Likelihood-Based Inference for Partially Observed Epidemics on Dynamic Networks
Likelihood-Based Inference for Partially Observed Epidemics on Dynamic Networks
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DOI：
10.1080/01621459.2020.1790376
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发表时间：
2020-08-18
影响因子：
3.7
通讯作者：
Volfovsky, Alexander
中科院分区：
文献类型：
--
作者：
Bu, Fan;Aiello, Allison E.;Volfovsky, Alexander
我们提出了一种基于动态自适应接触网络的流行病过程的生成模型和推理方案。网络进化被表述为一个链接-马尔可夫过程，然后与个体水平的随机易感-感染-恢复模型相耦合，以描述疾病传播动态与流行病背后的接触网络之间的相互作用。根据部分疫情观测数据，建立了基于似然推断的马尔可夫链蒙特卡罗框架，并设计了一种新的数据增强算法来处理动态网络设置下缺失的个体恢复时间。通过一系列的仿真实验，验证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高效性。该模型还应用于最近一个具有高分辨率社会接触追踪记录的流感样疾病传播的真实数据集。这篇文章可以在网上找到。
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.for this article are available online.