Likelihood-Based Inference for Partially Observed Epidemics on Dynamic Networks

• 成果类型: Article
• 署名作者: Bu, Fan; Aiello, Allison E.; Xu, Jason; Volfovsky, Alexander
• 署名单位: Duke University; University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2020.1790376
• 发表日期: 2022
• 页码: 510-526
• 关键词: stochastic compartmental-models; contact patterns; transmission; time; SPREAD; measles; populations; influenza; EVOLUTION; diseases

摘要

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.