378 HIGHTOWER AND OTHERS Figure 2. Rainfall in selected 10-day periods in Kenya. The first epidemic period, involving North Eastern province and Kilifi district, occurred from mid-December of 2006 to mid-January of 2007. The second epidemic period, involving Baringo district, began in mid-January of 2007 and peaked in early February. particularly during the beginning of rainy seasons. In arid areas, increases in rainfall precede increases in vegetation cover.19 As a result, increases in rainfall might be a better early indicator than NDVI measures for outbreak prediction. The estimated incidences in Figure 1 suggest that most of the country was at low risk for RVF during the outbreak period. There are some areas of estimated high incidence in northwestern and northeastern Kenya, where no cases were reported during this outbreak. These areas generally have solonetz (in the northeast) or solonchak (in the northwest) soil types,4 are plains, are sparsely populated, and in the case of the northeastern risk areas, are in the Somalia acacia eco- logical zone. The model notably does not explain the Kilifi cases well. Rainfall in early November was the only signifi- cant predictor of case occurrence in this area among the vari- ables considered. There are known informal trade routes for livestock originating in North Eastern province that pass through coastal areas, including Kilifi district.26 If some of these infected animals were the basis of the human disease, then livestock would provide a simple explanation for why these cases were not explained by climate or geology. This modeling approach has several shortcomings. First, it does not account for host susceptibility levels in both the human and animal populations; animals and humans previ- ously exposed to RVF virus would be unlikely to contribute to propagating and spread of virus during a period of poten- tial virus transmission. This result could overpredict outbreak occurrence or incorrectly assess an individual’s risk. Second, cases could have occurred in areas with no surveillance or reporting capabilities. Omission of such unreported cases could have easily changed the findings of the model. Third, it does not include individual risk activities, such as contact with bodily fluids from infected animals.3,9 Geographic locations for cases were approximate and may not be the same as their location when infected. This finding could result in attributes for case locations being biased to those attributes for non-case locations. Finally, this model used population data based on an assumption of a uniform density of people within the smallest administrative unit, the sublocation. Severe viola- tions of this assumption could lead to inaccuracies in the estimates of incidence. Analyses that were based on case versus non-case locations alone yielded somewhat different results than analyses using person-time as denominators (Table 1). This difference is because of the distribution of the population being different from the distribution of geological factors. For example, calcisols were a smaller percent of case locations than non- case locations, making it seem to be a risk factor for the location-based analysis. However, few people lived in areas with calcisols (less than 1% of person-time, despite covering