Date on Master's Thesis/Doctoral Dissertation

5-2015

Document Type

Doctoral Dissertation

Degree Name

Ph. D.

Department

Industrial Engineering

Degree Program

Industrial Engineering, PhD

Committee Chair

Evans, Gerald W.

Committee Co-Chair (if applicable)

Bae, Ki-Hwan

Committee Member

Alexander, Suraj

Committee Member

Ramirez, Julio

Subject

Epidemics--Computer simulation; Epidemics--Prevention; Influenza--Prevention; Virus diseases--Transmission

Abstract

Millions of people have been infected and died as results of influenza pandemics in human history. In order to prepare for these disasters, it is important to know how the disease spreads. Further, intervention strategies should be implemented during the pandemics to mitigate their ill effects. Knowledge of how these interventions will affect the pandemic course is paramount for decision makers. This study develops a simulation-based optimization model which aims at finding a combination of strategies that result in the best value for an objective function of defined metrics under a set of constraints. Also, a procedure is presented to solve the optimization model. In particular, a simulation model for the spread of the influenza virus in case of a pandemic is presented that is based on the socio-demographic characteristics of the Jefferson County, KY. Then, School closure and home confinement are considered as the two intervention strategies that are investigated in this study and the simulation model is enhanced to incorporate the changes of the pandemic course (e.g. the number of ill individuals during the pandemic period) as results of the establishment of different scenarios for the intervention strategies. Finally, an optimization model is developed that its feasible region includes the feasible scenarios for establishment of intervention strategies (i.e. home confinement and school closure). The optimization model aims at finding an optimal combination of those two strategies to minimize the economic cost of the pandemic under a set of constraints on the control variables. Control variables include time, length of closure for schools, and the rate of home confinement of the individuals for home confinement strategy. This optimization model is connected to the pre-mentioned simulation model and is solved using a simulation-based optimization procedure called NSGS. Where the results of the analysis show both home confinement and school closure strategies are effective in terms of the outputs of the model (e.g. number of illness cases during the pandemic), they show home confinement is a more cost effective one.

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