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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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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
Abstract
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.
