Epidemics: Models and Data

The problems of understanding and controlling disease raise a range of challenging mathematical and statistical research topics, from broad theoretical issues to specific practical ones. In particular, recent interest in acquired immune deficiency syndrome has stimulated much progress in diverse areas of epidemic modelling, particularly with regard to the treatment of heterogeneity, both between individuals and in mixing of subgroups of the population. At the same time better data and data analysis techniques have become available, and there have been exciting developments in relevant theory, ranging from random graphs and spatial stochastic processes to the structural stability of difference and differential equations. This progress in specific areas is now being matched by interdisciplinary cooperation aimed at elucidating relationships between the widely varying types of model that have been found useful, to determine their strengths and limitations in relation to basic aims such as understanding, prediction, and evaluation and implementation of control strategies. Such interdisciplinary work can be expected to make major contributions to the modelling of a wide range of human, animal and plant diseases, as well as to general statistical and biomathematical theory.