A mathematical model is an explicit mathematical description of the simplified dynamics of a system. Using mathematical modelling of infectious diseases, disease infection patterns can be simulated for the development of various health management strategies. Since the infectious diseases are sometimes sporadic and often difficult to control, knowledge guided intuitive approaches such as mathematical modeling could aid in developing an understanding for infection management and eradication. One popular model used in the modelling of infectious diseases by computing the amount of people in a closed population at a given period of time, is the SIR model (Susceptible (S) -> Infectious (I) -> Recovered (R)). The model and its derivatives can be used for explaining the increase and decrease in people needing medical care for a certain disease during an epidemic. From numbers generated by the SIR model researcher’s health officials can calculate different numbers that allow them to see if policies are effective and if occurrence of the disease is increasing, decreasing, or stable. In this project infectious disease data analysis and processing leading to the development of predictive approaches using the SIR model and its derivatives is proposed. Findings of the study are hoped to help determine the plausibility of epidemiological explanations, possibly predict unexpected interrelationships among empirical observations and help predict the impact of changes in the infectious disease system under consideration.