Abstract

We propose a generative model and an inference scheme for epidemic processes on dynamic, adaptive contact networks. Network evolution is formulated as a link-Markovian process, which is then coupled to an individual-level stochastic susceptible-infectious-recovered model, to describe the interplay between the dynamics of the disease spread and the contact network underlying the epidemic. A Markov chain Monte Carlo framework is developed for likelihood-based inference from partial epidemic observations, with a novel data augmentation algorithm specifically designed to deal with missing individual recovery times under the dynamic network setting. Through a series of simulation experiments, we demonstrate the validity and flexibility of the model as well as the efficacy and efficiency of the data augmentation inference scheme. The model is also applied to a recent real-world dataset on influenza-like-illness transmission with high-resolution social contact tracking records. Supplementary materials for this article are available online.

摘要

我们提出了一个生成模型和一个用于动态、自适应联系网络上的流行病过程的推理方案。网络进化被表述为一个链接-马尔可夫过程，然后将其与个体水平的随机易感-感染-恢复模型相结合，以描述疾病传播的动态与流行病潜在的联系网络之间的相互作用。马尔可夫链蒙特卡罗框架是为从部分流行病观察中进行基于似然的推断而开发的，具有一种新的数据增强算法，专门用于处理动态网络设置下丢失的个体恢复时间。通过一系列的仿真实验，我们证明了模型的有效性和灵活性以及数据增强推理方案的有效性和效率。该模型还应用于最近的真实世界数据集，该数据集具有高分辨率的社交联系跟踪记录，涉及流感样疾病传播。本文的补充材料可在线获取。