Bayesian Probabilistic Reasoning Applied to Mathematical Epidemiology for Predictive Spatiotemporal Analysis of Infectious Diseases Metadata

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Title

  • Main Title Bayesian Probabilistic Reasoning Applied to Mathematical Epidemiology for Predictive Spatiotemporal Analysis of Infectious Diseases

Creator

  • Author: Abbas, Kaja Moinudeen
    Creator Type: Personal

Contributor

  • Chair: Mikler, Armin R.
    Contributor Type: Personal
    Contributor Info: Major Professor
  • Committee Member: Atkinson, Samuel F.
    Contributor Type: Personal
  • Committee Member: Huang, Yan
    Contributor Type: Personal
  • Committee Member: Jacob, Roy T.
    Contributor Type: Personal
  • Committee Member: Oppong, Joseph R.
    Contributor Type: Personal

Publisher

  • Name: University of North Texas
    Place of Publication: Denton, Texas

Date

  • Creation: 2006-05
  • Digitized: 2008-04-11

Language

  • English

Description

  • Content Description: Abstract Probabilistic reasoning under uncertainty suits well to analysis of disease dynamics. The stochastic nature of disease progression is modeled by applying the principles of Bayesian learning. Bayesian learning predicts the disease progression, including prevalence and incidence, for a geographic region and demographic composition. Public health resources, prioritized by the order of risk levels of the population, will efficiently minimize the disease spread and curtail the epidemic at the earliest. A Bayesian network representing the outbreak of influenza and pneumonia in a geographic region is ported to a newer region with different demographic composition. Upon analysis for the newer region, the corresponding prevalence of influenza and pneumonia among the different demographic subgroups is inferred for the newer region. Bayesian reasoning coupled with disease timeline is used to reverse engineer an influenza outbreak for a given geographic and demographic setting. The temporal flow of the epidemic among the different sections of the population is analyzed to identify the corresponding risk levels. In comparison to spread vaccination, prioritizing the limited vaccination resources to the higher risk groups results in relatively lower influenza prevalence. HIV incidence in Texas from 1989-2002 is analyzed using demographic based epidemic curves. Dynamic Bayesian networks are integrated with probability distributions of HIV surveillance data coupled with the census population data to estimate the proportion of HIV incidence among the different demographic subgroups. Demographic based risk analysis lends to observation of varied spectrum of HIV risk among the different demographic subgroups. A methodology using hidden Markov models is introduced that enables to investigate the impact of social behavioral interactions in the incidence and prevalence of infectious diseases. The methodology is presented in the context of simulated disease outbreak data for influenza. Probabilistic reasoning analysis enhances the understanding of disease progression in order to identify the critical points of surveillance, control and prevention. Public health resources, prioritized by the order of risk levels of the population, will efficiently minimize the disease spread and curtail the epidemic at the earliest.

Subject

  • Library of Congress Subject Headings: Epidemiology -- Mathematical models.
  • Library of Congress Subject Headings: Bayesian statistical decision theory.
  • Library of Congress Subject Headings: HIV (Viruses) -- Texas -- Statistics.
  • Keyword: computational epidemiology
  • Keyword: Bayesian learning
  • Keyword: spatiotemporal analysis
  • Keyword: infectious diseases

Collection

  • Name: UNT Theses and Dissertations
    Code: UNTETD

Institution

  • Name: UNT Libraries
    Code: UNT

Rights

  • Rights Access: public
  • Rights License: copyright
  • Rights Holder: Abbas, Kaja Moinudeen
  • Rights Statement: Copyright is held by the author, unless otherwise noted. All rights reserved.

Resource Type

  • Thesis or Dissertation

Format

  • Text

Identifier

  • OCLC: 70286861
  • Archival Resource Key: ark:/67531/metadc5302

Degree

  • Degree Name: Doctor of Philosophy
  • Degree Level: Doctoral
  • Degree Discipline: Computer Science
  • Academic Department: Department of Computer Science and Engineering
  • Degree Grantor: University of North Texas

Note