This document discusses how epidemiological models can add value to plant disease surveillance efforts. It provides examples of how models have been used to predict the arrival and spread of pathogens like wheat rusts and ash dieback. Specific ways models have helped include generating risk maps showing where invasion is most likely and hazard maps showing where impact would be most severe. The models integrate data on host distribution, environmental conditions, and dispersal patterns. They can inform early warning systems and optimize sampling strategies. Partnerships between researchers and surveillance networks are important for providing the data needed to build accurate models.
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Early Warning and Mitigation Planning: Epidemiological Models Add Value to Surveillance
1. Early warning and mitigation
planning: Epidemiological
models add value to surveillance
D.P. Hodson1 & C.A. Gilligan2
1CIMMYT-Ethiopia
2Department of Plant Sciences,
University of Cambridge, UK
2. Overview: Partnerships adding value
1. Surveillance Component
„ Where are we now?
„ Starting to add value to surveillance
„ Foundation for epidemiological models
2. Epidemiological Modelling Component
„ How can epidemiological models help?
® Predicting pathogen arrival and spread
® ‘What if’ scenarios for management
® Sampling strategies
„ Data/information needs
3. Global Wheat “Footprint”Rust Survey “Footprint” 2006Rust Survey “Footprint” 2012
• 13,000+ survey records
• 30+ countries
• large % of developing world wheat
5. Ug99 races, Hotspots & Wheat
• Ug99 races detected in
many hotspots (but not
all)
• Current stem rust
hotspots occupy a tiny
fraction of wheat area
• What is the risk or
hazard in those other
wheat areas???
6. Information from Surveys: Yellow Rust Hotspots
• Different distribution
• More widespread than stem rust
11. Ethiopia: Less food for rusts?
2010
Yellow rust severity - surveys Susceptible vs resistant cultivars - surveys
12. Ethiopia: Less food for rusts?
2012
Yellow rust severity - surveys Susceptible vs resistant cultivars - surveys
13. Ethiopia: Estimated Wheat Area
Susceptibility to Ug99 races
2005/06 2013/14
BGRI Cornell Screening Dbase
CIMMYT Wheat Atlas
S
MR/MS
?
MR/MS
MR MS
S
?
14. Early warning – Ethiopia 2013
Action Steps:
• Informal rust planning meeting: 12th June 2013 (CIMMYT, EIAR, FAO)
• Comprehensive Belg season surveys (EIAR/CIMMYT)
• Formal rust planning meeting , 6th August 2013 (CIMMYT, EIAR, MoA Extension
Directorate, ATA, FAO, Animal & Plant Health Directorate)
• MoA, Extension Directorate + EIAR: Early, main season surveys
Global Rust Monitoring System Assessment
CWANA – Yellow
Rust Outbreaks
(surveys)
Climatic
Conditions –
favourable for
yellow rust?Regional Winds
Rust Caution – May 17th
15. Moving Forward: Value Addition from
Epidemiological Models
● Good inputs = Good outputs
„ Surveillance platform providing critical foundation
layers: Host distribution, pathogen sources +
environments, susceptibility distribution
● Despite an extensive surveillance network, many
gaps remain e.g., where are the risks and
hazards? Models have a key role here.
● Early warning. Some progress (e.g., Ethiopia
2013), but with model inputs can make
substantial gains
16. Epidemiological toolbox
● Landscape-scale models for disease spread
● Stochastic models: allow for uncertainty and
variability
● Coupling meteorological with epidemiological
models to predict:
„ Risk – where might the pathogen arrive?
„ Hazard – likely rates of spread if pathogen arrives?
„ Control – ‘what if’ scenarios
19. Landscape scale models
● Chalara fraxinea
„ Ash dieback
● Meteorological model
„ risk of spore arrival
● Consider all
potential
sources
2008-2011
20. Landscape scale models
● Chalara fraxinea
„ Ash dieback
● Meteorological model
„ risk of spore arrival
● Consider all
potential
sources
2008-2011
● Data supplied by UK Met Office
● Computational analysis based on NAME: also tested HYSPLIT
21. Landscape scale models
● Chalara fraxinea
„ Ash dieback
● Meteorological model
„ risk of spore arrival
● Identify
principal
sources that
pose risk
27. 2008 - 2011
Landscape scale models
● Model
predictions
independent
of disease
observations
● Very strong
agreement
● Good
predictor of
arrival
28. UK Spread Model: Infected Area
28
2013
● Epidemiological model
„ Transmission
„ Spread
® Wind dispersal
® Trade dispersal
● Host distribution
„ Density, connectedness
● Environmental conditions
„ Infection and sporulation
S I D R
Susceptible Infected Detected Removed
37. UK Spread Model: Infected Area
37
2022 ● Risk maps
Where is invasion
most likely?
● Hazard maps
Where is impact of
spread most severe?
● Inform control
and sampling
38. Wheat stem rust:
1) Long distance spore dispersal
Meteorological
dispersal model
Integrate multiple
sources of inoculum
Very low probability
of long distance
dispersal
Generating risk and hazard maps
39. Wheat stem rust:
2) Density and connectivity of host
Generating risk and hazard maps
40. Wheat stem rust:
3) Environmental suitability
Coincidence: Temp X Leaf wetness X Light
Infection
Sporulation
Generating risk and hazard maps
UK Met Office data @3-6h intervals
41. Wheat stem rust:
Generating risk and hazard maps
● Hazard maps
Where is impact of
spread most severe?
● Risk maps
Where is invasion
most likely?
42. Wheat stem rust:
Input from BGRI community
● Environmental suitability
„ Infection
„ Sporulation
● Host
„ where when and how much?
„ Alternative hosts
● Pathogen dispersal
„ Data on dispersal
„ Snapshots of disease maps
Generating risk and hazard maps
44. ● Sampling method varies depending on question
„ First detection in new area
„ How much disease is present at time of first detection
„ Optimizing new detections after pathogen is introduced
Optimising Sampling
● Use of epidemiological models for sampling
„ Citrus greening in Florida
„ Chalara fraxinea in UK
„ Phytophthora ramorum in UK
45. Optimising Sampling
Chalara fraxinea again
disease hazard map
(potential outbreak size)
xdistance to known outbreaks
(probability of an outbreak)
= risk weighting
locations to sample
