Nanyingi M ,
Ogola E, Olang G, Otiang E, Munyua P,
Thumbi S, Bett B, Muchemi G, Kiama S and Njenga K
Rift
Valley fever (RVF) is a vector-borne, viral, zoonotic disease that threatens
human and animal health. In Kenya the geographical distribution is determined
by spread of competent transmission vectors. Existing RVF predictive risk maps
are devoid of vectors interactions with eco-climatic parameters in emergence of
disease. We envisage to develop a vector surveillance system (VSS) by mapping
the distribution of potential RVF competent vectors in Kenya; To evaluate the
correlation between mosquito distribution and environmental-climatic attributes
favoring emergence of RVF and investigate by modeling the climatic, ecological
and environmental drivers of RVF outbreaks anddevelop a risk map for spatial prediction of RVF outbreaks in Kenya.
Using a
cross-sectional design we classified Kenya into 30 spatial units/districts (15
case, 15 control for RVF) based on historical RVF outbreaks weighted
probability indices for endemicity. Entomological and ecological surveillance
using GPS mapping and monthly (May 2013- February 2014) trapping of mosquitoes
is alternatively done in case and control areas. 2500 mosquitoes have been collected in 15 districts (50% geographical
target for each for case and control). Species
identified as (Culicines-86%, Anophelines-9.7%, Aedes- 2.6%) with over 65% distribution in RVF endemic areas. We
demonstrate the applications of spatial epidemiology using GIS to illustrate
RVF risk distribution and propose utilizing a Maximum Entropy (MaxEnt) approach
to develop Ecological Niche Models (ENM) for prediction of competent RVF vector
distributions in un-sampled areas. Targeting
RVF hotspots can minimize the costs of large-scale vector surveillance hence enhancing vaccination and vector
control strategies. A replicable VSS database and methods can be used for risk
analysis of other vector-borne diseases.