PhD Defense by Deji Fajebe

Event Details
  • Date/Time:
    • Friday September 30, 2016 - Saturday October 1, 2016
      12:00 pm - 1:59 pm
  • Location: Ivan Allen College G17.
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Summary Sentence: Computational Modeling of Spontaneous Behavior Changes and Infectious Disease Spread

Full Summary: No summary paragraph submitted.

Please join us as Deji Fajebe defends his dissertation thesis, “Computational Modeling of Spontaneous Behavior Changes and Infectious Disease Spread”. He will be presenting to the public at noon on Friday, September 30 in Ivan Allen College G17.


Members of the Defense Committee:



Peter Brecke, Committee Chair

Sam Nunn School


Michael Salomone

Sam Nunn School


John McIntyre

Sam Nunn School


Erica J. Briscoe

Georgia Tech Research Institute


Christine Ries

School of Economics


In the Spring of 2009, a new strain of pandemic influenza virus emerged in the human population and spread to major countries worldwide. This caused panic that the world was witnessing another influenza outbreak potentially of the size of the 1918 Spanish Influenza outbreak where a fifth of the world’s population was a↵ected. Although, this fear did not come to pass, the threat of a potentially deadly outbreak remains. The ability to mitigate and contain a disease is a vital aspect of any country’s response strategies. Through modeling and simulation of the spread of an outbreak, decision-makers can better plan mitigation and containment strategies. This dissertation investigates how changes in human behavior a↵ect the spread of pandemic influenza in the U.S. population using an agent-based computational model. The dissertation argues that more aspects of human behavior such as people’s attitudes and trust in government-issued health advisory infor- mation about the disease need to be integrated into population-level models of pandemic influenza to improve model realism. I present a framework for incorporating such factors into computational models of disease spread to simulate possible scenarios that the spread may take to improve policy insights. I created models to represent di↵erent configurations of the attitudinal disposition of the population and then examined how agents representing individuals responded to the interventions implemented. The study revealed that a popu- lation that responds postively to government interventions reduced overall disease impact in comparision to the other scenarios modeled. Although the model is built on the U.S. population, it may be generalized for other synthetic populations in the future.

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In Campus Calendar

Graduate Studies

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Phd Defense
  • Created By: Tatianna Richardson
  • Workflow Status: Draft
  • Created On: Sep 15, 2016 - 7:59am
  • Last Updated: Oct 7, 2016 - 10:19pm