Changes in behaviors are often acknowledged as a major source of uncertainty in infectious disease modeling work, and there is an increasing interest and need for simulation models of infectious diseases that couple transmission dynamics with adaptive behaviors. Such models could reflect how policies affect behaviors (in both desirable and undesirable ways) concerning subsequent disease epidemiology. This talk presents our path towards tackling behavior (and its inherent uncertainty) in our modeling work. We begin by introducing our COVID-19 model and analysis and explain how we accounted for some behavioral mechanisms in our stress-test of COVID-19 reopening policies. We then describe our prior efforts in developing and informing an ABM to model seasonal influenza vaccination dynamics whereby individuals are influenced by alters in their social network and use inductive reasoning to update their propensity to vaccinate. We will also present publicly available longitudinal surveys that we have designed and fielded through RAND's American Life Panel for the purpose of informing our models. Finally, we discuss our project that will extend this framework for COVID-19 and collect behavioral data on a representative US sample for the next four years. If you are an infectious disease modeler and are interested in the idea of modeling infectious behaviors, please join this talk, and give us feedback! Your feedback may inform how we design future surveys and could result in exciting collaborations.