Viruses pose a unique challenge to water safety due to their small size, structural diversity, and low infectious doses. While treatment processes rely on a combination of physical removal and disinfection, viral response to these methods is highly variable.

Despite significant public health implications, high-quality disinfection kinetics exist for only a small subset of viruses. This data gap is largely due to the complexities of viral cultivation, which leave many of the most significant human pathogens underrepresented in the literature. Furthermore, as new viruses emerge, there is often a critical lag time before cultivation methods can be established. Surrogate viruses are traditionally used to approximate the behavior of human pathogens; however, we still lack a consistent framework for translating surrogate performance to the human viruses of interest.

This challenge is compounded by the fact that virus reduction is not solely a function of the virus itself, but also of the complex interplay between water composition and treatment parameters. While the field has gained a mechanistic understanding of how factors like pH, organic matter, and disinfectant dose drive inactivation, accurately predicting the response of a novel or emerging pathogen remains a significant hurdle. The Wigginton group is bridging this gap by harnessing the collective understanding of virus structure, biology, and chemistry. We develop integrated frameworks that account for both viral characteristics and water quality variables to precisely estimate inactivation and removal kinetics across diverse treatment scenarios.

Our team has developed predictive models for virus inactivation with UV254 (Rockey 2021) and statistical models for virus inactivation with free chlorine (Chaplin 2024) and coagulation/flocculation/sedimentation (Chaplin 2025). We are now taking a similar approach for other treatment processes, including chloramines and sub-residual ozone. Specifically, we are applying concepts from environmental engineering, chemistry, virology, statistics, and data science to undertake four major research objectives, namely:

1. Systematic Data Synthesis We conduct rigorous literature reviews to identify and extract high-quality datasets. By characterizing this existing data, we pinpoint critical gaps in the viruses and environmental conditions currently represented in the literature.

2. Targeted Experimental Expansion To fill these gaps, we perform systematic experiments that capture a wide range of water quality parameters and viral characteristics. This work expands our understanding of disinfection and removal kinetics, thereby better representing the diversity of human viruses in the environment.

3. Mechanistic & Predictive Modeling We develop computational frameworks that estimate and predict virus inactivation kinetics. These models integrate complex variables, including water chemistry, operational parameters, and the unique biological and structural features of the virus particles.

4. Translation to Public Health & Policy By leveraging our expanded datasets and predictive tools, we provide actionable insights to improve treatment processes, inform evidence-based regulatory frameworks, and proactively predict disinfection rates for emerging viral threats.

The major outputs of this research are tools to accurately characterize the virus disinfection and removal, including our web-based tool for estimating chlorine disinfection and UV disinfection of viruses. This will help propel the field into a new era of disinfection design and implementation.