RESEARCH Table 4. Population residing in districts at risk for meningitis epidemics in Africa Risk No. of districts 1990 population in millions (%) Low 1,572 291 (49) Moderate 971 162 (27) High 482 102 (17) Very high 251 44 (7) Not known 5 1 (<1) Total 3,281 600 ability of suitable datasets and chose the simplest combi- nation of variables that best predicted outcome. While this choice is likely to have oversimplified the association between meningitis epidemics and the environment, the model has the advantage of using data that are available in the public domain and, being based on a simple combina- tion of variables, are an important basis on which to devel- op research and future operational applications in resource-limited settings. The analysis indicates not only that absolute humidity profiles and land-cover types can be used to distinguish between areas with high and low risk of epidemics but also that population density and dust may also be implicated. The incidence of meningococcal disease has previously been correlated with dry and dusty conditions in tropical and temperate climates (18–21). Humidity and land cover were included in the final model for statistical reasons, but dust and population density still have an independent effect and may be important in determining epidemic occurrence (22). The potential role of dust in precipitating epidemics is particularly interesting since dustiness in the meningitis belt has increased dramatically since the Sahelian droughts of the 1970s and 1980s. However, how environmental variables interact is unclear and remains the subject of extensive climatologic research. Furthermore, we did not take into account the effect of other nonenvi- ronmental factors likely to be related to epidemics, such as population movement, vaccination coverage, and recent epidemics in the area. A combination of conditions is like- ly to be necessary for an epidemic to occur, and these nonenvironmental variables are likely to have additional predictive potential and should be considered in further studies. Risk maps of vector-borne diseases in Africa based on environmental data have received considerable attention in recent years and are tools with public health potential (10,15). The mechanisms by which environmental factors influence meningitis epidemics in Africa are unclear (3). Areas within the traditional meningitis belt and beyond, however, are environmentally susceptible to epidemics with potentially large populations at risk; markers such as absolute humidity and dust profiles, land-cover type, and population density are independent predictors of these areas. In addition, rainfall and dust are predictors in some, but not all regions, and the potential to develop region-spe- cific models that could be more sensitive within given eco- logic zones warrants further study. Our findings could facilitate the development of models to identify regions with increased vulnerability to epidemics in the future and provide a basis for monitoring the impact of climate vari- ability and environmental change on epidemic occurrence in Africa. Acknowledgments We thank Jay Herman for facilitating access to the aerosol data used in the analyses, Ian Hastings for statistical advice, and all persons and organizations that have contributed to the Meningitis Forecasting Project for their support and collaboration. Financial support was received from the Meningitis Research Foundation (UK), the U.S. National Oceanic and Atmospheric Administration Office of Global Programs, and Médecins Sans Frontières. 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