Mathematical modeling has proven to be an essential tool for the development ofcontrol strategies and in distinguishing driving factors in disease dynamics. A keydeterminant of a given model's potential to aid in such measures is the availabilityof data to parameterize and verify the model. For developing countries in particular,data is often sparse and difficult to collect. It is therefore important to understandthe types of data that are necessary for a modeling project to be successful. In thisthesis I analyze the value of particular types of data for a set of infections. The firstproject analyzes the importance of considering age-specific mixing patterns in vaccinepreventable infections in which disease severity varies with age. The second projectuses a simulated data set to explore the plausibility of recovering the parameters of anepidemiological model from a time series data set of monthly dengue haemorrhagicfever reports.</p
