The ROC curve used to describe the performance of the model in terms of its sensitivity and specificity at various cutoffs, and its overall accuracy independent of a single probability cutoff is shown in Figure 3. The model can dis- criminate between districts that have experienced epi- demics and those that have never been affected. When the values for randomly selected districts were entered into the model, the epidemic risk assigned was higher for districts with epidemics than for those without in 82% of the cases. The areas identified by the model coincide to a large extent with the areas observed to have experienced epidemics. By using a probability cutoff value of >0.4 for predicting epi- demic experience, the model had a sensitivity and speci- ficity of 83% and 67%, respectively; these statistics were confirmed in the validation process (Table 3). The map of residual risk unaccounted for by the model had a random distribution, as expected for a model that worked well across the continent (not shown). According to the model, 7% of the population of Africa live in very high-risk areas, 17% in high, and 27% in moderate (based on 1990 esti- mates), corresponding to 44, 102, and 162 million people, respectively (Table 4). Discussion This study represents a comprehensive and detailed description of the spatial distribution of meningitis epi- demics at the district level in Africa and the first attempt to develop a spatial forecasting model for meningitis epi- demics on the basis of the environmental characteristics of the continent considered a priori to be related to the spatial distribution of epidemics. The data have limitations that need to be considered for the proper interpretation of the models. For example, census data for Africa have limited accuracy (12), variables derived through remote sensing may only partially capture surface conditions, and data from meteorologic stations in Africa are often incomplete (13–15). The epidemiologic data span more than a century whereas the environmental and population data are rela- tively recent. In addition, some epidemics were likely never reported, and small outbreaks and those occurring in the 19th and early 20th centuries are likely to be dispropor- tionately underrepresented. Problems in defining epi- demics exist as well, since most reports lack clear or inter- nationally recognized criteria; we had to accept the percep- tion of an increased incidence that prompted outbreak reports. Moreover, the aggregation of local level statistics to an often larger and somewhat arbitrary district level, dis- crepancies between where people become ill and the loca- tion of notifying health facilities, and changes to district boundaries over time may each have resulted in potential loss of specificity. Despite these limitations, major out- breaks were unlikely to have gone completely unreported, and the long-term cumulative distribution of events is like- RESEARCH Figure 3. Receiver-operator characteristics curve for the model. ly not misrepresented on a pancontinental scale. While population densities in Africa have increased greatly dur- ing the last 150 years and substantial land-use change (par- ticularly in West Africa) is known to have occurred, the model was still able to identify the meningitis belt and areas previously described at risk beyond the Sahel (2–5,16); reports of epidemics occurring since 1999 have coincided with this description (available from: URL: http://www.who.int/disease-outbreak-news/). The model also has its limitations. We are aware of those imposed on the analysis by spatial relationships in the data, which indicate that the importance of regression effects may be overstated (17). No perfect model exists, but one can be developed that is based on the simplest approach and combination of variables that can be used to distinguish between areas with high and low risk of epi- demic experience. We were restricted by the limited avail- Table 3. Performance of the model for predicting epidemic experience at the district levela Epidemic experience Observed Predicted Ever Never Total districts Ever 1,022 682 1,704 Never 209 1,363 1,572 Total districts 1,231 2,045 3,276 Model Sensitivity % Specificity % (95% CI) (95% CI) 83 (81 to 85) 67 (65 to 69) 84 (80 to 87) 65 (62 to 69) Final (100%) Mean validation (40%) (n=1a) Excludes five districts for which environmental data were unavailable CI, confidence interval. Emerging Infectious Diseases • Vol. 9, No. 10, October 2003 1291