Presentation is loading. Please wait.

Presentation is loading. Please wait.

A Predictive BTV Wind Spread Model A innovative approach which may contribute to improved vaccination schemes Guy Hendrickx (1), Yves Van Der Stede (2),

Similar presentations


Presentation on theme: "A Predictive BTV Wind Spread Model A innovative approach which may contribute to improved vaccination schemes Guy Hendrickx (1), Yves Van Der Stede (2),"— Presentation transcript:

1 A Predictive BTV Wind Spread Model A innovative approach which may contribute to improved vaccination schemes Guy Hendrickx (1), Yves Van Der Stede (2), Estelle Meroc (2), Benoit Durand (3) & E. Ducheyne (1) (1) Avia-GIS, (2) CODA, (3) AFSSA

2 BTV spread in Europe Table of Contents Introduction BTV Previous wind models (gen 1-2) Current wind model (gen 3) → Model Input Data → Model Parameters → Model Flow Chart → Model Outputs Discussion Introduction Gen 1-2 Input Parameters Flowchart Outputs Discussion

3 BTV spread in Europe Culicoides chiopterus Picture ITM Live cycle Introduction Gen 1-2 Input Parameters Flowchart Outputs Discussion

4 BTV spread in Europe European biomes Temperate: BTV 1-8 (6-11) Temperate: BTV 1-8 (6-11) Mediterranean: BTV Mediterranean: BTV Introduction Gen 1-2 Input Parameters Flowchart Outputs Discussion

5 Current EU restriction zones BTV spread in Europe Introduction Gen 1-2 Input Parameters Flowchart Outputs Discussion

6 BTV spread in Europe Table of Contents Introduction BTV Previous wind models (gen 1-2) Current wind model (gen 3) → Model Input Data → Model Parameters → Model Flow Chart → Model Outputs Discussion Introduction Gen 1-2 Input Parameters Flowchart Outputs Discussion

7 BTV spread in Europe 1 rst generation model: BTV     June  November Introduction Gen 1-2 Input Parameters Flowchart Outputs Discussion

8 BTV spread in Europe 2 nd generation model: BTV Introduction Gen 1-2 Input Parameters Flowchart Outputs Discussion

9 BTV spread in Europe 2 nd generation model: BTV Introduction Gen 1-2 Input Parameters Flowchart Outputs Discussion

10 BTV spread in Europe 2 nd generation model: BTV Introduction Gen 1-2 Input Parameters Flowchart Outputs Discussion

11 Table of Contents Introduction BTV Previous wind models (gen 1-2) Current wind model (gen 3) → Model Input Data → Model Parameters → Model Flow Chart → Model Outputs Discussion BTV spread in Europe Introduction Gen 1-2 Input Parameters Flowchart Outputs Discussion

12 Model input data (1/3) Epidemiological BTV1 FR2008 data (AFSSA) at the municipality level  epidemiological curves Denominator data (AFSSA) at municipality level  coordinates modeled within suitable LC Vaccination BTV1 FR2009 data (AFSSA) BTV spread in Europe Introduction Gen 1-2 Input Parameters Flowchart Outputs Discussion

13 Model input data (2/3) Entomological data: → In flight data ABTD m (Goossens et al. subm.) → Rothamsted suction trap 11m (Fassotte et al. 2008) → OVI ground level traps (Fassotte et al. 2008) Wind data (ECMWF) BTV spread in Europe Introduction Gen 1-2 Input Parameters Flowchart Outputs Discussion

14 Model input data (3/3) BTV spread in Europe Introduction Gen 1-2 Input Parameters Flowchart Outputs Discussion

15 BTV spread in Europe Table of Contents Introduction BTV Previous wind models (gen 1-2) Current wind model (gen 3) → Model Input Data → Model Parameters → Model Flow Chart → Model Outputs Discussion Introduction Gen 1-2 Input Parameters Flowchart Outputs Discussion

16 Model parameters (1/4) Cluster analysis using a retrospective space- time permutation model (SatScan)  identify location (S 1, S 2,… S n ) and timing (t S1, t S2,… t Sn ) of the model seed cases. Weekly incidence was calculated from the epidemiological curves and fitted using a least square estimator to the Verhulst-Pearl growth function (Verhulst, 1845). BTV spread in Europe Introduction Gen 1-2 Input Parameters Flowchart Outputs Discussion

