Scale and uncertainty are important issues for the global prediction of disease. Disease mapping over the entire surface of the Earth usually involves the use of remotely sensed imagery to provide environmental covariates of disease risk or disease vector density. It further implies that the spatial resolution of such imagery is relatively coarse (e.g., 8 or 1 km). Use of a coarse spatial resolution limits the information that can be extracted from imagery and has important effects on the results of epidemiological analyses. This paper discusses geostatistical models for (i) characterizing the scale(s) of spatial variation in data and (ii) changing the scale of measurement of both the data and the geostatistical model. Uncertainty is introduced, highlighting the fact that most epidemiologists are interested in accuracy, aspects of which can be estimated with measurable quantities. This paper emphasizes the distinction between data- and model-based methods of accuracy assessment and gives examples of both. The key problem of validating global maps is considered.