GIS ANALYSIS OF 2006–2007 KENYA RVF OUTBREAK DATA Table 2 Rainfall and NDVI measures by month and dekads (10-day interval) Case locations Non-case locations Rainfall (cm) Rain (annual) November 1–10 November 11–20 November 21–30 December 1–10 December 11–20 December 21–31 January 1–10 January 11–20 January 21–31 February 1–10 February 11–20 February 21–28 NDVI November 1–10 November 11–20 November 21–30 December 1–10 December 11–20 December 21–31 January 1–10 January 11–20 January 21–31 February 1–10 February 11–20 February 21–28 N 69 70 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 Mean 49.1 6.74 10.06 5.56 1.44 7.09 2.14 0.41 0.43 0.25 0.59 0 0.48 –0.16 –0.06 0.020 0.10 –0.12 0.11 0.12 –0.09 –0.16 –0.25 –0.21 –0.96 SD 24.6 5.56 4.42 1.51 1.94 5.83 2.63 0.61 0.50 0.68 1.60 0 0.52 0.26 0.24 0.29 0.29 0.31 0.35 0.31 0.38 0.28 0.26 0.23 0.04 N 45,813 46,130 45,532 45,532 45,532 45,532 45,532 45,532 45,532 45,532 45,532 45,532 45,532 45,532 45,532 45,532 45,532 45,532 45,532 45,532 45,532 45,532 45,532 45,532 45,532 Mean 57.5 3.66 6.36 6.37 2.56 3.15 5.46 1.34 0.79 0.59 1.31 0.27 0.99 –0.22 –0.12 0.01 0.07 –0.02 0.09 0.12 –0.08 –0.080 –0.183 –0.192 –0.923 SD 31.8 3.50 5.00 3.27 2.38 4.10 5.39 1.80 1.72 1.07 2.12 0.67 1.15 0.37 0.38 0.47 0.41 0.39 0.46 0.37 0.40 0.361 0.399 0.362 0.090 Relative risk 0.95 1.11 1.06 0.74 0.71 1.26 0.66 0.33 0.51 0.32 0.65 Undefined 0.15 0.996 1.023 1.0425 0.965 0.849 1.1345 0.922 0.895 0.792 0.780 0.789 0.086 Lower 95% CL Upper 95% CL 0.94 1.08 1.03 0.70 0.66 1.23 0.63 0.27 0.44 0.25 0.59 Undefined 0.11 0.959 0.980 1.009 0.921 0.818 1.098 0.879 0.861 0.765 0.754 0.761 0.058 377 0.95 1.14 1.08 0.78 0.78 1.30 0.69 0.40 0.58 0.40 0.70 Undefined 0.19 1.034 1.07 1.076 1.015 0.883 1.172 0.971 0.932 0.820 0.808 0.820 0.122 and December 11–20, NDVI of December 21–31, plains land- forms, and solonetz, planosol, and calcisol soil types. DISCUSSION The association of a number of past RVF outbreaks with flooding has led to a model to forecast future outbreaks.11,12,23 Notably, the 2006–2007 RVF outbreak in east Africa was forecast by one of these models.23 For our model, geocoded case locations, geographic and other geocoded information for the entire country, census data, and a defined period of risk allows Poisson regression to be used for modeling, which in turn, allows RRs and incidences to be computed. It should be noted that our model uses variables to predict RVF incidence during the outbreak period rather than outbreaks of RVF. Taken as a whole, the variables in the final multivariable model associate increased incidence of RVF with locations that have attributes that provide optimal vector habitat at each life stage. Elevation reflects the limited range of the vectors. The lower NDVI in the first 10 days of November of 2006 describes an area that is more arid than the rest of the country. The increased rainfall preceding the outbreak period provides water to rehydrate desiccated mosquito eggs in soil. A dense bush vegetation cover could provide landing zones and resting areas that would be desirable to vectors. The plains landform allows flood waters to pool more easily to provide larval habitat than hilly or other non-flat landforms. The asso- ciation of soil types has been discussed in detail previously, including with a soil map of Kenya showing case locations.4 Briefly, all of the associated soil types have substrata that could serve to retain water better than other soil types (e.g., sandy), a feature that could plausibly facilitate rehydration of desiccated mosquito eggs in normally arid settings. When investigating relationships between RVF and soil types in other settings, consideration should be given to any soil type that forms water-retaining strata, not just the types that were found to be associated in this outbreak investigation. Our model provides both linkages and contrasts between the cases occurring in the three clusters. The soil type analysis provides a potential linkage between the Baringo district clus- ter, where cases occurred in solonchak soils in a wetlands, to the cluster in normally arid North Eastern province, where solonetz soils, among others, were associated with case occur- rence. Solonchak soils transition to solonetz soils on dry- ing.4,16 In our models for each cluster, the North Eastern and Kilifi cases were both associated with rainfall occurring approximately 3 weeks before the first reported case onset. The first case in North Eastern province occurred on Novem- ber 30, 2006, whereas the first Kilifi case occurred on Decem- ber 1, 2006.4 The Baringo model showed that cases were associated with increased rainfall in early February when the rest of the country was much drier (including other case loca- tions). The first Baringo case occurred on January 25, with peak occurrence during the first week of February4 (Figure 2). Both rainfall and NDVI measures were in our final model, and their roles are complimentary. The coefficients of NDVI and the rainfall amounts for the first 10 days of November describe an arid area receiving more rainfall than other parts of the country. Although they are related, both factors are important in different ways in the mosquito lifecycle. Rainfall would be important in rehydrating soils needed to help mosquito eggs hatch, whereas higher values of NDVIs (with appropriate land cover) could reflect better resting places for mosquitoes. Higher values of NDVIs are often most correlated with rainfall occurrence in preceding weeks,