State-of-the-art epidemic simulation. (360G-Wellcome-089237_Z_09_Z)

Mathematical models are used to predict the likely spread of infections and hence optimize control methods, this requires increasing levels of realism to be included. At the heart of epidemic models are assumptions about how individuals interact and the chance of the pathogen spreading from an infectious to a susceptible host. In particular, for human diseases, the movement of individuals dictates the spatial spread of infection; however, relatively little information exists on the movement patt erns of humans. The commercial retail sector has dedicated substantial resources towards understanding and predicting human movement patterns and how these correlate with other social measures. Many of these predictions have been verified against a range of surveys. Such ground-truthed movement models provide an opportunity to include far greater realism in the interaction between individuals and hence greater realism in the transmission process. We aim to build next-generation epidemic simulation models that can incorporate a wide range of spatial and social heterogeneities; these detailed predictions will be used to inform localized control measures, develop better predictions of cases and dispersal following a pathogen release. The results will be compared to similar predictions from simpler models to elucidate which heterogeneities are pivotal in determining epidemic spread.