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Likelihood-Based Inference for Partially Observed Epidemics on Dynamic Networks

Citation

Bu, Fan; Aiello, Allison E.; Xu, Jason S.; & Volfovsky, Alexander (Online ahead of print). Likelihood-Based Inference for Partially Observed Epidemics on Dynamic Networks. Journal of the American Statistical Association.

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 for this article are available online.

URL

http://dx.doi.org/10.1080/01621459.2020.1790376

Reference Type

Journal Article

Journal Title

Journal of the American Statistical Association

Author(s)

Bu, Fan
Aiello, Allison E.
Xu, Jason S.
Volfovsky, Alexander

Year Published

Online ahead of print

Reference ID

13099