Waste stabilization ponds (WSPs) are one of the most prevalent types of domestic wastewater treatment technologies employed worldwide, and global stressors such as urbanization, population growth, climate change, and water scarcity have increased the demand for reusing treated wastewater. The safe reuse of treated wastewater in agriculture can ease water scarcity, aid in food production, and reduce environmental degradation from the discharge of wastewater effluent to surface waters. The ability to predict virus concentrations in wastewater effluent is an important criterion for determining whether wastewater is suitable for discharge to the environment or for reuse in agriculture. However, many uncertainties remain about virus removal efficiency in WSPs and there is currently no mechanistic or empirical model that reliably predicts virus removal in WSPs. The overall objective of this thesis research was to model the extent of virus removal in individual waste stabilization ponds to support the reuse of wastewater. A literature review was used to create a database of estimated apparent virus removal rate coefficients (Kv,app) in three different WSP types (anaerobic, facultative, and maturation ponds). The database consisted of 249 paired influent and effluent concentrations of enteric viruses or bacteriophages from 44 unique WSP systems, comprised of 112 individual WSPs from 19 different countries. Apparent virus removal rate coefficients (Kv,app) were calculated for each individual WSP using the following three mathematical models from reactor theory: complete mix, plug flow, and dispersed flow. Pearson’s correlation analysis was used to determine correlations between Kv,app values and the following design, operational, and environmental parameters: solar radiation, air temperature, pond depth, hydraulic retention time (HRT), and virus loading rates. The median Kv,app values were greater for anaerobic ponds than for facultative and maturation ponds; however, Kv,app values in facultative and maturation ponds had more significant correlations with design, operational, and environmental parameters. Additionally, Kv,app values appear to be significantly different for various types of enteric viruses and bacteriophages. Alternative multiple linear regression equations were developed to predict Kv,app values using the design, operational, and environmental parameters as explanatory variables. Analysis of variance (ANOVA) tests were used to select the most appropriate multiple linear regression equations with the least amount of explanatory variables. The most appropriate plug flow and dispersed flow multiple linear regression equations for predicting Kv,app values included air temperature and HRT as explanatory variables. The results indicate that the plug flow regression equation was able to better predict Kv,app values (R2 = 0.38) than the dispersed flow regression equation (R2 = 0.24) in facultative and maturation ponds based on the dataset. However, both the dispersed flow and plug flow models had R2 values of approximately 0.84 when they were used to predict effluent virus concentrations in facultative and maturation ponds based on the dataset. According to this research, the plug flow regression equation is recommended for predicting apparent virus removal rate coefficients in WSPs. However, a multi-model approach that utilizes both the plug flow and dispersed flow models may yield a more robust mathematical model that can improve WSP design, reliably predict virus removal in WSPs, and ultimately be used to support wastewater reuse.