摘要
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 realtime.Behavioral change is often ignored in epidemic modeling efforts,making these models less useful than they could be.We address this by introducing a novel class of datadriven epidemic models which characterize and accurately estimate behavioral change.Our proposed model allows time-varying transmission to be captured by the level of“alarm”in the population,with alarm specified as a function of the past epidemic trajectory.We investigate the estimability of the population alarm across a wide range of scenarios,applying both parametric functions and non-parametric functions using splines and Gaussian processes.The model is set in the data-augmented Bayesian framework to allow estimation on partially observed epidemic data.The benefit and utility of the proposed approach is illustrated through applications to data from real epidemics.
