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Malaria prevalence data were collated from surveys of childhood populations in Mali since 1960. Altogether 101 such surveys were identified yielding suitable estimates of malaria Background Good maps of malaria risk have long been recognized as an important tool for malaria control. The production of such maps relies on modelling to predict the risk for most of the map, with actual observations of malaria prevalence usually only known at a limited number of specific locations. Estimation is complicated by the fact that there is often local variation of risk that cannot be accounted for by the known covariates and because data points of measured malaria prevalence are not evenly or randomly spread across the area to be mapped. MethodWe describe, by way of an example, a simple two-stage procedure for producing maps of predicted risk: we use logistic regression modelling to determine approximate risk on a larger scale and we employ geo-statistical ('kriging') approaches to improve prediction at a local level. Malaria prevalence in children under 10 was modelled using climatic, population and topographic variables as potential predictors. After the regression analysis, spatial dependence of the model residuals was investigated. Kriging on the residuals was used to model local variation in malaria risk over and above that which is predicted by the regression model. ResultsThe method is illustrated by a map showing the improvement of risk prediction brought about by the second stage. The advantages and shortcomings of this approach are discussed in the context of the need for further development of methodology and software.

SummaryLarge parts of Africa are prone to malaria epidemics. Advance epidemic warning would give health services an opportunity to prepare. Because malaria transmission is largely limited by climate, climatebased epidemic warning systems are a real possibility. To develop and test such a system, good long-term malaria and climate data are needed. In KwaZulu-Natal (KZN), South Africa, 30 years of confirmed malaria case data provide a unique opportunity to examine short-and long-term trends. We analysed seasonal case totals and seasonal changes in cases (both log-transformed) against a range of climatic indicators obtained from three weather stations in the highest malaria incidence districts, using linear regression analysis. Seasonal changes in case numbers (delta log cases, dlc) were significantly associated with several climate variables. The two most significant ones were mean maximum daily temperatures from January to October of the preceding season (n ¼ 30, r 2 ¼ 0.364, P ¼ 0.0004) and total rainfall during the current summer months of November-March (n ¼ 30, r 2 ¼ 0.282, P ¼ 0.003). These two variables, when entered into the same regression model, together explained 49.7% of the total variation in dlc. We found no evidence of association between case totals and climate. In KZN, where malaria control operations are intense, climate appears to drive the interannual variation of malaria incidence, but not its overall level. The accompanying paper provides evidence that overall levels are associated with non-climatic factors such as drug resistance and possibly HIV prevalence.