Population surveys of health and fertility are an important source of information about demographic trends and their likely impact on the HIV/AIDS epidemic. In contrast to groups sampled at health facilities they can provide nationally and regionally representative estimates of a range of variables. Data on HIV sero-status were collected in the 2001-2 Zambia Demographic and Health Survey (ZDHS) and made available in a separate data file in which HIV status was linked to a very limited set of demographic variables. We utilized this data set to examine associations between HIV prevalence, gender, age and geographical location. We apply the generalized geo-additive semi-parametric model as an alternative to the common linear model, in the context of analyzing the prevalence of HIV infection. This model enables us to account for spatial auto-correlation, non-linear, location effects on the prevalence of HIV infection at the disaggregated provincial level (9 provinces) and assess temporal and geographical variation in the prevalence of HIV infection, while simultaneously controlling for important risk factors. 54 % of the overall sample of 3950 was female. The overall HIV positivity rate was 565 (14.3%). The mean age at HIV diagnosis for male was 30.3 (SD: 11.2) and 27.7 (SD: 9.3) for female respectively. Lusaka and Copperbelt have the first and second highest prevalence of AIDS/HIV (marginal odds ratios of 3.24 and 2.88 respectively) but when the younger age of the urban population and the spatial auto-correlation was taken into account Lusaka and Copper belt were no longer among the areas with the highest prevalence. Nonlinear effects of age at HIV diagnosis were also discussed and the importance of spatial residual effects and control of confounders on the prevalence of HIV infection. The study was conducted to assess the spatial pattern and the effect of confounding risk factors on AIDS/HIV prevalence and to develop a means of adjusting estimates of AIDS/HIV prevalence on the important risk factors. Controlling for important risk factors such as geographical location (spatial auto-correlation), age structure of the population, gender gave estimates of prevalence that are statistically robust. Researchers should be encouraged to use all available information in the data to account for important risk factors when reporting AIDS/HIV prevalence. Where this is not possible, correction factors should be applied, particularly where estimates of AIDS/HIV prevalence are pooled in systematic reviews. Our maps can be used for policy planning and management of AIDS/HIV in Zambia.