论文标题
传染病传播建模中环境随机性的近似扩散过程
An approximate diffusion process for environmental stochasticity in infectious disease transmission modelling
论文作者
论文摘要
建模传染病的传播动力学是一项复杂的任务。不仅很难准确地对传播的固有非平稳性和异质性进行建模，而且几乎不可能从机械上描述外部环境因素的变化，包括公共行为和季节性波动。捕获环境随机性的一种优雅的方法是将感染的力作为随机过程建模。 However, inference in this context requires solving a computationally expensive ``missing data" problem, using data-augmentation techniques. We propose to model the time-varying transmission-potential as an approximate diffusion process using a path-wise series expansion of Brownian motion. This approximation replaces the ``missing data" imputation step with the inference of the expansion coefficients: a simpler and computationally cheaper task.我们通过两个例子说明了这种方法的优点：使用规范的SIR模型对流感进行建模，以及使用多类型SEIR模型的Covid-19大流行的建模。
Modelling the transmission dynamics of an infectious disease is a complex task. Not only it is difficult to accurately model the inherent non-stationarity and heterogeneity of transmission, but it is nearly impossible to describe, mechanistically, changes in extrinsic environmental factors including public behaviour and seasonal fluctuations. An elegant approach to capturing environmental stochasticity is to model the force of infection as a stochastic process. However, inference in this context requires solving a computationally expensive ``missing data" problem, using data-augmentation techniques. We propose to model the time-varying transmission-potential as an approximate diffusion process using a path-wise series expansion of Brownian motion. This approximation replaces the ``missing data" imputation step with the inference of the expansion coefficients: a simpler and computationally cheaper task. We illustrate the merit of this approach through two examples: modelling influenza using a canonical SIR model, and the modelling of COVID-19 pandemic using a multi-type SEIR model.
