Title

Stochastic SIR Modeling of Disease Dynamics

Document Type

Thesis

Publication Date

4-2015

Advisor

Robert Hesse, Mathematics

Abstract

Mathematical modeling is a powerful tool used to study the dynamical processes of disease networks. Because diseases evolve to resist treatment, epidemiological models are useful as predicative tools to measure how diseases behave in a population. This research provides insight on deterministic and stochastic models, mainly the Susceptible-Infected-Recovered (SIR) compartmental model of epidemiology. Due to the limited applicability of the deterministic model and the insolvability of the stochastic model, computer simulation was also used to understand disease dynamics. Future work includes revising the code of the SIR and the Susceptible-Exposed-Infected-Recovered (SEIR) models to increase their applicability.

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