Geographic mapping of the distribution of malaria is complicated by the limitations of the available data. The most widely available data are from prevalence surveys, but these surveys are generally carried out at arbitrary locations and include nonstandardized and overlapping age groups. To achieve comparability between different surveys, the authors propose the use of transmission models, particularly the Garki model, to convert heterogeneous age prevalence data to a common scale of estimated entomological inoculation rates, vectorial capacity, or force of infection. They apply this approach to the analysis of survey data from Mali, collected in 1965-1998, extracted from the Mapping Malaria Risk in Africa database. They use Bayesian geostatistical models to produce smooth maps of estimates of the entomological inoculation rates obtained from the Garki model, allowing for the effect of environmental covariates. Again using the Garki model, they convert kriged entomological inoculation rates values to age-specific malaria prevalence. The approach makes more efficient use of the available data than do previous malaria mapping methods, and it produces highly plausible maps of malaria distribution.