Quantifying Weather and Climate Impacts on Health in Developing Countries (QWeCI)

Transcription

1 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 partners from 9 countries 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

2 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

3 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

4 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

5 Study area Bared Soil Pond Shrubby Savannah Steppe Village Wooded Savannah 2 km 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

6 Vectors abundance 14,604 mosquitoes Vectors 65 % Two main vectors (Aedes vexans and Culex poicilipes) 69% Abun ndance Ae. vexans Cx. poicilipes Other vectors

7 Spatial distribution Cx. poicilipes Ae. vexans ABUNDANCE km BIOTOPE Pond Wooded savannah Shrubby savannah Bared soil Steppe Village

8 Temporal distribution Cx.poicilipes Ae.vexans mosquitoes Seasonal activity Two abundance peaks 600 Number of July 2 August 1 August 2 September 1 September 2 October 1 October 2 November 1 November 2 December 1 December 2 0

9 Climatic and environmental variables Cumulative rainfall (mm) Temperature max ( C) Temperature min ( C) Relative humidity (%) mean NDVI JL2 A1 A2 S1 S2 O1 O2 N1 N2 D1 D2 JL2 A1 A2 S1 S2 O1 O2 N1 N2 D1 D2

10 Model framework

11 Model diagnostic (Cx. poicilipes) Temperature Max Temperature Min Relative humidity Density Density Density Devianceinformation criterion (DIC) Lowest DIC best model beta( ) beta( ) beta( ) Density Rainfall Density NDVI log(observed +1) Withoutdistance and landscape effects Negativelyassociatedby TemperatureMax and Rainfall beta( ) beta( ) log(fitted + 1)

12 Model diagnostic (Ae. vexans) Rainfall Density beta( ) log(observed +1) log(fitted + 1)

13 Temporal Prediction Cx. poicilipes Ae. vexans 8 10 Observed Model fit 95% CI 8 10 Observed Model fit 95% CI JL2 A1 A2 S1 S2 O1 O2 N1 N2 D1 D2 JL2 A1 A2 S1 S2 O1 O2 N1 N2 D1 D2

14 Predictions in biotopes (Cx. poicilipes) Pond Wooded savannah Shrubby savannah 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 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

15 Predictions in biotopes (Ae. vexans) Pond Wooded savannah Shrubby savannah 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 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

16 forecast (Cx. poicilipes) September Cx.poicilipes Ae.vexans Number of mosquitoes Niakhapond July 2 August 1 August 2 September 1 September 2 October 1 October 2 November 1 November 2 December 1 December 2 Kangaledji pond km 0

17 forecast (Cx. poicilipes) October Cx.poicilipes Ae.vexans Number of mosquitoes Niakha pond July 2 August 1 August 2 September 1 September 2 October 1 October 2 November 1 November 2 December 1 December 2 Kangaledji pond km 0

18 forecast (Ae. vexans) July Cx.poicilipes Ae.vexans Number of mosquitoes Niakhapond July 2 August 1 August 2 September 1 September 2 October 1 October 2 November 1 November 2 December 1 December km 1

19 forecast (Ae. vexans) October Cx.poicilipes Ae.vexans Number of mosquitoes Niakhapond July 2 August 1 August 2 September 1 September 2 October 1 October 2 November 1 November 2 December 1 December km 0

20 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

21 THANK YOU!