Applications of Mathematical Modelling to Infectious Disease Dynamics in Developing Countries.

Mathematical modeling has proven to be an essential tool for the development of

control strategies and in distinguishing driving factors in disease dynamics. A key

determinant of a given model's potential to aid in such measures is the availability

of 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 understand

the types of data that are necessary for a modeling project to be successful. In this

thesis I analyze the value of particular types of data for a set of infections. The first

project analyzes the importance of considering age-specific mixing patterns in vaccine

preventable infections in which disease severity varies with age. The second project

uses a simulated data set to explore the plausibility of recovering the parameters of an

epidemiological model from a time series data set of monthly dengue haemorrhagic

fever reports.