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

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, floodingAltitude: <1100masl Vector +: Aedes, culicines spp?NDVI: 0.1 units > 3 monthsSoil : 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)

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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

Objectives

Overall Objective Investigate climatic, ecological, entomological and

environmental drivers of RVF outbreaks in Kenya.

Specific ObjectivesGeographical 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.

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

Study Design and Research Approach Cross-sectional and purposive design1. 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

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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

Methodology: Integrated Vector dynamics conceptual framework

IN-SITU RS DATA IN-SITU RS DATA ENTOMOLOGICAL 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

Humans Humans Livestock(Ruminant)

Livestock(Ruminant)

VECTOR RISK MAP

RVF OCCURRENCE DATA

RVF OCCURRENCE DATA

Tourre YM (2009) Global Health Action. Vol.2

Entomological Surveillance

Habitat and Ecological EvaluationHabitat and Ecological Evaluation

Larval Scooping Larval Scooping Entomological characterizationEntomological characterization

Species identification Species identification

GPS MappingGPS Mapping

Data: Environmental/Climatic databases and Secondary sources

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

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

Compartmental Model: Ordinary Differential Equation

Chitnis et al 2006;

Herd Immunity

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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

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

r

h

Culex eggs

Aedes eggs

t0Jan Dec

t20

h

Aedes eggs

r

Culex eggs

t0

Jan Dec

Adu

lt D

ensi

ty

Adu

lt D

ensi

ty

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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.

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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.

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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.

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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.

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