ArticleJournal of the Royal Society, Interface2016
Bayesian uncertainty quantification for transmissibility of influenza, norovirus and Ebola using information geometry.
Article in Journal of the Royal Society, Interface, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Parameter estimation and uncertainty quantification using information geometry.Journal of the Royal Society, Interface · 2022Review
- Article
- Article
- Economic consequences of Japanese schools' recovery certificate policy for seasonal influenza.BMC public health · 2019Article
- Norovirus transmission dynamics: a modelling review.Epidemiology and infection · 2018Review
- An efficient moments-based inference method for within-host bacterial infection dynamics.PLoS computational biology · 2017Article
- Inferring stratified parasitoid dispersal mechanisms and parameters from coarse data using mathematical and Bayesian methods.Journal of the Royal Society, Interface · 2017Article
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Authors and funding
6 authors.
Funding
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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.
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