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Bayesian uncertainty quantification for transmissibility of influenza, norovirus and Ebola using information geometry

Title: Bayesian uncertainty quantification for transmissibility of influenza, norovirus and Ebola using information geometry
Authors: House, T
Ford, A
Lan, S
Bilson, S
Buckingham-Jeffery, E
Girolami, M
Item Type: Journal Article
Abstract: Infectious diseases exert a large and in many contexts growing burden on human health, but violate most of the assumptions of classical epidemiological statistics and hence require a mathematically sophisticated approach. Viral shedding data are collected during human studies—either where volunteers are infected with a disease or where existing cases are recruited—in which the levels of live virus produced over time are measured. These have traditionally been difficult to analyse due to strong, complex correlations between parameters. Here, we show how a Bayesian approach to the inverse problem together with modern Markov chain Monte Carlo algorithms based on information geometry can overcome these difficulties and yield insights into the disease dynamics of two of the most prevalent human pathogens—influenza and norovirus—as well as Ebola virus disease.
Issue Date: 1-Aug-2016
Date of Acceptance: 25-Jul-2016
URI: http://hdl.handle.net/10044/1/66125
DOI: https://dx.doi.org/10.1098/rsif.2016.0279
ISSN: 1742-5662
Publisher: Royal Society, The
Journal / Book Title: Journal of the Royal Society Interface
Volume: 13
Issue: 121
Copyright Statement: © 2016 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
Keywords: Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
shedding
Markov chain Monte Carlo
compartmental model
MONTE-CARLO METHODS
INFECTIOUS-DISEASE
GROWTH-RATE
IMPACT
EPIDEMICS
CLOSURE
VIRUS
MCMC
Bayes Theorem
Caliciviridae Infections
Ebolavirus
Hemorrhagic Fever, Ebola
Humans
Influenza A virus
Influenza, Human
Models, Biological
Norovirus
MD Multidisciplinary
General Science & Technology
Publication Status: Published
Article Number: 20160279
Online Publication Date: 2016-08-01
Appears in Collections:Mathematics
Statistics
Faculty of Natural Sciences



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