Introduction of the fundamental principles, including basic model structures for different diseases. How model equations are constructed to reflect biology (e.g. modes of transmission, whether immunity occurs or not). How age structure and heterogeneity in risk behaviour or disease susceptibility are incorporated. How the basic reproduction number is calculated. Stochastic and spatially-explicit models are also explained.

• Vector-borne diseases: a multi-species ecosystem. 
• The herd effect in infectious disease epidemiology.
• Planning mass vaccination campaigns.
• Hospital-acquired infections: where stochasticity rules.
• Interactions between infectious diseases 

• Designing a model of tuberculosis transmission.
• Introducing modelling software
• Estimating key parameters from an outbreak of influenza.
• Exploring heterogeneous behaviour in a model of sexually transmitted diseases.
• A stochastic model of nosocomial MRSA.  

• Mathematical models and infectious diseases: successes of the past and challenges for the future.
• Health economics of infectious disease control.
• Interventions against HIV in Zimbabwe
• The Schistosomiasis Control Initiative.
• Human Papillomavirus Vaccination.
• Schistosomiasis: from models to data.
• Seasonality of infectious diseases.
• BSE and vCJD: Mad cows and Englishmen.
• HIV, UNAIDS and models for a global pandemic.
• Bacterial genetics, epidemiology and evolution.
• Bovine TB: science, policy and dogma.
• Planning for Pandemic Influenza. 
• Preparing for future infectious disease threats. 

• HIV/AIDS: Antiretroviral therapy and HIV transmission in a developing country context.
• Malaria & human onchocerciasis.
• Avian influenza. 
• Ebola: real-time response to the ongoing epidemic.