论文标题
流行病期间动态行为变化的贝叶斯建模
Bayesian Modeling of Dynamic Behavioral Change During an Epidemic
论文作者
论文摘要
对于许多传染病爆发，高危人群会因爆发爆发的严重程度而改变其行为，从而导致传播动态实时变化。在流行性建模工作中，通常会忽略行为改变，从而使这些模型的用处不及可能。我们通过引入一类新的数据驱动流行模型来解决这一问题，该模型表征并准确估计行为改变。我们提出的模型允许人口中的“警报”级别捕获时变的传输，并指定警报是过去流行轨迹的函数。我们研究了在广泛的场景中的人口警报的估计性，同时使用花纹和高斯过程应用参数函数和非参数函数。该模型是在数据增强的贝叶斯框架中设置的，以允许对部分观察到的流行数据进行估计。提出方法的好处和实用性通过应用于实际流行病的数据的应用来说明。
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.
