Date of Award

4-2018

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Engineering and Sciences

First Advisor

Marco Carvalho

Second Advisor

Robert van Woesik

Third Advisor

William Allen

Fourth Advisor

Ronaldo Menezes

Abstract

Epidemic diseases have become one of the major sources of concern in modern society. Diseases such as HIV and H1N1 have infected millions of people around the world. Many studies have attempted to describe the dynamics of disease spreading. Despite the numerous models, most only analyze one aspect of a given model, such as the impact of the topology of a contagious network, or, the spreading rate of disease. Although useful, these types of analyses do not provide insights into how different models could be combined to better understand the phenomenon, or how the modeling decisions affect the dynamics of disease spreading. The lack of methods to compare and analyze different models and assess their limitation is an open problem. We need methods to answer questions such as: Is it possible to combine different scenarios produced by different models to have a better understanding of disease spreading? How do modeling decisions affect the dynamics of disease spreading? Is it possible to rank different models according to their accuracy and identify statistical dependencies among the parameters of the system? In this dissertation, I propose an approach to compare disease spreading models using Bayesian networks, which allows one to understand the relationship between the variables of the model and the results it is able to generate. This approach, formalized as a methodology, will provide a new perspective to the study of disease spreading models. By creating a merged Bayesian model that accounts for all the results of the analyzed models we are able to evaluate how modeling decisions affect the dynamics of disease spreading and consequently the accuracy of such models. We apply our methodology by analyzing models of disease spreading presented in the literature. Our results shown that some models are more indicated than others related to their accuracy to reproduce real world scenarios. We evaluated the effects on the spreading dynamics of the models parameters and their values chosen. We also used real-world data about chlamydia to validate our results.

Download

Share

COinS