The problems of understanding and controlling disease present a range of mathematical challenges, from broad theoretical issues to specific practical ones, making epidemiology one of the most vibrant branches of applied biology. Progress in this field requires collaboration among leading researchers with a wide range of mathematical expertise and close involvement in applied fields across the social, medical and biological sciences. This volume surveys the current state of epidemic modeling in relation to basic aims such as understanding, prediction, and evaluation and implementation of control strategies. The book is divided into five parts, covering the conceptual framework, three major problem areas (space, nonlinearity, heterogeneity), and the direct relation of models to data. The contributors discuss a wide range of methodological issues, e.g. comparing different approaches to the modeling of heterogeneity and relations among different types of model; and different data analytic approaches, together with the availability and quality of the data they require.