katherine, a suburban freshwater lake in Columbia, South Carolina, using metagenomic sequencing and statistical modeling over one year of sampling. Six of nine ARG classes with sufficient data showed significant responses to two-day cumulative rainfall, with Bacitracin, Aminoglycoside, and Unclassified resistance genes increasing after precipitation while Tetracycline, Multidrug, and Peptide resistance genes declined. These gene-class-specific responses found that rainfall reshapes the environmental resistome through the complex interactions of runoff mobilization, dilution, and microbial community turnover rather than through uniform enrichment. Chapter 3 presents the development and validation of the Systematic Thresholding Utility for Reproducible DdPCR (STURD), an open-source Python pipeline for automated droplet digital PCR analysis designed for wastewater-based epidemiology. The pipeline implements sigma-based automated thresholding with kernel density estimation mode alignment, three-category droplet classification excluding ambiguous rain droplets, Poisson concentration estimation with Clopper-Pearson confidence intervals, and integrated wastewater backcalculations and quality control metrics. Validation against expert manual classification and Bio-Rad QuantaSoft across 2,127 matched observations from five respiratory virus targets demonstrated 96.2% detection sensitivity and a log-scale Pearson correlation of r = 0.948, outperforming QuantaSoft automated analysis (62% sensitivity, r = 0.362). Chapter 4 applied ecological community analysis frameworks to characterize the dynamics of five respiratory viruses across four South Carolina wastewater treatment plants over 15 months. Co-occurrence analysis identified a respiratory virus guild consisting of influenza A, influenza B, and RSV, while portfolio effect analysis demonstrated that multi-target panels provide 6-27% lower variability than single-target monitoring. Community state classification found spatially consistent, seasonally predictable dynamics driven mainly by the balance between SARS- CoV-2 and influenza A relative abundances. Collectively, these studies demonstrate that integrating rigorous molecular quantification, purpose-built computational tools, and ecologically informed analytical frameworks substantially advances the capacity of environmental surveillance to generate actionable public health information. Extra supplemental files as well as this thesis have been made available in ProQuest Dissertations & Theses.
Rights
© 2026, Jack Andrew Roddey
Recommended Citation
Roddey, J. A.(2026). Novel Computational and Ecological Approaches For Environmental Microbial Surveillance in South Carolina Freshwater and Wastewater Systems. (Master's thesis). Retrieved from https://scholarcommons.sc.edu/etd/8804
Included in
Environmental Indicators and Impact Assessment Commons, Environmental Monitoring Commons, Epidemiology Commons, Fresh Water Studies Commons, Water Resource Management Commons