  Infectious diseases remain one of the major causes of human mortality and
suffering. Mathematical models have been established as an important tool for
capturing the features that drive the spread of the disease, predicting the
progression of an epidemic and hence guiding the development of strategies to
control it. Another important area of epidemiological interest is the
development of geostatistical methods for the analysis of data from spatially
referenced prevalence surveys. Maps of prevalence are useful, not only for
enabling a more precise disease risk stratification, but also for guiding the
planning of more reliable spatial control programmes by identifying affected
areas. Despite the methodological advances that have been made in each area
independently, efforts to link transmission models and geostatistical maps have
been limited. Motivated by this fact, we developed a Bayesian approach that
combines fine-scale geostatistical maps of disease prevalence with transmission
models to provide quantitative, spatially explicit projections of the current
and future impact of control programs against a disease. These estimates can
then be used at a local level to identify the effectiveness of suggested
intervention schemes and allow investigation of alternative strategies. The
methodology has been applied to lymphatic filariasis in East Africa to provide
estimates of the impact of different intervention strategies against the
disease.
