In the field of public health and epidemiology, creating an epidemic model that accurately reflects real world contact networks has proven to be a complicated task. Contact networks model how individuals in a population come into contact and interact with other individuals. The implementation of adaptive networks in an epidemic model is a relatively new development. This project aims to use adaptive networks to model the spread of norovirus in a population. I will use computer software, specifically R, to create models and run simulations of a norovirus epidemic. Statistical analysis of the models and simulation results will reveal important details about norovirus transmission and spread, which is crucial in infectious disease control.