Spatial risk assessment of Rift Valley Fever potential
outbreaks using a vector surveillance system in
Kenya
Presented at ...
History, Etiology and Epidemiology
Montgomery , 1912, Daubney 1931, Davies 1975, Jost et al., 2010
 RVF viral zoonosis of...
3
RVF Vector Emergence (Ecological and Climatic)
 Precipitation: ENSO/Elnino above average
rainfall leading hydrographica...
Objectives
Overall Objective
 Investigate climatic, ecological, entomological and
environmental drivers of RVF outbreaks ...
Justification
 RVF is broadening its geographic range in Kenya with
potentially significant burden on animal and human he...
Study Design and Research Approach
 Cross-sectional and purposive design
1. Randomization of 15 high and 15 low risk (Cas...
7
Protocol development
 Malaria Endemicity zones
 Weighted Probability index
 Randomization of case and
control areas.
...
Methodology: Integrated Vector dynamics conceptual framework
IN-SITU RS DATAIN-SITU RS DATAENTOMOLOGICAL DATAENTOMOLOGICAL...
Entomological Surveillance
Habitat and Ecological EvaluationHabitat and Ecological Evaluation
Larval ScoopingLarval Scoopi...
Data: Environmental/Climatic databases and Secondary sources
Datatype Spatialresolution Timeperiod Sources
NDVI 250×250 m ...
Statistical and Spatial Analysis
 Descriptive analysis for vector distribution on land cover
was done using R- Statistic....
Mosquitoes collected( %) (N≈ 3000) for 11 months
Compartmental Model: Ordinary Differential Equation
Chitnis et al 2006;
Herd Immunity
14
Primary vectors and Host contact analysis
 Ae. Aegypti Ae dimorphous A. mcintoshi
 Ae. Circumluteolus Ae. ochraceus
...
Multi-vector correlation to Rainfall and NDVI
 Aedes mcintosh
 Ae.circumluteolus
 Ae.Ochraceus,
 Mansonia uniformis,
...
r
h
Culex
eggs
Aedes
eggs
t0Jan Dec
t20
h
Aedes
eggs
r
Culex
eggs
t0
Jan Dec
AdultDensityAdultDensity
17
Elevation (DEM) determinant for Multivector spread
• Altitude influences flooding
patterns and vector
emergence.
• 1100...
18
Limitations of the study
 Transhumance: The seasonal movement of humans with
their livestock that are sero-positive ma...
19
Further Analysis
 Bayesian geostastical modeling: spatial and non spatial
models with other covariate like distance fr...
20
Conclusions and Recommendations
 This is an empirical attempt to predict large-scale country
level spatial patterns of...
ACKNOWLEDGEMENTS
Data sources
 Moderate Resolution Imaging Spectroradiometer (MODIS); available at
https://lpdaac.usgs.go...
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Spatial risk assessment of Rift Valley Fever potential outbreaks using a vector surveillance system in Kenya

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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.

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Spatial risk assessment of Rift Valley Fever potential outbreaks using a vector surveillance system in Kenya

  1. 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. 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. 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. 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. 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. 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. 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. 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. 9. Entomological Surveillance Habitat and Ecological EvaluationHabitat and Ecological Evaluation Larval ScoopingLarval Scooping Entomological characterizationEntomological characterization Species identificationSpecies identification GPS MappingGPS Mapping
  10. 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. 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. 12. Mosquitoes collected( %) (N≈ 3000) for 11 months
  13. 13. Compartmental Model: Ordinary Differential Equation Chitnis et al 2006; Herd Immunity
  14. 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. 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. 16. r h Culex eggs Aedes eggs t0Jan Dec t20 h Aedes eggs r Culex eggs t0 Jan Dec AdultDensityAdultDensity
  17. 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. 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. 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. 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. 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

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