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 and develop 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.

1. Spatial risk assessment of Rift Valley Fever potential
outbreaks using a vector surveillance system in
Kenya
Presented at KVA Scientific Conference at Boma Hotel, Eldoret 25th
April 2014
Nanyingi M,Ogola E, Olang G, Otiang E, Munyua P, Thumbi S, Bett B, Muchemi G, Kiama
S and Njenga K

2. History, Etiology and Epidemiology
Montgomery , 1912, Daubney 1931, Davies 1975, Jost et al., 2010
 RVF viral zoonosis of cyclic
occurrence(5-10yrs), described In
Kenya in 1912 isolated in 1931 in
sheep with hepatic necrosis and
fatal abortions.
 RVFV is an OIE transboundary
high impact pathogen and CDC
category A select agent.
Etiology: Phlebovirus in
Bunyaviridae (Family).
 Genome: tripartite RNA segments
designated large (L), medium (M),
and small (S) contained in a
spherical (80–120 nm in diameter)
lipid bilayer.
 Risk factors:
Precipitation: > 600mm, flooding
Altitude: <1100masl
 Vector +: Aedes, culicines spp?
NDVI: 0.1 units > 3 months
Soil : Solonetz, Solanchaks,
planosols
 Historical Outbreaks
 Epidemics in Africa and recently
Arabian Peninsula; in Egypt (1977),
Kenya (1997–1998, 2006-2007),
Saudi Arabia (2000–2001) and
Yemen (2000–2001), Sudan (2007)
and Mauritania (2010)

3. 3
RVF Vector Emergence (Ecological and Climatic)
 Precipitation: ENSO/Elnino above average
rainfall leading hydrographical
modifications/flooding
(“dambos”,dams,irrigation channels).
Vector Presence: 35/38 spp.
(interepidemic transovarial maintenance by
aedes 1º and culicine 2º (vectorial capacity/
competency)
 Dense vegetation cover =Persistent NDVI.
(0.1 units > 3 months)
 Soil types: Solonetz, Solanchaks,
planosols (drainage/moisture)
 Elevation : altitude <1,100m asl
Linthicum et al., 1999; Anyamba et al., 2009; Hightower et al., 2012

4. Objectives
Overall Objective
 Investigate climatic, ecological, entomological and
environmental drivers of RVF outbreaks in Kenya.
Specific Objectives
Geographical mapping and systematic classification of RVF
risk levels based on presence of competent vectors.
Develop a Vector surveillance Systems (VSS) RVF vector
distribution map for Kenya
 Molecular characterizing of RVFV and phylogenetic profiling
by geographical distribution.

5. Justification
 RVF is broadening its geographic range in Kenya with
potentially significant burden on animal and human health.
Previous RVF predictive models have factored in climatic
and environmental variables to forecast occurrence.
 This will be first attempt at a national level to create RVF
vector surveillance system and predictive risk maps for
Kenya using vector distribution profile to guide in strategic
surveillance and control strategies.
“Mosquitoes, flies, ticks and bugs may be a threat to your health –
and that of your family - at home and when travelling. This is the
message of this year’s World Health Day, on 7 April.”
VBD = RVF + Malaria

6. Study Design and Research Approach
 Cross-sectional and purposive design
1. Randomization of 15 high and 15 low risk (Case & Control)
districts based on RVF occurrence data (2006-2007).
2. Seasonality based on precipitation : Wet and dry
3. Monthly multisite sampling: 40 points in 4 quadrants.
4. Population based: Livestock and household distribution.
5. Socioeconomic survey (SES) and health care access.
6. Multivariable geostatistical analysis for RVF risk prediction.
KEMRI CDC Ethical Clearance SSC 1849
Geographical Distribution of Arthropod Vectors and Exploration of
Pathogens they
Transmit in Kenya

7. 7
Protocol development
 Malaria Endemicity zones
 Weighted Probability index
 Randomization of case and
control areas.
 Aedes and culicines are main
focus.
 Spatial distribution of vectors
in relation to RVF.
 Ecological Niche Modeling
(Maxent- Entropy)
 Phylogenetic characterization
 Design of control strategies for
vectors/vaccination prioritization

8. Methodology: Integrated Vector dynamics conceptual framework
IN-SITU RS DATAIN-SITU RS DATAENTOMOLOGICAL DATAENTOMOLOGICAL DATA
Hazard and Vulnerability Maps
(Environmental Risk)
ZPOM
Hazard and Vulnerability Maps
(Environmental Risk)
ZPOM
 Presence(Map Breeding sites)
 Abundance (Density)
 Flying range
 Host contact rate
 Presence(Map Breeding sites)
 Abundance (Density)
 Flying range
 Host contact rate
 Precipitation (WorldClim)
 Land cover (SPOT 7)
 Soil types
 Elevation (DEM)
 NDVI
 Precipitation (WorldClim)
 Land cover (SPOT 7)
 Soil types
 Elevation (DEM)
 NDVI
HumansHumans Livestock
(Ruminant)
Livestock
(Ruminant)
VECTOR RISK MAP
RVF OCCURRENCE
DATA
RVF OCCURRENCE
DATA
Tourre YM (2009) Global Health Action. Vol.2

