Bayesian uncertainty quantification for transmissibility of influenza, norovirus and Ebola using information geometry
Author(s)
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.
Date Issued
2016-08-01
Date Acceptance
2016-07-25
Citation
Journal of the Royal Society Interface, 2016, 13 (121)
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.
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000385993000011&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
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
Publication Status
Published
Article Number
20160279
Date Publish Online
2016-08-01