Data-driven prediction and origin identification of epidemics in population networks

Data-driven prediction and origin identification of epidemics in population networks

Effective intervention strategies for epidemics rely on the identification of their origin and on the robustness of the predictions made by network disease models. We introduce a Bayesian uncertainty quantification framework to infer model parameters for a disease spreading on a network of communiti...

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Bibliographic Details
Main Authors: Karen Larson, Georgios Arampatzis, Clark Bowman, Zhizhong Chen, Panagiotis Hadjidoukas, Costas Papadimitriou, Petros Koumoutsakos, Anastasios Matzavinos
Format: Article
Language:English
Published: The Royal Society 2021-01-01
Series:Royal Society Open Science
Subjects:
uncertainty quantification
transitional markov chain monte carlo
inverse problem
epidemic modelling
bayesian model selection
sir model
Online Access:https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.200531

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https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.200531

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