Date of Award


Publication Type

Master Thesis

Degree Name



Computer Science

First Advisor

Kobti, Ziad (Computer Science)


Computer science.




Mathematical models, such as the Susceptible-Infected-Removed (SIR) epidemiological model, have been proven successful in predicting the spread of disease. Studies show that knowledge held by people, coupled with cultural influences, play important roles in identifying preventive behavior of people during an epidemic spread. In this research, two complementary extensions to the basic SIR framework are proposed. The first extension includes building a knowledge aware SIR (KSIR) model, adding a knowledge factor, where knowledge represents preventive behavior during a disease spread. The second extension provides for a population learning model and thus introduces a culturally sensitive KSIR model. A basic agent based model incorporating SIR model has been built as an initial framework wherein cultural algorithms are employed to create a culturally evolving population during an epidemic spread. A case study based on a cross cultural survey was used to initialize the data and validate the framework. Experimental results show that during a disease spread cultural knowledge influences people's behavior and thus is a deciding factor in risk assessment.