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 association 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 cluster, 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 occurrence. Solonchak soils transition to solonetz soils on drying.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 November 30, 2006, whereas the first Kilifi case occurred on December 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 locations). The first Baringo case occurred on January 25, with peak occurrence during the first week of February4
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, 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 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 ecological zone. The model notably does not explain the Kilifi cases well. Rainfall in early November was the only significant predictor of case occurrence in this area among the variables 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 previously exposed to RVF virus would be unlikely to contribute to propagating and spread of virus during a period of potential 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 violations 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 (). 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 10% of Kenya), resulting in the incidence for those areas being significantly greater than the reference areas. Similarly, the differences in the percent of case and non-case locations that were solonetz soil types were relatively small. As with calcisols, few people lived in these areas, which when combined with the number of cases occurring, resulted in a high incidence and increased RRs during the outbreak period.
One of the strengths of this model is that it uses geographic information that is commonly available. Many countries have rich geographical, geological, and meteorological data available in georeferenced format that could serve as a foundation for similar investigations of the occurrence of RVF or other diseases. Although considerable effort was spent geocoding the case locations, the specificity of this information allowed optimal use of the available reference data on the other geographic variables. This effort is the first that takes place on a scale between individual risk factors3,4,9
and factors affecting multiple countries or regions.23
It improves on earlier efforts4
by using methods that allow simultaneous assessment of multiple variables and incidences for the epidemic period to be computed.
In conclusion, our models suggest that RVF incidence during the outbreak period in Kenya was related to a number of geological, geographical, and meteorological factors, some previously recognized and others not recognized. We have shown that the Kilifi and North Eastern clusters could be linked by rainfall in early November and that the Baringo and North Eastern clusters could be linked by soil types and land cover. The model notes that all cases occurred in generally flat areas and at lower altitudes (always 1,100 m or less). The findings suggest that, although rainfall and associated measures are important predictors of RVF outbreaks, there are additional factors that better define the optimal environment for RVF occurrence. Such findings have the potential to improve current outbreak prediction models by limiting the geographic ranges of prediction to areas that are at risk of having outbreaks occur.