The spread of disease is a major health concern in many parts of the world. In the absence of vaccines and treatments, the only method to stop the spread of disease is to control population movements. Human mobility is one of the causes of the geographical spread of emergent human infectious diseases and plays a key role in human-mediated bio-invasion, the dominant factor in the global biodiversity crisis. One of the most serious emergent infectious diseases in the last 30 years or so is AIDS (acquired immunodeficiency syndrome), where multiple pathogen species infect a human body. HIV/AIDS is now considered much more commonplace than previously thought. AIDS leads to interaction effects between the pathogens that may alter previously understood patterns of disease spread. There has been longstanding interest in how to model population movements in order to find optimal control strategies for a particular disease. The simulation models proposed here use cellular automata based on sound mathematical principles and epidemiological theory to model HIV/AIDS to provide a suitable framework to study the spatial spread of disease in different scenarios. This work investigates how probabilistic parameters affect the model in terms of time, location, gender, age and subgroups of the population. The cellular automaton modelling approach is used to forecast numbers of cases in different subgroups. An approach using wavelet transforms analysis is illustrated to understand the impact of delay on the spread of infectious disease. The results confirm that the higher the frequency, then the slower the spread of disease and vice versa. The thesis concludes with showing how co-infection can be modelled in future work on a theoretical base.