Likelihood-based Inference for Partially Observed Epidemics on Dynamic Networks release_hqmitl6atfactarvo22gat4fwq

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

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abstracts [{'sha1': '85ebeae43dadad14b04d91014d401f3e75e237cb', 'content': 'We propose a generative model and an inference scheme for epidemic processes\non dynamic, adaptive contact networks. Network evolution is formulated as a\nlink-Markovian process, which is then coupled to an individual-level stochastic\nSIR model, in order to describe the interplay between epidemic dynamics on a\nnetwork and network link changes. A Markov chain Monte Carlo framework is\ndeveloped for likelihood-based inference from partial epidemic observations,\nwith a novel data augmentation algorithm specifically designed to deal with\nmissing individual recovery times under the dynamic network setting. Through a\nseries of simulation experiments, we demonstrate the validity and flexibility\nof the model as well as the efficacy and efficiency of the data augmentation\ninference scheme. The model is also applied to a recent real-world dataset on\ninfluenza-like-illness transmission with high-resolution social contact\ntracking records.', 'mimetype': 'text/plain', 'lang': 'en'}]
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release_date 2020-04-05
release_stage submitted
release_type article
release_year 2020
revision aab9d1ea-39cd-44a5-9568-5f8f3b36d60f
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title Likelihood-based Inference for Partially Observed Epidemics on Dynamic Networks
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arxiv.base_id 1910.04221
arxiv.categories ['stat.ME', 'physics.soc-ph', 'q-bio.PE']