Upscaling disease risk estimates

1. Upscaling disease risk estimates Karen Garrett Kansas State University

2. Recruitment • We are seeking a collaborator who can authoritatively address scaling of weather/climate data relevant to pathogens

3. Outline • Upscaling disease forecasting models based on weather • Network models at national scale • Network model for soybean rust incorporating wind speed and direction

5. Garrett et al. 2006

6. Garrett et al. 2006

7. Adam Sparks From Sparks, Hijmans, Forbes, and Garrett, in preparation

8. Outline • Upscaling disease forecasting models based on weather • Network models at national scale • Network model for soybean rust incorporating wind speed and direction

9. Many predictive models of plant disease rely upon fine-scale weather data This data requirement is a major constraint when applying disease prediction models in areas of the world where hourly weather data are unreliable or unavailable. We developed a framework to adapt an existing potato late blight forecast model, SimCast for use with coarse scale weather data.

10. Objectives Develop disease prediction models based on daily and monthly weather means and compare to results based on hourly weather data. Compare risk predictions based on hourly, daily, and monthly weather data to late blight severity data sets from several countries. Predict disease for resistant and susceptible cultivars under climate change scenarios.

12. Methods – development of models Hourly weather data from the US National Climatic Data Center were used in SimCast to calculate blight units, a daily measure of disease risk. Generalized additive models (GAM) were created to estimate blight units based on daily or monthly averages of weather data.

13. Blight units predicted by SimCast Daily Means susceptible cultivars. “Observed” blight units are SimCast estimates based on hourly observations.

14. Blight units predicted by SimCast Monthly Means susceptible cultivars. “Observed” blight units are SimCast estimates based on hourly observations.

15. Observed: p>0.01, R2=0.56 Predicted: p>0.01, R2=0.62 Comparison of estimates for blight units at two levels weather data resolution vs. late blight severity (AUDPC) from 19 cultivar-site-year combinations

16. Methods – climate change scenarios Maps of disease risk were produced using WorldClim (http://www.worldclim.org/) datasets that include the IPCC A2a (high growth carbon emission) climate change scenario for 2080. We applied SimCast Monthly Means to this data to compare current and future risk estimates. We have low confidence in our estimation of relative humidity – thus seek a collaborator with expertise in this area

17. Peru Peru Bolivia Bolivia Late blight severity for February for current conditions and 2080 a2a climate scenario

18. Upshot Using this approach we have created models that can quickly estimate late blight risk over large areas using readily available weather data sets. Although the models underpredict, they are useful for evaluating relative levels of risk.

19. Outline • Upscaling disease forecasting models based on weather • Network models at national scale • Network model for soybean rust incorporating wind speed and direction

20. The connectivity of the American agricultural landscape Applying graph theory to assess the national risk of crop pest and disease spread Peg Margosian, Shawn Hutchinson, and Kim With Margosian et al. BioScience 2009

21. The potential for movement through landscapes can be modeled by evaluating nodes and the edges that connect them Node and edge characteristics may influence the potential for movement

22. Maize

23. Soybean

24. Wheat

25. Cotton

26. Outline • Upscaling disease forecasting models based on weather • Network models at national scale • Network model for soybean rust incorporating wind speed and direction

27. Dynamic network models of a national epidemic: soybean rust Sweta Sutrave, Caterina Scoglio, Scott Isard, and Karen Garrett Sweta Sutrave

28. Objectives Develop a framework for estimating edge weights using observed epidemic time series in dynamic network models Apply the model to the spread of soybean rust in the US. Evaluate the estimation framework potential for epidemic modeling.

29. Data Sets Rust status data: 2005 to 2008, from Dr.Scott Isard. Host density data:2005 to 2008, from US National Agricultural Statistics Service. Wind data: Wind speed and direction, National Climatic Data Center’s website.

30. Model SI model which classifies nodes as being susceptible or infected. We consider the centroid of each county of the United states as a node or vertex. We assume that the sentinel plot and the area around it behave in a similar manner and begin by modeling dynamics within a single season.

31. Edge weight function • uji : Edge-weight between two nodes • A function of the following - Distance between the sentinel plots. - Crop density and kudzu density. - Speed and direction of wind w.r.t the link

32. Example of Epidemic Prediction Soybean rust model realization Green = no rust predicted Red shading = likelihood of infection

33. Looking toward the future • Developing global model for general environmental-response classes of pathogens • Seeking a collaborator who can authoritatively address scaling of weather/climate data relevant to pathogens

36. Mapping disease risk based on: -Climate -Historical disease distribution -Host availability Rival models for consideration Sparks et al., in prep