Bayesian Modeling of Dynamic Behavioral Change During an Epidemic
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For many infectious disease outbreaks, the at-risk population changes their behavior in response to the outbreak severity, causing the transmission dynamics to change in real time. Various approaches to incorporating behavioral change in epidemic models have been proposed, but work assessing the statistical properties of these models in relation to real data 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 splines and Gaussian processes. The benefit and utility of the proposed approach is illustrated through an application to COVID-19 data from New York City.
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