This document summarizes a study on ecological niche modeling and spatial risk analysis of Rift Valley Fever vectors in Kenya. The study aimed to evaluate the correlation between mosquito distribution and environmental factors associated with RVF outbreaks. Maximum entropy, boosted regression trees, and random forest models were used to develop risk maps predicting the potential spread of RVF vectors based on climatic and environmental variables. The models found that variables like rainfall, number of dry months, and moisture indices influenced the distributions of Culex and Aedes mosquitoes. The risk maps developed can help target RVF surveillance and control in high-risk areas. Limitations included lack of data from known outbreak hotspots and unreliable local climatic/ecological databases

1. Ecological Niche Modeling and Spatial Risk Analysis
for potential spread of Rift Valley Fever Vectors in
Kenya
Nanyingi M,Bayoh N, Ogola E, Thumbi M,Mosmtai G,Gachie T, Muchemi G,
Kiama G , Munyua P, Sang R, Njenga K and Bett B
Presented at 3rd MVVR Conference at Hilton Hotel Nairobi, 16th October 2014

2. History, Etiology and Epidemiology
 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.
Montgomery , 1912, Daubney 1931, Davies 1975, Jost et al., 2010
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;Sang et al ., 2010; Hightower et al., 2012

4. r
h
t Jan 0 Dec
Culex
eggs
Aedes
eggs
t20
h
Aedes
eggs
r
Culex
eggs
t0
Jan Dec
Adult Density Adult Density
(Aedes- Culex complex) responsible for maintenance and amplification of the virus

5. Objectives and Approaches
 To evaluate the correlation between mosquito distribution
and environmental-climatic attributes favoring emergence of
RVF.
(Statistical modeling the climatic, ecological and
environmental drivers of RVF outbreaks).
 To develop a risk map for spatial prediction of RVF
outbreaks in Kenya based on potential vector distribution
(Spatial and temporal analysis and risk modelling by GIS
Analysis)

6. Study Design and Research Approach
Geographical Distribution of Arthropod Vectors and Exploration of
Pathogens they Transmit in Kenya (Approved KEMRI ERC, SSC 1849)
 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. Spatiotemporal analysis and ENM for RVF risk prediction
(Maxent, GARP,BRT,RF) using R- Statistics,ArcGIS,QGIS

7. Maximum Entropy (Maxent) Model
Culex species was highly influence by the number of dry months variable (dm),
mean annual rainfall (bio12), Aedes was influenced by rainfall derived variables

8. Boosted Regression Trees(BRT)
Number of dry months (dm), longest dry seasons (llds) and
rainfall of wettest month (bio 13), had the highest influence on
culex species distribution.

9. Comparative Random Forest(RF) Output
Aedes is highly influence by moisture index of moist quarter (mimq)
rainfall of driest quarter (bio 17), rainfall of wettest month (bio13).

10. What Next?? Regional Models = Model Validation
Maxent
Geographically linked phylogenetic models?
Multisite country level surveillance coupled with RVF
seroepidemiology profiles for hotspots is promising for
validation and genomic pathogen discovery.

11. 11
Limitations of the study
 Lack of data from “hotspots” may complicate conclusive
associations between the vector presence, epidemiological data and
ecological predictors.
 Temporal and spatial distribution was not explicitly examined due to
insufficient vector presence data.
 Lack of reliable climatic and ecological parameters from local
databases hence leading to risk generalization projected from the
regional- global databases.
 Despite excellent model agreement in prediction of habitat suitability
for vectors, species taxonomic identification is underway for specific
niche modelling.
 Overfitting due to clustered sampling can lead to misinterpretation of
geographical spread of vector( corrected by stratification and cross-validation)

12. 12
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.
 The forecasting and early detection of RVF outbreaks
using VSS contributes to comprehensive risk assessment
of pathogen diffusion to naive areas, hence essential in
disease control preparedness.
 GIS tools and ENM can contribute to existing model
frameworks for mapping the areas at high risk of RVFV
and other vector borne diseases.

13. ACKNOWLEDGEMENTS
Data sources
 AFRICLIM database
 World Clim - Global Climate data, available at http://www.worldclim.org/
Collaborating Institutions
DVS, DDSR,DVBD,MOPH, ZDU,USAMRU
Individuals
 IHAHP team, study participants, CHW, Local administrators
Contact : mnanyingi@kemricdc.org, mnanyingi@gmail.com