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

Thomas House, Ashley Ford, Shiwei Lan, Samuel Bilson, Elizabeth Buckingham-Jeffery, Mark Girolami

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

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.

Original languageEnglish (US)
Article number20160279
JournalJournal of the Royal Society Interface
Volume13
Issue number121
DOIs
StatePublished - Aug 1 2016
Externally publishedYes

Keywords

  • Compartmental model
  • Markov chain Monte Carlo
  • Shedding

ASJC Scopus subject areas

  • Biotechnology
  • Biophysics
  • Bioengineering
  • Biomaterials
  • Biochemistry
  • Biomedical Engineering

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