Upscaling disease risk estimates

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Upscaling disease risk estimates

• Karen Garrett
• Kansas State University

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Recruitment

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

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Outline

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

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Garrett et al. 2006
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Garrett et al. 2006
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Adam Sparks
From Sparks, Hijmans, Forbes, and Garrett, in
preparation
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Outline

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

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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.
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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.
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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.
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Blight units predicted by SimCast Daily Means
susceptible cultivars. Observed blight units
are SimCast estimates based on hourly
observations.
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Blight units predicted by SimCast Monthly Means
susceptible cultivars. Observed blight units
are SimCast estimates based on hourly
observations.
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Observed pgt0.01, R20.56 Predicted pgt0.01,
R20.62
Comparison of estimates for blight units at two
levels weather data resolution vs. late blight
severity (AUDPC) from 19 cultivar-site-year
combinations
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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
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Late blight severity for February for current
conditions and 2080 a2a climate scenario
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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.
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Outline

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

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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
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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
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Maize
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Soybean
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Wheat
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Cotton
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Outline

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

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

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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 Centers website.

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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.

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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

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

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