UC Davis

Viruses in wastewater present public-health challenges as well as public-health opportunities. I consider both herein. I begin with a systematic literature review of nearly 300 studies, published from 2000 to 2018, that document applications of flow cytometry (FCM) to ensure microbial water quality and hence facilitate safe and effective water treatment, distribution, and reuse. I find that while there is a large body of evidence supporting widespread adoption of FCM as a routine method for microbial water-quality assessment, key knowledge gaps impede the technique from realizing its full potential. One of these gaps is robust protocols for FCM-based analysis of waterborne viruses. In this dissertation, I hypothesize that a fractional factorial experimental design is a better alternative to the “pipeline” strategy commonly followed for FCM protocol optimization. I then demonstrate my approach, using a fractional factorial experimental design to optimize staining of the bacteriophage T4 prior to FCM analysis. My results yield a specific protocol for reliably identifying and quantifying T4 bacteriophage through FCM.

I also explain why manual gating of FCM data using a series of two-dimensional plots—the typical approach to FCM data analysis—is problematic, especially with respect to applications of FCM to facilitate advanced water treatment and reuse. I suggest that algorithmic clustering approaches could expedite and improve FCM data analysis, and could even help position FCM as a technique for real-time microbial water-quality monitoring. I test this theory by generating FCM data from two solutions: (i) a mixed-target solution containing a variety of environmentally relevant viral surrogates, and (ii) an environmental-spike solution comprising T4 bacteriophage in a wastewater matrix. I first analyze these data through manual gating, and then compare results to results obtained through algorithmic clustering: specifically, by coupling the OPTICS ordering algorithm with either manual or automated identification of clusters from the OPTICS-ordered data. I demonstrate that OPTICS-assisted clustering can in some cases work as well or better than manual gating of FCM data—and is certainly far faster and less labor-intensive. OPTICS-assisted clustering can also point to features in FCM data that are difficult to detect through manual gating alone. However, I also find that more needs to be done to position OPTICS as a reliable tool for automated, objective analysis of FCM data from environmental samples, especially data generated from challenging biological targets like viruses in challenging matrices like wastewater.

I explore wastewater-borne viruses as a public-health opportunity through the lens of the COVID-19 pandemic. Wastewater-based epidemiology (WBE) quickly became recognized as a useful complement to clinical testing following the pandemic’s onset. However, little is known about sub-community relationships between wastewater and clinical data. I present a novel framework for probabilistically aligning wastewater and clinical data with high spatial granularity. I use this framework to uncover clear sub-regional (i.e., sub-city) and building/neighborhood-scale correlations between wastewater and clinical data collected through the Healthy Davis Together (HDT) pandemic-response initiative in Davis, CA. In addition, I hypothesize that multiple imputation (using an expectation maximization-Markov chain Monte Carlo (MCMC) approach) of non-detects in wastewater qPCR data is less likely to bias results than more commonly used non-detect handling methods (e.g., censoring or single imputation). I use the HDT data to test this hypothesis. I find that while results obtained using different non-detect handling methods are similar, they are not the same—indicating the importance of specifying non-detect handling method in WBE studies. I also find that the EM-MCMC method yields somewhat better agreement between clinical and wastewater data than do the other non-detect handling methods examined. Refinements to the algorithm, tuning parameters, and variable groupings used in this dissertation could further recommend the EM-MCMC method for wastewater-data analysis in the future.

I conclude the dissertation with a discussion of lessons learned from my experience helping launch, grow, and manage the HDT WBE program. Conducting WBE requires significant investments of time, money, labor, and expertise. Given that much information gleaned from wastewater is not directly actionable, and/or duplicates information from other sources, it is prudent to consider whether these investments are worth it. I present seven recommendations for end users seeking to incorporate WBE into COVID-19 response: (1) avoid redundancy between clinical testing and WBE; (2) emphasize statistical thinking, data analysis, and data management; (3) define action thresholds; (4) monitor fewer sites more frequently; (5) build on existing infrastructure and programs for wastewater collection and analysis; (6) be prepared to adapt as the pandemic evolves; and (7) keep an eye on the future, including by proactively searching for emerging variants of concern.