RESEARCH Events affecting parts of any one country in one epidemic season were considered as one epidemic. We then classi- fied the 3,281 districts of Africa as ever or never having experienced a documented epidemic of meningitis. In this analysis, no attempt was made to distinguish between epi- demics of different scales. Environmental Data Environmental information for the African continent was obtained from a variety of sources. In this analysis, we restricted data to variables available in the public domain with digital grid–based uniform continental coverage, which we considered important. Variables included month- ly means (long-term averages) of absolute humidity (7), absorbing aerosols (dust) and rainfall, and land-surface maps of land-cover type and population density (Table 1). All data grids were incorporated into the GIS. To collect the seasonal variation in climate while reduc- ing the number of explanatory variables without loss of information, we reprocessed the monthly means of each variable to create a single surface comprising categories representing unique seasonal profiles. This reprocessing involved submitting the monthly means to a principal com- ponents analysis followed by a clustering procedure in which we grouped regions with a similar seasonal profile using ADDAPIX software (version 2.05, S. Griguolo, University of Venice, Italy, available from: URL: http://metart.fao.org/T_I/GBR/Tools/Eaddapix.htm). This software performs spatial and temporal analyses of time- series data in continuous grid-based surfaces (8). The use of seasonal profiles for describing the climate of an area is widely used in crop monitoring in agriculture and has been used in modeling malaria prevalence in Gambia (9). The profile surfaces were then imported into ArcView 3.1 (ESRI). The seasonal absolute humidity profile is shown in Figure 1. For every district, a value was extracted in GIS repre- senting those grid cells of each profile surface contained within the district boundary. These values comprised the most common seasonal profile class for each variable (absolute humidity, dust, and rainfall), the most common land-cover type, and the geometric mean population den- sity. Analysis A logistic regression analysis was used to identify asso- ciations between a district ever or never having experi- enced an epidemic and the environment by using SPSS 11.0 software (SPSS Inc., Chicago, IL). Explanatory vari- ables were first investigated individually and then entered stepwise into a forward conditional multiple regression analysis. Because of the need to reduce colinearity, we did not analyze environmental variables with similar geo- graphic distributions together in the same model. The final model was based on the simplest approach and a combina- tion of variables that best predicted the distribution of epi- demics. This model was created with and without weight- ing for the inverse size of the district to assign less weight to larger districts, which may have been more prone to eco- logic variation and therefore inadequately represented by a single value. The probability of each district ever having had an epidemic was predicted by using the model. These probabilities were grouped into risk categories and mapped, and the estimated total population was extracted in GIS derived from population density forecasts (U.S. Geological Survey, 1990). The sensitivity and specificity of the model were assessed by examining the agreement between predicted and observed epidemic experience by using a receiver- operator characteristics (ROC) curve to select the optimal probability cutoff values on which predictions are based (10). The dataset was then split at random into two parts containing approximately 60% and 40% of the 3,281 dis- tricts. The model was recreated with the 60% dataset, using the same variables as above and used to predict the risk for epidemics in the remaining 40% (the validation set). We repeated this process 10 times and compared the mean sen- sitivity and specificity of the validation set with the model Table 1. Characteristics of the environmental variables Variable Temporal resolution Time period Resolution of grid squares Interpolated meteorologic station dataa Average daily mean absolute humidity Mean monthly 1961–1990 0.5° lat x 0.5° long (nominal 50 km) Average daily rainfall Mean monthly 1961–1990 0.5° lat x 0.5° long (nominal 50 km) Remotely sensed satellite datab Average daily aerosol index (dust) Mean monthly 1980–1999 1.0° lat x 1.25° long (nominal 100 km) Digital maps Land-cover typec 1992–1993 1 x 1 km Population densityd 1990 0.042° lat x 0.042° long (nominal 4 km) aMean monthly climate averages (1961–1990) for absolute humidity and rainfall (the former derived from vapor pressure and mean temperature [7]) were obtained from the Climate Research Unit, University of East Anglia, UK (available from: URL: http://ipcc-ddc.cru.uea.ac.uk/). bDust was obtained as monthly aerosol index coverages from the U.S. National Aeronautics and Space Administration (NASA), Goddard Space Flight Center, Maryland (available from: URL: http://www.gsfc.nasa.gov/), excluding the period May 1993 to June 1996 for which data are not available. cLand-cover type was obtained from the USGS NASA PATHFINDER 1km project (available from: URL: http://edcdaac.usgs.gov/1KM/1kmhomepage.html/). dU.S. Geologic Survey 1990 population density forecasts (available from: URL: http://grid2.cr.usgs.gov/globalpop/africa/). 1288 Emerging Infectious Diseases • Vol. 9, No. 10, October 2003