Infectious diseases impose a significant health and economic burden upon individuals and societies. Modelling is increasingly used to understand how pathogens spread through a population, and to inform decisions about potential control measures. Traditional mathematical approaches to modelling infectious diseases, while powerful, can require simplifying assumptions about the characteristics and mixing behaviour of populations. However, optimal use of healthcare resources requires interventions that are tailored to the structural and behavioural characteristics of specific populations. Computational approaches, such as individual-based models, can be better suited to capturing the complexity and heterogeneity of real populations.

In this talk, I will describe an individual-based modelling platform we have developed that captures realistic patterns of age and household structure, in addition to the demographic processes that modify this structure over time.

I will outline the application of this platform to a number of important topics in disease dynamics and control, including: (A) understanding the implications of demographic transitions (to older populations living in smaller households) for disease transmission and vaccine impact; (B) evaluating the impact of maternally-targeted vaccination strategies against pertussis (whooping cough) and influenza in developed and developing country settings; and (C) the design of enhanced surveillance strategies ("first few hundred studies") for impact assessment during pandemic events.