Complex simulations are often the means of informing public health disease control strategies. Chlamydia is one example of a disease where models are required to predict the impact of large scale control programmes. This thesis uses a variety of deterministic models parametrised for chlamydia to design effective control strategies for use in more complex models. Key information is extracted from simulations and data in order to correctly define how deterministic models respond to changes in prevalence, and how the risk of re-infection impacts on individuals who are treated. By using both screening and contact tracing as targeted interventions, it is possible to improve the effectiveness of current measures used to control chlamydia. The tracing of the partners of infected individuals is effective at reducing prevalence and financial costs, while re-testing individuals three months after treatment prevents re-infection from untreatedpartners.
|Date of Award||20 Feb 2014|
|Supervisor||Jane White (Supervisor)|