Quantifying Weather and Climate Impacts on Health in Developing Countries (QWeCI) Science Talk QWeCIis funded by the European Commission s Seventh Framework Research Programme under the grant agreement 243964 13 partners from 9 countries www.liv.ac.uk/qweci Statistical modelling of Rift Valley Fever vectors abundance in a Sahelian area (Barkedji, Senegal,West Africa) Cheikh Talla 1,2, Yamar Ba 1, Jacques A. Ndione 3 Diawo Diallo 1, Ibrahima Dia 1, Mawlouth Diallo 1 cheikhtalla@hotmail.com 1 Institut Pasteur de Dakar, 2 Université Gaston Berger 3 CSE
Introduction Rift Valley Fever is a viral disease that represents a threat to human and animal health in Africa (Senegal). Prevent Rift Valley Fever disease Modelling approaches have been proposed in East Africa
Introduction It is well known that RVF virus emergence, could be predicted up to 5 months in advance using sea surface temperature, climatic and environmental parameters Can not be applicable in West Africa situation The dynamic of emergence seems to be different Models proposed for West Africa, restricted the analysis to the impact of rainfall and do not integrate a spatial dimension
Introduction Including several climatic and environmental parameters Quantify the abundance of vectors Identify climatic and environmental variables affecting abundance of RFV vectors Identify periods and areas at risk by generating forecast maps
Study area 1684000 168800 00 1692000 Bared Soil Pond Shrubby Savannah Steppe Village Wooded Savannah 2 km 510000 514000 518000 522000 Radius of 13 km centeredon Barkedji Fortnightly collection (July- Dec during rainy season 2005) 79 sites Belonging to 6 landscape classes: pond, wooded savannah, shrubby savannah, steppe, bared soil, village CDC light trap
Vectors abundance 14,604 mosquitoes Vectors 65 % Two main vectors (Aedes vexans and Culex poicilipes) 69% Abun ndance 3000 4000 5000 0 1000 2000 Ae. vexans Cx. poicilipes Other vectors
Spatial distribution Cx. poicilipes Ae. vexans ABUNDANCE 5 50 100 300 841 2 km BIOTOPE Pond Wooded savannah Shrubby savannah Bared soil Steppe Village
Temporal distribution 1400 1200 Cx.poicilipes Ae.vexans 1000 800 mosquitoes Seasonal activity Two abundance peaks 600 Number of 400 200 July 2 August 1 August 2 September 1 September 2 October 1 October 2 November 1 November 2 December 1 December 2 0
Climatic and environmental variables 0 50 100 0 150 Cumulative rainfall (mm) Temperature max ( C) Temperature min ( C) Relative humidity (%) mean 0.25 0.30 0.35 0.40 0.45 NDVI JL2 A1 A2 S1 S2 O1 O2 N1 N2 D1 D2 JL2 A1 A2 S1 S2 O1 O2 N1 N2 D1 D2
Model framework
Model diagnostic (Cx. poicilipes) Temperature Max Temperature Min Relative humidity Density 0 20 40 60 80 100-0.10-0.06-0.02 0.02 Density 0 10 20 30 40 50 0.00 0.02 0.04 0.06 Density 0 50 100 150 200 250 0.01 0.02 0.03 0.04 Devianceinformation criterion (DIC) Lowest DIC best model beta( -0.08292 ) beta( 0.01788 ) beta( 0.02648 ) Density 0 200 400 600 800 Rainfall Density 0.0 0.2 0.4 0.6 0.8 1.0 1.2 NDVI log(observed +1) 0 2 4 6 8 Withoutdistance and landscape effects Negativelyassociatedby TemperatureMax and Rainfall -0.015-0.010-0.005 beta( -0.01438 ) 0 2 4 6 beta( 6.379 ) 0 2 4 6 8 log(fitted + 1)
Model diagnostic (Ae. vexans) Rainfall Density 0 100 200 0.002 0.006 0.010 0.014 beta( 0.0098 ) log(observed +1) 0 2 4 6 8 0 2 4 6 8 log(fitted + 1)
Temporal Prediction Cx. poicilipes Ae. vexans 8 10 Observed Model fit 95% CI 8 10 Observed Model fit 95% CI 0 2 4 6 0 2 4 6 JL2 A1 A2 S1 S2 O1 O2 N1 N2 D1 D2 JL2 A1 A2 S1 S2 O1 O2 N1 N2 D1 D2
Predictions in biotopes (Cx. poicilipes) Pond Wooded savannah Shrubby savannah 0 5 10 15 20 0 2 4 6 8 10 0 2 4 6 8 JL2 A1 A2 S1 S2 O1 O2 N1 N2 D1 D2 JL2 A1 A2 S1 S2 O1 O2 N1 N2 D1 D2 JL2 A1 A2 S1 S2 O1 O2 N1 N2 D1 D2 Bared soil Steppe Village 0 5 10 15 20 25 30 35 0 2 4 6 8 0 2 4 6 Observed Fitted 95% CI JL2 A1 A2 S1 S2 O1 O2 N1 N2 D1 D2 JL2 A1 A2 S1 S2 O1 O2 N1 N2 D1 D2 JL2 A1 A2 S1 S2 O1 O2 N1 N2 D1 D2
Predictions in biotopes (Ae. vexans) Pond Wooded savannah Shrubby savannah 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 JL2 A1 A2 S1 S2 O1 O2 N1 N2 D1 D2 JL2 A1 A2 S1 S2 O1 O2 N1 N2 D1 D2 JL2 A1 A2 S1 S2 O1 O2 N1 N2 D1 D2 Bared soil Steppe Village 0 10 20 30 40 0 5 10 15 0 2 4 6 8 10 Observed Fitted 95% CI JL2 A1 A2 S1 S2 O1 O2 N1 N2 D1 D2 JL2 A1 A2 S1 S2 O1 O2 N1 N2 D1 D2 JL2 A1 A2 S1 S2 O1 O2 N1 N2 D1 D2
forecast (Cx. poicilipes) September 1 80 1400 1200 Cx.poicilipes Ae.vexans Number of mosquitoes Niakhapond 60 1000 800 600 400 July 2 August 1 August 2 September 1 September 2 October 1 October 2 November 1 November 2 December 1 December 2 Kangaledji pond 40 200 0 20 0 2km 0
forecast (Cx. poicilipes) October 2 30 1400 1200 Cx.poicilipes Ae.vexans Number of mosquitoes Niakha pond 25 1000 800 600 400 July 2 August 1 August 2 September 1 September 2 October 1 October 2 November 1 November 2 December 1 December 2 Kangaledji pond 20 15 200 0 10 5 0 2km 0
forecast (Ae. vexans) July 2 9 8 1400 1200 Cx.poicilipes Ae.vexans Number of mosquitoes Niakhapond 7 6 1000 800 600 400 200 July 2 August 1 August 2 September 1 September 2 October 1 October 2 November 1 November 2 December 1 December 2 5 0 4 3 2 0 2km 1
forecast (Ae. vexans) October 1 30 1400 1200 Cx.poicilipes Ae.vexans Number of mosquitoes Niakhapond 25 1000 800 600 400 20 200 July 2 August 1 August 2 September 1 September 2 October 1 October 2 November 1 November 2 December 1 December 2 0 15 10 5 0 2km 0
summary Climateaffects the abondance of two main vectors Result can be used to improve the surveillance and control of Rift Valley virus vectors Withweatherforecastto definearea atpriority for reducing vectors abundance Provide informations to farmers about area at risk More informations RVF to improve the model
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