For many infectious disease outbreaks, the at-risk population changes their behavior in response to the outbreak severity, changing the transmission dynamics to change in real-time. Various approaches to behavioral change modeling have been proposed, but work assessing the statistical properties of these models is limited. We propose a model formulation where time-varying transmission is captured by the level of “alarm” in the population and specified as a function of the past epidemic trajectory. The model is set in a data-augmented Bayesian framework as epidemic data are often only partially observed, and we can utilize prior information to help with parameter identifiability. We investigate the estimability of the population alarm across a wide range of scenarios, using both parametric functions and non-parametric Gaussian process and splines. The benefit and utility of the proposed approach is illustrated through an application to COVID-19 data from various cities.