17 Model parameters (2/4) BTV spread in Europe Introduction Gen 1-2 Input Parameters Flowchart Outputs Discussion

18 Model parameters (3/4) Based on results 2 nd gen. model (Hendrickx et al. 2008) a distinction is made between: → Local mainly midge-flight driven spread (LDS): 50% of the weekly incidence is observed within a range of 4km. → Medium range mainly wind driven spread (WDS): an additional 45% of the weekly incidence is observed within a range of 22km. BTV spread in Europe Introduction Gen 1-2 Input Parameters Flowchart Outputs Discussion

19 Model parameters (4/4) Wind parameters included to restrict WDS: → highest Culicoides activity during the period prior to sunset (Fassotte et al., 2008) → highest density of airborne Culicoides at an altitude of 150m (Goossens et al., submitted). The slowing impact of slope on medium distance wind spread (Bishop et al., 2000, 2005; Ducheyne et al., 2007; Hendrickx et al., 2008) was taken into consideration by reducing the wind magnitude relative to the slope. BTV spread in Europe Introduction Gen 1-2 Input Parameters Flowchart Outputs Discussion

20 BTV spread in Europe Table of Contents Introduction BTV Previous wind models (gen 1-2) Current wind model (gen 3) → Model Input Data → Model Parameters → Model Flow Chart → Model Outputs Discussion Introduction Gen 1-2 Input Parameters Flowchart Outputs Discussion

21 3 rd generation model flowchart BTV spread in Europe Introduction Gen 1-2 Input Parameters Flowchart Outputs Discussion

22 BTV spread in Europe Table of Contents Introduction BTV Previous wind models (gen 1-2) Current wind model (gen 3) → Model Input Data → Model Parameters → Model Flow Chart → Model Outputs Discussion Introduction Gen 1-2 Input Parameters Flowchart Outputs Discussion

23 BTV spread in Europe Descriptive wind model BTV Introduction Gen 1-2 Input Parameters Flowchart Outputs Discussion

24 BTV spread in Europe BTV1 FR 2008: Observed-weekly Introduction Gen 1-2 Input Parameters Flowchart Outputs Discussion

25 BTV spread in Europe BTV1 FR 2008: Observed-cumulative Introduction Gen 1-2 Input Parameters Flowchart Outputs Discussion

26 BTV spread in Europe BTV1 FR simulated BTV observed (Includes vaccination) BTV simulation output Introduction Gen 1-2 Input Parameters Flowchart Outputs Discussion

27 BTV spread in Europe Predictive wind model BTV Introduction Gen 1-2 Input Parameters Flowchart Outputs Discussion

28 BTV spread in Europe BTV 1 – vaccination coverage bovine Adapted from: Data May 2009 included in model Data August 2009 Introduction Gen 1-2 Input Parameters Flowchart Outputs Discussion

29 BTV spread in Europe BTV 1 – vaccination coverage ovine Adapted from: Data May 2009 included in model Data August 2009 Introduction Gen 1-2 Input Parameters Flowchart Outputs Discussion

30 BTV spread in Europe BTV1 prediction 2009 – SW France Introduction Gen 1-2 Input Parameters Flowchart Outputs Discussion

31 BTV spread in Europe Table of Contents Introduction BTV Previous wind models (gen 1-2) Current wind model (gen 3) → Model Input Data → Model Parameters → Model Flow Chart → Model Outputs Discussion Introduction Gen 1-2 Input Parameters Flowchart Outputs Discussion

32 Significant step to predict BTV spread after first introduction: → Predict spatial extent → Improved vaccination policy Further model improvements: → Include impact weather → Disentangle wind and movement BTV spread in Europe Introduction Gen 1-2 Input Parameters Flowchart Outputs Discussion

33 Thank you.


Download ppt "A Predictive BTV Wind Spread Model A innovative approach which may contribute to improved vaccination schemes Guy Hendrickx (1), Yves Van Der Stede (2),"

Similar presentations


Ads by Google