A Global Stochastic Modeling Framework to Simulate and Visualize Epidemics

PDF Version Also Available for Download.

Description

Epidemics have caused major human and monetary losses through the course of human civilization. It is very important that epidemiologists and public health personnel are prepared to handle an impending infectious disease outbreak. the ever-changing demographics, evolving infrastructural resources of geographic regions, emerging and re-emerging diseases, compel the use of simulation to predict disease dynamics. By the means of simulation, public health personnel and epidemiologists can predict the disease dynamics, population groups at risk and their geographic locations beforehand, so that they are prepared to respond in case of an epidemic outbreak. As a consequence of the large numbers of ... continued below

Creation Information

Indrakanti, Saratchandra May 2012.

Context

This thesis is part of the collection entitled: UNT Theses and Dissertations and was provided by UNT Libraries to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 453 times , with 10 in the last month . More information about this thesis can be viewed below.

Who

People and organizations associated with either the creation of this thesis or its content.

Chair

Committee Members

Publisher

Rights Holder

For guidance see Citations, Rights, Re-Use.

  • Indrakanti, Saratchandra

Provided By

UNT Libraries

Library facilities at the University of North Texas function as the nerve center for teaching and academic research. In addition to a major collection of electronic journals, books and databases, five campus facilities house just under six million cataloged holdings, including books, periodicals, maps, documents, microforms, audiovisual materials, music scores, full-text journals and books. A branch library is located at the University of North Texas Dallas Campus.

Contact Us

What

Descriptive information to help identify this thesis. Follow the links below to find similar items on the Digital Library.

Degree Information

Description

Epidemics have caused major human and monetary losses through the course of human civilization. It is very important that epidemiologists and public health personnel are prepared to handle an impending infectious disease outbreak. the ever-changing demographics, evolving infrastructural resources of geographic regions, emerging and re-emerging diseases, compel the use of simulation to predict disease dynamics. By the means of simulation, public health personnel and epidemiologists can predict the disease dynamics, population groups at risk and their geographic locations beforehand, so that they are prepared to respond in case of an epidemic outbreak. As a consequence of the large numbers of individuals and inter-personal interactions involved in simulating infectious disease spread in a region such as a county, sizeable amounts of data may be produced that have to be analyzed. Methods to visualize this data would be effective in facilitating people from diverse disciplines understand and analyze the simulation. This thesis proposes a framework to simulate and visualize the spread of an infectious disease in a population of a region such as a county. As real-world populations have a non-homogeneous demographic and spatial distribution, this framework models the spread of an infectious disease based on population of and geographic distance between census blocks; social behavioral parameters for demographic groups. the population is stratified into demographic groups in individual census blocks using census data. Infection spread is modeled by means of local and global contacts generated between groups of population in census blocks. the strength and likelihood of the contacts are based on population, geographic distance and social behavioral parameters of the groups involved. the disease dynamics are represented on a geographic map of the region using a heat map representation, where the intensity of infection is mapped to a color scale. This framework provides a tool for public health personnel and epidemiologists to run what-if analyses on disease spread in specific populations and plan for epidemic response. By the means of demographic stratification of population and incorporation of geographic distance and social behavioral parameters into the modeling of the outbreak, this framework takes into account non-homogeneity in demographic mix and spatial distribution of the population. Generation of contacts per population group instead of individuals contributes to lowering computational overhead. Heat map representation of the intensity of infection provides an intuitive way to visualize the disease dynamics.

Language

Collections

This thesis is part of the following collection of related materials.

UNT Theses and Dissertations

Theses and dissertations represent a wealth of scholarly and artistic content created by masters and doctoral students in the degree-seeking process. Some ETDs in this collection are restricted to use by the UNT community.

What responsibilities do I have when using this thesis?

When

Dates and time periods associated with this thesis.

Creation Date

  • May 2012

Added to The UNT Digital Library

  • Nov. 6, 2012, 3:03 p.m.

Description Last Updated

  • Nov. 16, 2016, 5:45 p.m.

Usage Statistics

When was this thesis last used?

Yesterday: 0
Past 30 days: 10
Total Uses: 453

Interact With This Thesis

Here are some suggestions for what to do next.

Start Reading

PDF Version Also Available for Download.

Citations, Rights, Re-Use

Indrakanti, Saratchandra. A Global Stochastic Modeling Framework to Simulate and Visualize Epidemics, thesis, May 2012; Denton, Texas. (digital.library.unt.edu/ark:/67531/metadc115099/: accessed May 28, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; .