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. 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 data-driven 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.

翻译：针对许多传染病暴发事件，高风险人群会根据疫情严重程度调整自身行为，导致传播动力学实时变化。然而，行为变化在疫情建模中常被忽视，使得这些模型的实际效用大打折扣。为解决这一问题，我们提出了一类新型数据驱动型疫情模型，能够刻画并准确估计行为变化。该模型通过人群中的"警报"水平来捕获时变传播特征，其中警报被定义为过去疫情轨迹的函数。我们研究了不同场景下人群警报的可估计性，同时应用参数函数及基于样条与高斯过程的非参数函数进行建模。模型基于数据增强贝叶斯框架构建，可在部分观测的疫情数据条件下进行参数估计。通过真实疫情数据的应用案例，验证了所提方法的优势与实用性。