Graduation Year


Document Type




Degree Granting Department

Health Policy and Management

Major Professor

Alan M. Sear, Ph.D.

Committee Member

Barbara L. Orban, Ph.D.

Committee Member

James Studnicki, Sc.D.

Committee Member

Yiliang Zhu, Ph.D.


computer modeling, simulation, blasts, planning, emergency care, care processes, health system preparedness, health system resources


Recent events throughout the world and in the US lend support to the belief that another terrorist attack on the US is likely, perhaps probable. Given the potential for large numbers of casualties to be produced by a blast using conventional explosives, it is imperative that health systems across the nation consider the risks in their jurisdictions and take steps to better prepare for the possibility of an attack. Computer modeling and simulation offers a viable and useful methodology to better prepare an organization or system to respond to a large scale event. The real question, given the shortage, and in some areas absence, of experiential data, could computer modeling and simulation be used to predict the resource requirements generated by this type of event and thus prepare a health system in a defined geographic area for the possibility of an event of this nature? Research resulted in the identification of variables that surround a health system at risk, the development of a computer model to predict the injuries that would be seen in an injured survivor population and the medical resources required to care for this population. Finally, methodologies were developed to modify the existing model to match unique health system structures and processes in order to assess the preparedness of a specific geographic location or health system. As depicted in this research, computer modeling and simulation was found to offer a viable and usable methodology for a defined geographic region to better prepare for the potential of a large scale blast event and to care for the injured survivors that result from the blast. This can be done with relatively low cost and low tech approach using existing computer modeling and simulation software, making it affordable and viable for even the smallest geographic jurisdiction or health system.