Koelle, KatharinaCastorena, Christopher Robert2013-12-162013-12-162013https://hdl.handle.net/10161/8267<p>Mathematical modeling has proven to be an essential tool for the development of</p><p>control strategies and in distinguishing driving factors in disease dynamics. A key</p><p>determinant of a given model's potential to aid in such measures is the availability</p><p>of data to parameterize and verify the model. For developing countries in particular,</p><p>data is often sparse and difficult to collect. It is therefore important to understand</p><p>the types of data that are necessary for a modeling project to be successful. In this</p><p>thesis I analyze the value of particular types of data for a set of infections. The first</p><p>project analyzes the importance of considering age-specific mixing patterns in vaccine</p><p>preventable infections in which disease severity varies with age. The second project</p><p>uses a simulated data set to explore the plausibility of recovering the parameters of an</p><p>epidemiological model from a time series data set of monthly dengue haemorrhagic</p><p>fever reports.</p>Public healthDisease ModellingMaster's thesis