Ecological Niche Modelling of Potential RVF Vector Mosquito Species and their Geographical Association with RVF Epizootics in Kenya.

Research Scientist em Kenya Scientific Analyst
21 de Oct de 2014

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Ecological Niche Modelling of Potential RVF Vector Mosquito Species and their Geographical Association with RVF Epizootics in Kenya.

  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 Collaborating Institutions DVS, DDSR,DVBD,MOPH, ZDU,USAMRU Individuals  IHAHP team, study participants, CHW, Local administrators Contact :,