| Summary: | Disease outbreaks are a constant threat to public health and so effective management of these outbreaks is vital. By using statistical methods, we can better understand how a disease is affecting populations and monitor the progression of diseases over time. This thesis applies and develops statistical methods to studies of vaccine-preventable disease outbreaks in Scotland and aims to aid in the detection and management of outbreaks. For detecting outbreaks, a system was designed for Health Protection Scotland (HPS) to link cases with incomplete genetic typing data to other cases to form potential clusters that may be worthy of further investigation. A novel inuenza strain spread worldwide in 2009 and this work helps in the understanding and monitoring of that outbreak. A key parameter is the reproductive number and this was monitored for pandemic influenza using routinely collected data. Then postcode data was added to develop a spatial model for estimating the rate of spread. For vaccine-preventable diseases, the primary intervention strategy is vaccination but their effectiveness must be assessed. Vaccine effectiveness (VE) was assessed against various clinical outcomes which were associated with influenza to differing extents. Additionally, different methods were employed, including attempts to correct for biases. The main findings of this work have important implications. The system to identify linked TB cases helps to ensure more links between cases are found, preventing further disease spread. The spatial method for estimating reproductive numbers offers improved parameter estimates. The VE study found that estimates differ more by the outcomes it was measured against than by the methods employed. Moreover, estimates found using outcomes with low specificity for influenza can be unreliable. Therefore, the recommendation for future studies is to focus on using outcomes with higher specificity for influenza.
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