9. Entomological Surveillance
Habitat and Ecological EvaluationHabitat and Ecological Evaluation
Larval ScoopingLarval Scooping Entomological characterizationEntomological characterization
Species identificationSpecies identification
GPS MappingGPS Mapping

10. Data: Environmental/Climatic databases and Secondary sources
Datatype Spatialresolution Timeperiod Sources
NDVI 250×250 m 1990-2013 MODIS/Terra1
Rainfall 1×1km 1990-2013 WorldClim2
Altitude(DEM) 1×1km N/A USGS3
Landcover 1×1km 1990-2013 GLCN4
Waterbodies-Lakes,ponds 1×1km 1990-2013 GLCN
Data type Sources
RVF occurence data Surveillance data (DVS, DDSR, Publications)
Livestock population Human and household census (KNBS), 2009
Human population Human and household census (KNBS), 2009
Livelihood zones FEWS NET

11. Statistical and Spatial Analysis
 Descriptive analysis for vector distribution on land cover
was done using R- Statistic.
 Spatial data was analysed by creation of thematic
distribution maps of vector species, livestock density in
Qgis and ArcGIS 9.3.
 Raster analysis using geoprocessing tools for buffering
was used to estimate the ZPOM. Zonal statistic function for
delimiting thresholds for elevation(DEM) and terrain
analysis using raster calculator was estimated.
 The boundaries of the risk maps were set by creating a
spatial mask to define the potential epizootic area (PEAM)
by thresholding method on NDVI climatological values
(0.15–0.4) NDVI units and precipitation of < 500mm pa

12. Mosquitoes collected( %) (N≈ 3000) for 11 months

13. Compartmental Model: Ordinary Differential Equation
Chitnis et al 2006;
Herd Immunity

14. 14
Primary vectors and Host contact analysis
 Ae. Aegypti Ae dimorphous A. mcintoshi
 Ae. Circumluteolus Ae. ochraceus
 Goats: Primary
hosts for viral
intensification
before spill over.
 Human- animal
aggregation
increasing biting
rates

15. Multi-vector correlation to Rainfall and NDVI
 Aedes mcintosh
 Ae.circumluteolus
 Ae.Ochraceus,
 Mansonia uniformis,
 Cx. poicilipes,
 Cx bitaeniorhynchus
 Anopheles
squamosus
 Mansonia africana,
 Cx. quinquefasciatus,
 Cx. univittatus ,
 Ae. pembaensis,
 Ae. Pembaensis
 Cx. bitaeniorhynchus
Sang et al 2010

16. r
h
Culex
eggs
Aedes
eggs
t0Jan Dec
t20
h
Aedes
eggs
r
Culex
eggs
t0
Jan Dec
AdultDensityAdultDensity

17. 17
Elevation (DEM) determinant for Multivector spread
• Altitude influences flooding
patterns and vector
emergence.
• 1100m asl favors RVF
occurrence by influencing
vector flight rate and
competence.

18. 18
Limitations of the study
 Transhumance: The seasonal movement of humans with
their livestock that are sero-positive may complicate
conclusive associations between the vector presence,
epidemiological data and ecological predictors.
 Temporal and spatial correlation was not explicitly
examined due to insufficient RVF serological and vector
presence data.
 Lack of reliable climatic and ecological parameters from
local databases hence leading to risk generalization
projected from the global databases.

19. 19
Further Analysis
 Bayesian geostastical modeling: spatial and non spatial
models with other covariate like distance from water
bodies would provide explanatory predictions for vector
emergence.
 Ecological Niche Modelling: Maxent and GARP analysis
is therefore recommended to explain species distribution
in non-sampled areas.
 Database refining: Cost effective surveillance
mechanisms are necessary for definition of spatial risk of
RVF at a small scale, the role wildlife spillover can be
assessed.
 Compartmental transmission models: Multivector–
Multihost risk models will be informative.

20. 20
Conclusions and Recommendations
 This is an empirical attempt to predict large-scale country
level spatial patterns of RVF occurrence using vector data
and ecological predictor variables.
 The vector predictive risk maps will be useful to animal
and human health decision-makers for planning
surveillance and control in RVF known high-risk areas.
 Cost effective vaccination programs can be spatially
targeted contiguous high-risk areas with evidence from
detailed epidemiologic and entomological investigations.
 The forecasting and early detection of RVF outbreaks
using the VSS can assist in comprehensive risk
assessment of pathogen diffusion to naive areas, hence
essential to enable effective and timely control measures
to be implemented.

21. ACKNOWLEDGEMENTS
Data sources
 Moderate Resolution Imaging Spectroradiometer (MODIS); available at
https://lpdaac.usgs.gov
 World Clim - Global Climate data, available at http://www.worldclim.org/
 United States Geological Services (USGS) Digital Elevation Model
(DEM) available at: http://eros.usgs.gov/
 Global Land Cover Network (GLCN):available at
http://www.glcn.org/databases/lc_gc-africa_en.jsp
Collaborating Institutions
DVS, DDSR,DVBD,MOPH, ZDU
Individuals
 Participants(SES), DVOs, CHW, Local administrators
Contact : mnanyingi@kemricdc.org, mnanyingi@gmail.com